Social Network Analysis: Practical Uses and Implementation is a presentation that discusses social network analysis and its practical applications. It introduces key concepts such as social networks, social network analysis, roles in social networks, and graph theory. It also covers metrics and implementations of social network analysis, including calculating metrics from social networks and recommended approaches involving data preparation, metric calculation, model creation and scoring, and measurement. The presentation provides an overview of how social network analysis can be a useful tool for understanding relationships and influence.
The document provides information about social network visualization and analysis. It includes contact information for librarians at UT Austin who can assist with data visualization. It discusses how to structure network data, including examples of node and edge files. Different types of networks like undirected, directed, and weighted networks are described. Centrality measures and applications of network analysis like Gephi software are also mentioned.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
Quick introduction to community detection.
Structural properties of real world networks, definition of "communities", fundamental techniques and evaluation measures.
Community detection algorithms are used to identify densely connected groups of nodes in networks. Modularity optimization is commonly used, which detects communities as groups of nodes with more connections within groups than expected by chance. Parameters like resolution affect results. Multilayer networks model systems with multiple network layers over nodes. Multilayer modularity generalizes modularity to multilayer networks. Community detection in multilayer networks provides insights into structures across data types and applications.
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
This document provides an overview of social network analysis, including what social networks are, what can be learned from analyzing social networks, and how social network analysis can be performed. Some key findings that can be uncovered include the six degrees of separation principle, the 80-20 rule of social popularity where a minority of nodes have most connections, how to identify influential nodes, and how to group similar nodes into communities. Various metrics and models are described for analyzing features like path lengths, degree distributions, ranking nodes, measuring community structure, and more. Examples of social network analysis are also provided.
Graph theory concepts like centrality, clustering, and node-edge diagrams are used to analyze social networks. Visualization techniques include matrix representations and node-link diagrams, each with advantages. Hybrid representations combine these to leverage their strengths. MatrixExplorer allows interactive exploration of social networks using both matrix and node-link views.
This document discusses data mining in social networks. It covers topics like social network analysis, graph mining, and text mining on social media platforms. Graph mining is used to understand relationships and extract communities from social networks. Text mining techniques like clustering and anomaly detection are applied to textual data from blogs, messages, etc. on social platforms. The document also discusses accessing Facebook data through its API and SDK, and applications and limitations of social network analysis.
Social media mining extracts information from social media sources like Facebook, Twitter, and YouTube to understand phenomena and improve services. It addresses challenges from vast, noisy, distributed, unstructured, and dynamic social media data. Common data mining tools and techniques are used to analyze social media data for applications like personalization, targeted marketing, community analysis, and sentiment analysis. Research issues include privacy and developing methods to effectively handle large-scale social media data.
Introduction to Social Network AnalysisPatti Anklam
This document provides an overview of network analysis and its applications. It discusses the origins and history of network study in fields like graph theory and sociology. Various network patterns and metrics are described, including density, distance, centrality, and structural measures. Case studies are presented on using network analysis to understand expertise management, trust, and performance issues in organizations. The document emphasizes that network analysis can provide insights through metrics and visualization to inform important business and organizational questions.
Big Data: Its Characteristics And Architecture CapabilitiesAshraf Uddin
This document discusses big data, including its definition, characteristics, and architecture capabilities. It defines big data as large datasets that are challenging to store, search, share, visualize, and analyze due to their scale, diversity and complexity. The key characteristics of big data are described as volume, velocity and variety. The document then outlines the architecture capabilities needed for big data, including storage and management, database, processing, data integration and statistical analysis capabilities. Hadoop and MapReduce are presented as core technologies for storage, processing and analyzing large datasets in parallel across clusters of computers.
This document discusses content-based recommendation systems. It describes how items and user profiles are represented, and different methods for making recommendations including manual methods, decision trees/rule induction, and nearest neighbor algorithms. Content-based systems recommend items to users based on descriptions of the items and profiles of users' interests, but have limitations in recognizing subtleties and anticipating future interests.
This document provides an overview of social network analysis, including key concepts, analytic techniques, and examples of classic studies. It discusses the basic components of social networks like actors, ties, and relationships. It also describes different types of networks and measures used in social network analysis, such as degree centrality and betweenness centrality. Finally, it highlights some influential early social network analysis studies and resources for further information.
Disclaimer :
The images, company, product and service names that are used in this presentation, are for illustration purposes only. All trademarks and registered trademarks are the property of their respective owners.
Data/Image collected from various sources from Internet.
Intention was to present the big picture of Big Data & Hadoop
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Media Mining - Chapter 7 (Information Diffusion)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Big Data - The 5 Vs Everyone Must KnowBernard Marr
This slide deck, by Big Data guru Bernard Marr, outlines the 5 Vs of big data. It describes in simple language what big data is, in terms of Volume, Velocity, Variety, Veracity and Value.
Big Data Analytics : A Social Network ApproachAndry Alamsyah
This document discusses using social network analysis approaches for big data analytics. It begins by introducing social network metrics like centrality and modularity that can be applied to large social network datasets. It then provides examples of how social network analysis has been used to detect terrorist cells and identify research communities. Finally, it outlines the author's research interests and publications in areas like sentiment analysis on social media and using social networks to analyze industries.
Quadratic assignment procedure (QAP) is a permutation test that controls for non-independence in network data where dyads are not independent by permuting the response variable to create a sampling distribution of the null hypothesis. QAP can perform correlations and regressions on network data and is easy to interpret. It works by performing a regression on the original data and then permuting the response variable many times to create random datasets for comparison. The p-value is the proportion of times the null coefficient is greater than or equal to the observed estimate.
This document discusses social network analysis and its practical uses and implementation. It begins with definitions of key terms like social network and social network analysis. It then covers graph theory concepts used in social network analysis like nodes, edges, directed/undirected edges, scale-free networks, and network shapes. The document recommends approaches to social network analysis including identifying the social network, influencers, communities, and social leaders. It also discusses calculating common metrics like degree, centrality, and betweenness centrality. Finally, it provides examples of data preparation and filtering for social network analysis.
01 Introduction to Networks Methods and Measuresdnac
This document provides an introduction to social network analysis. It discusses how networks matter through two fundamental mechanisms: connections and positions. Connections refer to the flow of things through networks, viewing networks as pipes. Positions refer to relational patterns and networks capturing role behavior, viewing networks as roles. The document also covers basic network data structures including nodes, edges, directed/undirected ties, binary/valued ties, and different levels of analysis such as ego networks and complete networks. It provides examples of one-mode and two-mode network data.
This document provides an introduction to social network analysis. It discusses how network analysis allows us to understand social connections and positions. There are two key mechanisms through which networks can impact outcomes: connections, where networks matter because of what flows through them, and positions, where networks capture roles and social exchange. Network analysis provides tools to empirically study patterns of social structure by mapping relationships between actors.
This document provides an introduction to social network analysis. It discusses how social network analysis views social relationships as connections between individuals, and uses tools to systematically study these connections. The key topics covered include:
- Why social networks are important to study as they influence information and resource sharing
- The basic data elements in social network analysis, including nodes to represent individuals and edges to represent relationships between nodes
- Different levels of network data, from ego networks to complete networks
- Common ways to represent network data structurally, including graphs, matrices, and lists
- An overview of how social network analysis can help answer questions about how social relationships influence individual behaviors and the structure of social hierarchies.
Network Data Collection
The document discusses collecting social network data. It covers three main topics:
1) Introduces social network analysis and why networks are important in social science. Networks matter because of connections that allow diffusion and because positions in networks influence roles and behavior.
2) Discusses research design considerations for collecting network data, including specifying relations of interest based on theoretical mechanisms, boundary selection, and sampling approaches.
3) Addresses accuracy of network survey data and how to handle inaccurate or missing data. The goal is to systematically understand connections between actors using empirical network data and analysis methods.
- The speaker discusses thinking in terms of networks and how it can inform policymaking. Networks represent relationships across entities and can be used to model many real-world systems from social networks to transportation networks.
- Behavior spreads through social networks in a process similar to contagion. Knowing the network structure allows policies to be targeted to high influence nodes for greater impact.
- Online interaction leaves network data that can be analyzed to understand how ideas and behaviors spread. The speaker uses their experience with an online participatory policy project to explore the network of conversations and interactions that emerged.
A high-level overview of social network analysis, providing background on how it came into the knowledge management field. Includes an example and core concepts pertinent to the audience, online community managers.
The document discusses social networks and their application in business. It covers several key topics:
- Social networks can be mapped to show relationships between individuals and how they are connected. They provide flexible means of social organization.
- Social business strategies can help organizations understand motivations and objectives of internal and external clients to enhance productivity.
- Case studies show how social network analysis can provide insights into knowledge sharing, innovation, and organizational transformation.
Social Network Analysis - an Introduction (minus the Maths)Katy Jordan
This document provides an overview of social network analysis concepts without advanced mathematics. It defines social network analysis as conceptualizing social relationships as links between nodes, which can be visualized and analyzed using graph theory. It discusses frequently used network metrics like degree, density, and betweenness centrality. It summarizes classic social network studies by Milgram on "six degrees of separation" and Granovetter on "the strength of weak ties." It also discusses considerations for social network analysis and tools like Gephi for visualizing networks.
In this talk we shall introduce the main ideas of TruSIS (Trust in Social Internetworking System), a Marie Curie Fellowhsip financed by European Union and hosted at VU University, Department of Computer Science, Business and Web group. The goal of TruSIS is to study the baheviour of users who affiliate to multiple social networking sites and
are active in them (e.g., users may publish personal profiles on sites like MySpace and post videos on sites like YouTube). We briefly called this scenario as SIS (Social Internetworking system).
As a first research contribution, we implemented a crawler to gather data about users and link their profiles on multiple social networking websites. To this purpose we used Google Social Graph API, a powerful API released by Google in 2008. We obtained a sample of about 1.3 millions of user accounts and 36 millions of connections between them.
Parameters from social network theory (like average clustering coefficient, network modularity and so on) were used to study the structural properties of the gathered sample and how these properties depend on user behavious.
A second contribution is about the computation of distance between two users in a SIS on the basis of their social ties. We used a popular parameter from Social Network Theory known as Katz coeffcient and
provide a computationally afficient approach to computing Katz coefficient which relies on the usage of a popular tool from linear algebra known as Sherman- Morrison formula.
Finally, we shall describe our work on extending the notion of trust from single social networks to a SIS. We describe the main research challenges tied to the definition of trust and how they relate to Semantic Web technologies.
ICPSR - Complex Systems Models in the Social Sciences - Lecture 4 - Professor...Daniel Katz
This document provides an overview of complex systems models in social sciences, focusing on network analysis and community detection methods. It discusses key concepts like directed vs undirected networks, weighted vs unweighted edges, and overlapping vs non-overlapping communities. It also notes important considerations like network resolution, computational complexity, and how community detection results depend on the specific context and questions being examined. A variety of examples are provided, including social networks defined by friendships or voting coalitions.
A presentation describing application of Node XL into analyzing social networks.
Made as part of project work for ITB course at VGSOM IIT Kharagpur.
By : Mayank Mohan
Anuradha Chakraborty
( Batch of 2012)
This document provides an overview of social network analysis. It defines key concepts like nodes, edges, degrees, and centrality measures. It describes different types of networks including full networks, egocentric networks, affiliation networks, and multiplex networks. It also outlines common network analysis metrics that can be used to analyze networks at both the aggregate and individual level. These include measures like density, degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. The document discusses tools for social network analysis and ways of visually mapping social networks.
Networks & Health
This document provides an introduction and overview of social network analysis and its relevance to health research. It discusses key concepts such as what networks are, different types of network data including one-mode and two-mode data, and different levels of analysis including ego networks, partial networks, and complete networks. The document also discusses why networks matter for health through connectionist mechanisms like diffusion and positional mechanisms like social roles. Overall, the document serves as a high-level introduction to social network concepts and their application to health research.
This document provides an introduction to social networks and social network analysis. It defines social networks as descriptions of social structures between actors like individuals and organizations. Social network analysis maps and measures relationships and information flows. Key aspects of social network analysis include degree centrality, betweenness centrality, and closeness centrality. The document discusses how social network analysis can be applied in domains like knowledge management systems, counterterrorism, marketing, and more. It also profiles the social networking site LinkedIn and how its platform facilitates network growth and connection.
Social Network Analysis is a study of relationships and ties between nodes/actors in a network. It seeks to understand the structure of relationships and how an individual's position in a network affects opportunities and constraints. SNA can be used to map and analyze networks in fields like public health, national security, and design. It provides insights into topics like information diffusion, social influence, and identifying important actors. SNA tools help visualize networks and analyze metrics like centrality, density, and connectivity.
Social Network Analysis & an Introduction to ToolsPatti Anklam
This document provides an introduction to social network analysis. It discusses how networks can be mapped and analyzed using tools to understand their structure and flow of information. Key aspects of network analysis are introduced, including nodes, ties, centrality metrics, and structural patterns. A variety of tools are presented, ranging from free social media applications to specialized software, that can be used to map and analyze networks. The value of network analysis is in identifying influential individuals, improving collaboration and knowledge sharing, and intervening to change network structures and behaviors.
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GDG Cloud Southlake #34: Neatsun Ziv: Automating AppsecJames Anderson
The lecture titled "Automating AppSec" delves into the critical challenges associated with manual application security (AppSec) processes and outlines strategic approaches for incorporating automation to enhance efficiency, accuracy, and scalability. The lecture is structured to highlight the inherent difficulties in traditional AppSec practices, emphasizing the labor-intensive triage of issues, the complexity of identifying responsible owners for security flaws, and the challenges of implementing security checks within CI/CD pipelines. Furthermore, it provides actionable insights on automating these processes to not only mitigate these pains but also to enable a more proactive and scalable security posture within development cycles.
The Pains of Manual AppSec:
This section will explore the time-consuming and error-prone nature of manually triaging security issues, including the difficulty of prioritizing vulnerabilities based on their actual risk to the organization. It will also discuss the challenges in determining ownership for remediation tasks, a process often complicated by cross-functional teams and microservices architectures. Additionally, the inefficiencies of manual checks within CI/CD gates will be examined, highlighting how they can delay deployments and introduce security risks.
Automating CI/CD Gates:
Here, the focus shifts to the automation of security within the CI/CD pipelines. The lecture will cover methods to seamlessly integrate security tools that automatically scan for vulnerabilities as part of the build process, thereby ensuring that security is a core component of the development lifecycle. Strategies for configuring automated gates that can block or flag builds based on the severity of detected issues will be discussed, ensuring that only secure code progresses through the pipeline.
Triaging Issues with Automation:
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Identifying Ownership Automatically:
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This is the combined Sessions of ACE Atlassian Coimbatore event happened on 22nd June 2024
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How Netflix Builds High Performance Applications at Global ScaleScyllaDB
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We certainly hope we have managed to spike your interest in the subjects to be presented and the incredible networking opportunities at hand, too!
Check out our proposed agenda below 👇👇
08:30 ☕ Welcome coffee (30')
09:00 Opening note/ Intro to UiPath Community (10')
Cristina Vidu, Global Manager, Marketing Community @UiPath
Dawid Kot, Digital Transformation Lead @Proservartner
09:10 Cloud migration - Proservartner & DOVISTA case study (30')
Marcin Drozdowski, Automation CoE Manager @DOVISTA
Pawel Kamiński, RPA developer @DOVISTA
Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
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Pawel Poplawski, Director, Improvement and Automation @McCormick & Company
Michał Cieślak, Senior Manager, Automation Programs @McCormick & Company
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Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner
10:35 ☕ Coffee Break (15')
10:50 Document Understanding with my RPA Companion (45')
Ewa Gruszka, Enterprise Sales Specialist, AI & ML @UiPath
11:35 Power up your Robots: GenAI and GPT in REFramework (45')
Krzysztof Karaszewski, Global RPA Product Manager
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Kamil Miśko, UiPath MVP, Senior RPA Developer @Zurich Insurance
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Thomasz Wierzbicki, Business Analyst @Office Samurai
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What’s New in Teams Calling, Meetings and Devices May 2024
Social network analysis (SNA) - Big data and social data - Telecommunications and more
1. Social Network Analysis:
Practical Uses and Implementation
Presented by Wael Elrifai (WAEL@PEAKCONSULTING.EU)
London
-
New York
- Dubai - Hong Kong - Mumbai
2013
2. Table of Contents
Introduction
o Metrics and Implementation
o Social Network Analysis with Relationships
o Social Network Analysis with Transactions
o Conclusions
o
4. Definitions: Social Network
Social Network: A social structure composed of
individuals (or organizations) interconnected by one
or more specific types of interdependencies such as
friendship, kinship, financial exchanges,
communication exchanges, etc.
5. Definitions: Social Network Analysis
Social Network Analysis: The application of graph
theory to understand, categorize and quantify
relationships in a social network.
In the representation of a social network, nodes in
a graph represent the individuals or organizations
(actors) and edges in the graph represent
interdependencies. Edges may be either directed or
non-directed.
Confidential - not for redistribution
6. Why should you care about SNA?
oCustomer
are sceptical: if you want to sell your products
to your customers, convince their friends.
oIf
you want to sell lots of stuff to your customers… do it
in a viral way (target the “right” customers).
oUse
social network analysis to understand more about
your customers and their communities.
oEnhance
existing reports, modelling
methodologies with social metrics.
Confidential - not for redistribution
tools,
and
7. Why should you care about SNA?
Traditional marketing practices are becoming obsolete.
Test and control group methodologies no longer work as
intended.
o
•Information
exchange between individuals within an online social
network is extremely high.
•Difficult to keep control group “pure”.
Need to understand behaviour across and within
communities rather than focusing just on individuals.
o
Leverage (and protect against) high velocity of
information exchange within on-line social networks.
o
Confidential - not for redistribution
8. How does a Customer with the Role of an Influencer in
the Social Network Work?
Influential user adopts a product or behaviour.
o Influential user tells (and influences) his or her
immediate contacts within the community.
o These immediate contacts tell their contacts.
o ...and the viral marketing spreads.
o
It is important…
• To identify these people.
• To influence these people.
• To monitor the behaviour of these people.
Confidential - not for redistribution
9. Roles in a social Network
Malcom Gladwell characterized key actors in a social network
in his seminal work The Tipping Point:
Connector: people who “link us up with the world … people
with a special gift for bringing the world together”. Gladwell
characterizes these individuals as having social networks of
over one hundred people.
Salesperson: people who are charismatic with powerful
negotiation skills. They tend to have an indefinable trait that
goes beyond what they say, which makes others want to agree
with them.
Maven: people who are “information specialists” or “people
we rely upon to connect us with new information”. They
accumulate knowledge, especially about the marketplace, and
know how to share it with others.
Confidential - not for redistribution
10. Social Network Analysis
Recommended Approach
The Social Network
1.
Identify the Social Network
•Who contacts whom?
•How often?
•How long?
•Both directions?
•On Net, Off Net?
2.
There is no ‘general’
Identify Influencers for each Topic
influencer!
•Who influence whom, how much,
on what purchases?
Price
•Who influences whom, how much
John’s father
John
on churn?
•Who will acquire others?
Technology
Confidential - not for redistribution
11. Definitions: Social Network Analysis
Rather than treating individuals (persons, organizations) as
discrete units of analysis, social network analysis focuses on how
the structure of ties (links) affects individuals and their
relationships.
Not a new science:
oStarted
in the social sciences.
oFormalized by J.A. Barnes 50+ years ago.
oSix degrees of separation small world phenomena.
• Stanley Milgram’s post mail experiments.
• Watts, Dodds, Muhamed email study.
A boom of popular press:
oGladwell:
The tipping Point
oWatts: Small Worlds: The Dynamics of Networks Berween Order and
Randomness.
oBarabasi: Linked: The New Science of Networks
oWatts: Six Degrees: The Science of a Connected Age
Confidential - not for redistribution
12. Definitions: Graph Theory
Directed Edges: Captures the “direction” of a
relationship. For example, A calls B would have a
different direction than B calls A.
Non-directed Edges: Relationship has no direction.
For example A is married to B is the same as B is
married to A.
Edges can be binary (e.g., exist or not) or weighted
(e.g., representing a count of the number of calls
between two individuals).
Confidential - not for redistribution
13. Definitions: Graph Theory
A scale-free network is a network whose degree
distribution follows a power law, at least asymptotically.
That is, the fraction P(K) of nodes in the network having
k connections to other nodes goes for large values of k as
P(K) ~ K-Y where Y is a constant whose value is typically
in the range 2 < Y < 3, although occasionally it may lie
outside these bounds.
Node connectivity is defined by power law.
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14. Definitions: Graph Theory
The shape of a social network can influence its behaviour
and usefulness.
“Closed” social networks are tightly knit with many
redundant ties.
• In-breeding of ideas: persons who only interact with
each other share the same ideas and opportunities.
• Characterized by a (near) fully connected graph.
o
“Open” social networks have loose ties (weak links)
across multiple communities.
• More likely to introduce new ideas and opportunities
to their members.
• Requires connector nodes to bridge across.
o
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15. Definitions: Graph Theory
A clique is a fully connected set of nodes within a graph.
o An N-Clique is a subgraph of N nodes (actors) which are
fully connected (“closed” network).
o Maximum clique detection within a graph is an NPcomplete computational problem.
o A K-plex is a less strict subset of the graph.
o A giant component is a connected subgraph that contains
a majority of an entire graph’s nodes.
o
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16. Social Network Analysis
Circle analysis:
o Neighbours of a node.
o
Count neighbours (degree).
o
Count those in circle@
• Who churned,
• Who have a product P,
• Who became customers after node A,
• …..
o
Enrich node label with these metrics.
Confidential - not for redistribution
A
17. Key Observation: Few Isolated Communities
Exist in the Real World
Most
subscribers are part
of a single mega-community.
Splitting them up requires
artificial decisions.
Experiment:
oFive random starting
osubscribers.
oCount number of new subscribers in degree 1,
degree 2, etc.
oConclusion:
• Peak numbers between degree 5 and degree 7.
• Very few new subscribers after degree 8.
• Most subscribers are interconnected, rather than in
discrete communities.
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18. Key Observation: Few Isolated Communities
Exist in the Real World
An example with real world data:
73,277
Singletons
15,666
2,658
653
pairs
triangles
From 5 to 22 nodes:
- 320 communities.
- 1905 nodes.
squares
Large Component of 1.1M nodes
(with off-network nodes = 3.6M)
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20. Calculation of Metrics from a Social Network
o
In networks, connection is power.
• Centrality is a key measure.
• “Social Degree” measures how well a node
is connected.
An “influencer” is a node that is well
connected.
o
•
Capable of propagating information to
lots of people via Word-of-Mouth
(Mouse).
There exist many measures to identify
the power to influence.
o
•
Depending on your data, some might be
easier to compute than others.
• Some might bring more useful information
than others.
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21. Overview of Social Network Analysis
Circle
Analysis:
Count the number
of contacts.
• Rank best contacts.
•
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Connection
Analysis:
Profile contacts.
• Describe each
customer by its
contacts.
• Social boundaries.
•
Community
Analysis:
Identify
communities.
• Add each customer
to its community.
•
Social Leader
Analysis:
Identify social
leaders.
• Analyze impact of
the social leaders.
•
22. Recommended Approach: Key Steps
oData
preparation.
• In-database conversion of data to Node: Edge model.
•
Data filtering.
oMetric
calculation.
•
In-database calculation of SNA metrics.
• Degree, Centrality, Betweenness, etc…
oSNA
model creation: inverse cascading model.
oSNA model scoring.
oTarget
and control group creation.
oMeasurement.
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23. Data Preparation Example:
From XDR to Node/Edge Graph
oA
can communicate with B in various ways:
Voice, SMS, MMA. Thus, we allow a separate
Edge for each type of communication.
Master Out 8
Voice Out 6
SMS Out 1
A
The Mater Edge defines that a
communication exists, and is irrespective of
the actual type.
o
MMS Out 1
B
MMS In 2
SMS In 3
Voice Out 20
Master Out 25
Performed for on-net
and off-net numbers.
Of course, B can reciprocate communicate
with A in various ways also: Voice, SMS or
MMS. Thus, we allow a separate Edge for that
too.
o
That means from A’s perspective there could
be a maximum of 8 Edges associated with A if
all communication types are used and
reciprocated with B.
o
o
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Edge IDs are unique system wide.
24. Data Preparation: Filtering
o
Not all numbers are valid:
•
Non-human numbers are identified and filtered.
• Service numbers are identified and filtered.
o
Some links are trivial:
•Remove
links that are infrequently called.
Different filters can be applied for different
metrics.
o
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25. Data Preparation: Target Data Model
Graph Theory concepts are the foundation of a good
model for use in Social Network Analysis.
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26. Metric Calculation
Social network metrics are calculated directly from
the ‘graph’.
o
Social Network metrics describe nodes and edges, and
attempt to give meaning to position.
o
o
Metrics are typically calculated in-database.
o
Metrics are created using scripts that use:
•
SQL for simple metrics.
• UDFs for complex metrics.
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27. Social Media Metrics
Identity confidence
o Group detection
o
•
Degree
• First/second
• On-net/off-net
• Peak/off-peak
• Etc.
Centrality
o Betweenness
o Closeness
o Triangles
o Authority
o Cohesion
o Prestige and trust
o Many more…
o
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28. SN Metrics: Degree
D1: Size of the degree 1 social circle.
D2: Size of the degree 2 social circle.
Degree 1 Circle = 5
Degree 2 Circle = 7
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29. SN Metrics: Centrality
Centrality measures how ‘important’ an actor is in the
social network.
Highly Central
Very low centrality indicates:
•Social network isolation.
•Low impact on calling circle.
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Isolated
Appropriate segment
for classic approach!
30. Centrality According to Philip Bonacich
Being connected to many people is good, but what indicates the
most influence is to be connected to ‘important’ people.
• Similar to Google’s page rank.
o Bonacich Centrality measures the total number of paths starting
from a node, with a decay factor favouring shorter paths over
longer ones.
• C is vector of centralities
• A is graph matrix.
• Alpha is a scaling factor.
• Beta is decay factor between 0 and 1.
• C = alpha * SUM _ (k = 0 to infinity) Beta ^ k * A ^ (k + 1)
• If Beta = 0, this is degree count.
• If Beta = 1, this is eigenvalue centrality (page rank).
Ideal for computation in parallel!
o
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31. Examples of Bonacich Centrality
(with decay factor of 0.5)
0.64
1.12
1.12
11.28
0.76
0.76
1
0.64
1
1
0.76
1.77
1
1
1
0.73
0.76
0.76
1.30
0.73
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1.46
1.30
0.73
0.73
32. SN Metrics: Reciprocal Degree
RD: Reciprocal Degree.
- Communication in both directions.
Reciprocal
Edge
Reciprocal Degree = 2
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33. SN Metrics: In-Network Degree
RDNetwork: Reciprocal Degree within the network.
- Communication on-network in both directions.
Reciprocal
Edge
Network
Reciprocal Degree within Network = 1
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34. SN Metrics: Triangles
TRG: a count of the number of triangles within a
social network involving a particular focus node.
Degree of interconnectedness in the social network.
In this example, four triangles
exist within the social network
– three of which involve the
focus node.
Triangles = 3
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35. SN Metrics: Betweenness
Betweenness: The number of node pairs that have only a
direct link through the focus node.
This is a simpler (faster) calculation than the more precise
definition that involves an all node pair shortest path
calculation.
Betweenness is a measure of how essential the focus node
is to facilitate communication within the social network.
Betweenness = 6
These subscribers
need the focus node
to communicate
with each other.
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These
subscribers
do not..
36. SN Metrics: Density
DEN: Density is the number of actual edges divided by
the number of possible edges (n * ( n – 1 ) / 2) within a
social network (simplified).
How dense is the calling pattern within the calling
circle?
Low if many nodes in the calling circle are not
connected. Usually low when calling circle is large.
Inversely related to Betweenness.
Density = 3/10
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Only three edges for a
calling circle of five nodes.
37. Social Network Model Creation
Models are created using SN metrics and other
data.
o
Apply the inverse cascading model: Edges are
modelled for the chance that a message or behaviour
will be transmitted.
o
o
Example types of models:
•
Churn risk: how likely is churn spread from A to B?
• Product/service spread: if A uses product/service X,
how likely is it to be taken up by B?
• Viral marketing: if we send A an offer, will they pass
it to B?
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38. Example: Product Affinity Model Timeline
Focus Node
t0
Feb
Mar
Training ADS
Apr
May
June
July
Target
At t0 the subscriber is still active.
o He does not have product X.
o Has a non-trivial centrality score.
o Positive target:
o
• Within
30 days of t0 the subscriber adopts product X.
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39. Example: Viral Adoption Model Timeline
Focus Node
Adjacent Node
t0
Feb
Mar
Training ADS
Apr
May
June
July
Target
At t0 the subscriber is still active.
o He does not have product X.
o Has a non-trivial centrality score.
o Positive target:
o
• Within
30 days of t0 the subscriber adopts product X.
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40. Example: Model Inputs
Affinity Model
Viral Model
Usage History
SN Metrics
Focus Node:
Usage History
SN Metrics
Adjacent Node:
Usage History
SN Metrics
Historical Edge
Information:
Edge Usage History
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41. Measurement and Testing
o
Need to define target and control groups.
The number and size of control groups will vary
according to the effect being testing:
• Control group for direct take up.
• Control group for viral take up.
• Control groups for different messages.
o
o
For example: to control for a X-sell message and model
• A group of high scoring customers should not be
contacted ( control of message).
• A group of low scoring customers should be contacted
(control of model).
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42. Data Mining Using Social Network Analysis
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43. Output of Social Network Analysis
oA
set of metrics than can be input to other analytics and
directly to marketing actions. These metrics are applied to each
customer:
•
Degree
• Centrality
• Betweenness
• Triangles
• Etc.
SN Metrics along with traditional attributes are used to derive
characteristics such as influence, but these should scored
relative to specific topics (price, technology, churn, etc.)
o
o Analysis
is often done in the context of specific
domains…defining time horizon for the analysis, customer
segments, types of call to consider, weekly vs weekend call
patterns, filtering outliers calls, etc.
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45. Social Networks are Not New to Analytics
The idea of understanding relationships to enhance
analytic capabilities is not a new idea.
o Householding, for example, is something that most
sophisticated analytic organizations have been doing
for years.
o Most common use cases:
• Marketing analytics.
• Risk analytics.
• Crime investigation.
• Health care.
o Householding is a very simple form of network
analysis.
o
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46. Traditional Householding
Construction of ‘decision-making units’ based on social
relationships (marriage, co-habitation) for the purpose of
marketing and risk analytics.
Traditional relationship indicators:
•Common address.
•Common last name.
•Marital (or other) relationships.
New scoring variables:
•Pooled assets.
•Pooled purchases.
•Pooled behaviours.
•Derived ‘head of household’ variables.
Used to construct new variables for the purpose of propensity
scoring, risk scoring, segmentation, etc.
Tracking of relationships over time to give visibility to life stage
transitions.
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47. Classic example: 15th Century Florence Family
Politics (Padgett, 1994)
o
o
Degree.
o
Centrality.
o
Betweenness.
o
Geodesic distance.
o
Influence.
o
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Links are marriages.
Pressure.
48. Beyond Householding
Extend an understanding of name, address, and
marital relationships to other kinds of relationships:
Investment accounts
• Credit card accounts
• Mortgages
• Insurance policies
• Frequent flyer accounts
• Buyer-supplier relationships
• Many other possibilities…
•
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49. Beyond Householding
There are many different kinds of relationships:
Joint tenancy with rights of survivorship versus
primary owner on a financial account.
• Credit guarantor (co-signer) versus recipient on a loan
or credit card.
• Custodian versus trustee on a financial account.
• Beneficiary versus owner on a life insurance policy.
• Insured versus payer on a driver’s insurance policy.
• Sponsor versus recipient for frequent flyer status.
• And so on…
•
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50. Marketing Analytics Use Cases
Customer acquisition.
o Cross Selling.
o Customer retention.
o Price bundling.
o Profitability management.
o Customer segmentation.
o
Key concept: Derive new variables for analytic
purposes based on characteristics and events related
to a group of individuals (social network) rather than
looking at individuals in isolation.
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51. Marketing Analytics Use Cases
Simple extension of model variables from an individual level to a
household level gives significant uplift in predictive accuracy:
• Total
customer value by product category
• Total number of accounts or purchases by product category
• Date of last account open or purchase by product category
• Date of first account open or purchase by product category
• Date of last account close or purchase by product category
• Date of first account close or purchase by product category
• Date of last inquiry
• Date of first inquiry
• Total number of inquiries in the last three months
• Total number of inquiries over customer lifetime
• Date of last complaint.
• Date of first complaint
• Total number of complaints in last three months
• Total number of complaints over customer lifetime
• Etc..
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52. Customer Retention by Identifying At-Risk
for Defection Relationships
Individual defection impacts household defection:
When one individual closes all accounts or has a bad
experience, all individuals in the household may be put at
risk. Retention programs identifying these situations can be
put into place to ‘save’ at-risk for defection customers.
Broker defection impacts customer defection:
If the broker for a customer of a financial or insurance
company takes a job with the competition, the customer is
likely to be highly at-risk for defection. Again, explicit
retention programs can be used to ‘save’ these customers.
Important note: roles in the relationships matter.
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53. Risk Analytics Use Cases
Better understand financial risk of incurring bad debt
based on relationships:
•
Scoring for consumer loans/collections based on
household characteristics rather than on an
individual in isolation.
•
Scoring for commercial loans/collections based
on supplier and/or customer relationships with
insight related to industry or customer
concentration as a measure of risk.
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54. Crime Investigation Use Cases
Fraud rings.
• Likely suspects.
• Terrorist networks.
•
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56. Health Care Use Cases
Understanding relationships helps to determine
how diseases spread and therefore how to
better perform disease management:
Tracing epidemics to their origin.
• Prioritizing vaccines and health education.
•
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58. Social Network Analysis with Transactions
Relationships can be inferred through transactions.
There are many different kinds of transactions:
• Telephone calls.
• Messages (SMS, MMS, etc.)
• Emails.
• Package Shipments.
• Financial transactions.
• Physician referrals.
Cardinality of transactions can be used to indicate
intensity of the relationship.
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59. Employee Retention
Google was given a ‘2010 Best HR Ideas’ award from HR
Executive for using analytics to improve employee retention.
Social network analysis can enhance algorithms used to
predict risk of defection.
Use intra-company interactions to understand social network
within the company:
•Telephone
calls.
•Messages (SMS, MMS, etc.)
•Emails.
•Meeting interactions.
•Organizational Structure.
•How ‘connected’ is the employee to the company?
•If one employee leaves, what is the risk of defection for adjacent
nodes in that employees social network.
Combine SN Metrics with traditional metrics (salary, years of
service, performance evaluations, commute distance, etc.) to
build predictive models.
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60. Organizational Effectiveness
Social network analysis can be used to build algorithms to identify
dysfunctions within an organization.
Use intra-company interactions to understand social network within
the company:
Telephone calls.
• Messages (SMS, MMS, etc,.)
• Emails.
• Meeting interactions.
• Organizational Structure.
• What is the level (intensity) of communication between departments that
should be cooperating?
• What is the richness’ and ‘diversity’ of the communications between
departments?
• Always email with no face-to-face meetings or telephone conversations
may indicate a problem.
• Are there a small number of connectors between the departments?
•
• At
what level are they within the organization?
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61. Social Network Analysis (SNA)
in Telecommunications
Social Network Analysis (SNA) is focused on the relations
between subscribers (customers); traditional propensity or
segmentation models are based on individual subscriber attributes.
Map of ties among subscribers provide a useful framework to
identify role played by each individual within the network.
Ties between two subscribers can be identified using voice, calls,
sms, mms, etc.
SNA is often used to:
Define targeted treatments based on network roles of an
individual to encourage/discourage specific events (churn,
acquisition, product adoption).
Monitor new product adoption by studying diffusion within a
social network.
Identify unusual behaviours (fraud detection).
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62. Social Network Analysis for Mobile Network
Operators
Mobile communication is an essential and basic
form of communication between persons in a
social network.
o
There are many communication
channels/mediums; call data captures just a portion
of communication among people.
o
Data availability is quite good for mobile
network operators who capture CDR data – more
data leads to better results.
o
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63. Application of Social Network Analysis
Critical success factors for effective
social network analysis:
Need a critical mass of mobile
phone penetration within the country.
Market share matters:
•
New versus established operators.
• The flower pattern.
Data collection: Need detailed and cleaned history
data.
Understand the profile of your customers
segments (consumer, small business, corporate,
pre-paid, post-paid, etc.)
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64. Application of Social Network Analysis
Challenges with high returns:
• Uncover hidden social network information in your data
– such as phone books in the phones of subscribers.
• Identify key parameters of measured call data
(normalization, skew elimination).
• Separate random connections and weak connections.
• Understand the size and density of the social network.
• Understand the dynamics in the social network: stable
versus volatile communities.
• Target selection, test/control groups, and measurement.
• Read correctly and understand the results of the Social
Network Analysis.
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65. What Kind of Social Networks can be created?
CDRs (Call Detail Records) are generated for each call:
Node1
Node 2
Date
Class
Duration
705 626 2002
416 414 6454
1 Dec 07
Voice
00:04:22
778 388 4363
604 805 5682
1 Dec 07
SMS
00:00:12
…
…
…
…
…
From these CDRs, many different social networks can be built:
•Product
focused:
•Voice Network (Individual A calls Individual B on voice)
•SMS Network (Individual A sends a SMS to Individual B)
•MMS Network *Individual A sends a MMS to Individual B)
•All services Network (Individual A calls or sends SMS or MMS to B)
•Intensiity focused:
•At least 5 communications; duration at least 15 seconds.
•Period focused:
•Interactions during this month, or week, or day…
•Directed or Un-directed
Using different definitions to calculate links gives different networks
for different purposes.
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66. Network Structure
Weak ties: Connection between
communities; connection to the rest of the
‘World’.
Strong ties: Communication ties with high
call frequency and/or high call duration.
Importance of ties from the perspective of:
•
Information diffusion..
• Networrk integrity.
• Kind of tie (driend, intimate, parent/child).
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67. Calculating Social Network Attributes
•Counts
for incoming and outgoing contacts.
•Triangle counts (centrality measure).
•First Circle Statistics: counts, averages,
proportions. Separate counts for nominal variables
(gender, brand).
•Counts and ratios for neighbours on-network and
off-network.
•Sums for link labels (total minutes between
phones).
•Statistics for the community of a user (more than
first circle).
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69. Conclusions on Social Network Analysis
o
Social Network Analysis is not a silver bullet:
•
Can enrich existing marketing, fraud detection, and
other analytic capabilities…
• ….but does not replace traditional analytical
techniques.
• Combine SN Metrics with traditional metrics for
scoring.
Social Network Analysis provides insights as to
how individual customers behave in the context of
larger communities.
o
•
Need to think differently about test and control groups.
• Viral marketing opportunities.
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70. Conclusions on Social Network Analysis
The success of marketing with techniques based on Social
Network Analysis will depend on many factors:
•
Success rates will be different in different cultures and
demographic groups.
• What works in Europe will be different from what works in
Asia or the United States.
The answers are in your data!
For marketing activities to have a viral component there has
to be value to both the A and B nodes:
• Value
can be in terms of prestige (true viral marketing) or can
be financial.
• Most marketing messages have no viral component.
• Any viral marketing must have a fulfilment method to match.
• Product and offer characteristics can have a dramatic impact on
effectiveness of program targeting.
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71. Conclusions on Social Network Analysis
Data! Data! Data!
•It’s
all about the data!
•Don’t under estimate the amount of work and time required to
cleanse and understand the data.
•Want to combine event data with SN data to maximize impact.
•Scalable solutions needed when dealing with large volumes of
data.
Social Network Analysis is different than traditional
analysis.
•New
lingo, new concepts.
•Many different forms of SNA.
•Lots of market confusion between social network analysis and
social media analysis.
•Want to track how networks change over time.
•Measure, learn and improve.
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72. Contact PEAK for more information
You can reach me directly at WAEL@PEAKCONSULTING.EU
or by calling me directly at +44 74 4743 0757.
www.peakconsulting.eu
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73. Recommended Reading
Gladwell, M. The Tipping Point. Little Brown, and Company.
2002.
Green, H. The Rise of Niche Social Networks and that Money
Question. Business Week. On-line Blog. March 15, 2007.
Finkeldey, D. and V. Liu. User Survey Analysis: U.S. SocialMedia Adoption Across Industries. Gartner Research Presentation.
2009.
Pandit, S., D. Chau, S. Wang, and C. Faloutsos.
NetProbe: A Fast and Scalable System for Fraud Detection in
Online Auction Networks. Proceedings of WWW 2007. 2007. pp.
201-210.
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