Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

Influence maximization (IM) is formulated as selecting a set of initial users from a social
network to maximize the expected number of influenced users. Researchers have made …

Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

Abstract Graph Neural Networks (GNNs) have proven to be useful for many different
practical applications. However, many existing GNN models have implicitly assumed …

Abstract Graph Neural Networks (GNNs) have shown promising results on a broad spectrum
of applications. Most empirical studies of GNNs directly take the observed graph as input …

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 …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

The acquisition, processing, mining, and visualization of sensory data for knowledge
discovery and decision support has recently been a popular area of research and …

Networks evolve continuously over time with the addition, deletion, and changing of links
and nodes. Although many networks contain this type of temporal information, the majority of …