Recently, graph neural networks (GNNs) have shown prominent performance in semi-
supervised node classification by leveraging knowledge from the graph database. However …

Many networked datasets with units interacting in groups of two or more, encoded with
hypergraphs, are accompanied by extra information about nodes, such as the role of an …

For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the
generative paradigm and learns to reconstruct masked graph edges or node features while …

Hypergraphs are marked by complex topology, expressing higher-order interactions among
multiple entities with hyperedges. Lately, hypergraph-based deep learning methods to learn …

Attributed networks containing entity-specific information in node attributes are ubiquitous in
modeling social networks, e-commerce, bioinformatics, etc. Their inherent network topology …

Group interactions arise in various scenarios in real-world systems: collaborations of
researchers, co-purchases of products, and discussions in online Q&A sites, to name a few …

Attributed bipartite graphs (ABGs) are an expressive data model for describing the
interactions between two sets of heterogeneous nodes that are associated with rich …

Unified graph representation learning aims to produce node embeddings, which can be
applied to multiple downstream applications. However, existing studies based on graph …

Subspace clustering seeks to identify subspaces that segment a set of n data points into k
(k<< n) groups, which has emerged as a powerful tool for analyzing data from various …

Power systems have numerous issues arising from increased demand and advancements in
technology. Among these challenges, the most major ones are the planning of reactive …