Link prediction finds missing links (in static networks) or predicts the likelihood of future links
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …

Networks have become increasingly important to model complex systems composed of
interacting elements. Network data mining has a large number of applications in many …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

Our personal social networks are big and cluttered, and currently there is no good way to
organize them. Social networking sites allow users to manually categorize their friends into …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

In social networks, link prediction predicts missing links in current networks and new or
dissolution links in future networks, is important for mining and analyzing the evolution of …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

Traditional link prediction methods are generally only calculated for the neighbor information
of nodes, and the network path between nodes has not been fully utilized. Therefore, this …

Link prediction is one of the most widely studied problems in the area of complex network
analysis, in which machine learning techniques can be applied to deal with it. The biggest …