Cliques and their generalizations are frequently used to model “tightly knit” clusters in
graphs and identifying such clusters is a popular technique used in graph-based data …

A k-club is a distance-based graph-theoretic generalization of a clique, originally introduced
to model cohesive social subgroups in social network analysis. The k-clubs represent low …

Detecting low-diameter clusters is an important graph-based data mining technique used in
social network analysis, bioinformatics and text-mining. Low pairwise distances within a …

A k-club is a distance-based graph-theoretic generalization of clique, originally introduced to
model cohesive subgroups in social network analysis. The k-clubs represent low diameter …

Finding large “cliquish” subgraphs is a central topic in graph mining and community
detection. A popular clique relaxation are 2-clubs: instead of asking for subgraphs of …

Given a simple undirected graph $ G $, the maximum $ k $-club problem is to find a
maximum-cardinality subset of nodes inducing a subgraph of diameter at most $ k $ in $ G …

We introduce a stochastic extension for the problem of finding nonhereditary subgraphs of
maximum size in randomly changing graphs. The proposed formulation utilizes a two-stage …

In the analysis of networks, one often searches for tightly knit clusters. One property of a
“good” cluster is a small diameter (say, bounded by k), which leads to the concept of ak-club …

The Clique problem is one of the best-studied problems in computer science. However,
there exist only few studies concerning the important Clique generalization, called the s …

Given a simple undirected graph G, ak-club is a subset of vertices inducing a subgraph of
diameter at most k. The maximum k-club problem (M k CP) is to find ak-club of maximum …