Link prediction methods anticipate the likelihood of a future connection between two nodes
in a given network. The methods are essential in social networks to infer social interactions …

Real networks exhibit heterogeneous nature with nodes playing far different roles in
structure and function. To identify vital nodes is thus very significant, allowing us to control …

“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 …

Users organize themselves into communities on web platforms. These communities can
interact with one another, often leading to conflicts and toxic interactions. However, little is …

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 …

Weighted signed networks (WSNs) are networks in which edges are labeled with positive
and negative weights. WSNs can capture like/dislike, trust/distrust, and other social …

Due to the fact much of today's data can be represented as graphs, there has been a
demand for generalizing neural network models for graph data. One recent direction that …

A deluge of new data on real-world networks suggests that interactions among system units
are not limited to pairs, but often involve a higher number of nodes. To properly encode …

In online social networks people often express attitudes towards others, which forms
massive sentiment links among users. Predicting the sign of sentiment links is a fundamental …

Emerging graph neural networks (GNNs) have extended the successes of deep learning
techniques against datasets like images and texts to more complex graph-structured data …