Authors:
Yuan Heng Chou
1
;
En Tzu Wang
2
and
Arbee L. P. Chen
3
Affiliations:
1
National Tsing Hua University, Taiwan
;
2
Industrial Technology Research Institute, Taiwan
;
3
National Chengchi University, Taiwan
Keyword(s):
Dense Sub-graphs, Quasi-cliques, Maximal Quasi-cliques, Maximal Cliques.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Many real-world phenomena such as social networks and biological networks can be modeled as graphs. Discovering dense sub-graphs from these graphs may be able to find interesting facts about the phenomena.
Quasi-cliques are a type of dense graphs, which is close to the complete graphs. In this paper, we want to find all maximal quasi-cliques containing a target vertex in the graph for some applications. A quasi-clique
is defined as a maximal quasi-clique if it is not contained by any other quasi-cliques. We propose an algorithm to solve this problem and use several pruning techniques to improve the performance. Moreover,
we propose another algorithm to solve a special case of this problem, i.e. finding the maximal cliques. The experiment results reveal that our method outperforms the previous work both in real and synthetic datasets
in most cases.