Higher-order graph clustering aims to partition the graph using frequently occurring
subgraphs (ie, motifs), instead of the lower-order edges, as the atomic clustering unit, which …

Size-constrained community search, retrieving a size-bounded high-quality subgraph
containing user-specified query vertices, has been extensively studied in graph analysis …

Community search is a fundamental graph processing task that aims to find a community
containing the given query node. Recent studies show that machine learning (ML)-based …

Community search has been extensively studied in the past decades. In recent years, there
is a growing interest in attributed community search that aims to identify a community based …

Given a graph, searching for a community containing a query vertex is a fundamental
problem and has found many applications. Most existing community search models are …

The problem of efficiently computing all k-edge-connected components (k-ECCs) of a graph
G for a user-given k has been extensively studied recently in view of its importance in many …

Synthetic graph datasets are crucial for the assessment of network analysis algorithms,
providing a measure of their effectiveness and efficiency. However, most existing generation …

Evolving graphs consisting of slices are large and constantly changing. For example, in
Alipay, the graph generates hundreds of millions of new transaction records every day …

Retrieving cohesive subgraphs in networks is a fundamental problem in social network
analysis and graph data management. These subgraphs can be used for marketing …

Community search has aroused widespread interest in the past decades. Among existing
solutions, the learning-based models exhibit outstanding performance in terms of accuracy …