Disinformation-the deliberate spread of false or misleading information poses a significant
threat to our society by undermining trust, exacerbating polarization, and manipulating …

Numerous graph mining applications rely on detecting subgraphs which are large near-
cliques. Since formulations that are geared towards finding large near-cliques are hard and …

Densest subgraph discovery (DSD) is a fundamental problem in graph mining. It has been
studied for decades, and is widely used in various areas, including network science …

Finding dense substructures in a graph is a fundamental graph mining operation, with
applications in bioinformatics, social networks, and visualization to name a few. Yet most …

As one of the most fundamental problems in graph data mining, the densest subgraph
discovery (DSD) problem has found a broad spectrum of real applications, such as social …

Discovering dense subgraphs and understanding the relations among them is a
fundamental problem in graph mining. We want to not only identify dense subgraphs, but …

Finding the densest subgraph (DS) from a graph is a fundamental problem in graph
databases. The DS obtained, which reveals closely related entities, has been found to be …

Transaction processing database management systems (DBMSs) are critical for today's data-
intensive applications because they enable an organization to quickly ingest and query new …

Finding the dense regions of a graph and relations among them is a fundamental problem in
network analysis. Core and truss decompositions reveal dense subgraphs with hierarchical …

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 …