The Densest Subgraph Problem requires us to find, in a given graph, a subset of vertices
whose induced subgraph maximizes a measure of density. The problem has received a …

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 …

Given a directed graph G, the directed densest subgraph (DDS) problem refers to the finding
of a subgraph from G, whose density is the highest among all the subgraphs of G. The DDS …

Graph analytics have been effective in the data science pipeline of fraud detections. In the
ever-evolving landscape of e-commerce platforms like Grab or transaction networks such as …

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 prevalence of graphs for modeling complex relationships among objects, the topic
of graph mining has attracted a great deal of attention from both academic and industrial …

Network analysis has played a key role in knowledge discovery and data mining. In many
real-world applications in recent years, we are interested in mining multilayer networks …

Dense subgraph discovery is an important graph-mining primitive with a variety of real-world
applications. One of the most well-studied optimization problems for dense subgraph …

In the densest subgraph problem, given an undirected graph G (V, E, w) with non-negative
edge weights we are asked to find a set of nodes S ⊆ V that maximizes the degree density …

Densest subgraphs are often interpreted as communities, based on a basic assumption that
the connections inside a community are much denser than those between communities. In a …