The problem of extracting meaningful data through graph analysis spans a range of different
fields, such as the internet, social networks, biological networks, and many others. The …

The core decomposition of networks has attracted significant attention due to its numerous
applications in real-life problems. Simply stated, the core decomposition of a network …

Motivated by recent studies in the data mining community which require to efficiently list all k-
cliques, we revisit the iconic algorithm of Chiba and Nishizeki and develop the most efficient …

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 …

The densest subgraph problem in a graph (DSG), in the simplest form, is the following.
Given an undirected graph G=(V, E) find a subset S⊆ V of vertices that maximizes the ratio …

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 …

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 …

We study the densest subgraph problem (DSG) and the densest subgraph local
decomposition problem (DSG-LD) in undirected graphs. We also consider supermodular …

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

The problem of finding densest subgraphs has received increasing attention in recent years
finding applications in biology, finance, as well as social network analysis. The k-clique …