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 …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Many Graph Neural Networks (GNNs) perform poorly compared to simple heuristics on Link
Prediction (LP) tasks. This is due to limitations in expressive power such as the inability to …

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 …

Link prediction is a key problem for network-structured data. Link prediction heuristics use
some score functions, such as common neighbors and Katz index, to measure the likelihood …

Network embedding assigns nodes in a network to low-dimensional representations and
effectively preserves the network structure. Recently, a significant amount of progresses …

We consider the graph link prediction task, which is a classic graph analytical problem with
many real-world applications. With the advances of deep learning, current link prediction …

Graphs, such as social networks, word co-occurrence networks, and communication
networks, occur naturally in various real-world applications. Analyzing them yields insight …

Social and information networking activities such as on Facebook, Twitter, WeChat, and
Weibo have become an indispensable part of our everyday life, where we can easily access …