Mining maximal subgraphs with cohesive structures from a bipartite graph has been widely
studied. One important cohesive structure on bipartite graphs is k-biplex, where each vertex …

As a typical most-to-most connected quasi-biclique model, k-biplex is a superset of
bicliques, which allows nodes on each side of a fully connected subgraph to lose at most k …

Bipartite graphs have been widely used to model the relationship between entities of
different types, where vertices are partitioned into two disjoint sets/sides. Finding dense …

Finding cohesive subgraphs from a bipartite graph is a fundamental operator in bipartite
graph analysis. In this paper, we focus on the problem of mining cohesive subgraphs from a …

Mining cohesive subgraphs from a graph is a fundamental problem in graph data analysis.
One notable cohesive structure is γ-quasi-clique (QC), where each vertex connects at least a …

Enumerating maximal k-biplexes (MBPs) of a bipartite graph has been used for applications
such as fraud detection. Nevertheless, there usually exists an exponential number of MBPs …

As a fundamental problem in graph data mining, Densest Subgraph Discovery (DSD) aims
to find the subgraph with the highest density from a graph. It has been studied for several …

With the advent of a wide spectrum of recent applications, querying large heterogeneous
information networks (HINs) has received a great deal of attention from both academic and …

A k-biplex is an induced subgraph of a bipartite graph which requires every vertex on the
one side disconnecting at most k vertices on the other side. Enumerating all maximal k …

Mining dense subgraphs in a bipartite graph is a fundamental task in bipartite graph
analysis, with numerous applications in community detection, fraud detection, and e …