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 …

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 …

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

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 …

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 …

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

Normalization is a mandatory preprocessing step in time series problems to guarantee
similarity comparisons invariant to unexpected distortions in amplitude and offset. Such …

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

Finding an optimal set of nodes, called key players, whose activation (or removal) would
maximally enhance (or degrade) a certain network functionality, is a fundamental class of …

Hypergraph, an expressive structure with flexibility to model the higher-order correlations
among entities, has recently attracted increasing attention from various research domains …