The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Enhancing community detection using a network weighting strategy
Information Sciences: an International Journal
An O(n2) algorithm for detecting communities of unbalanced sizes in large scale social networks
Knowledge-Based Systems
Integrating social media data for community detection
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
Mining direct antagonistic communities in signed social networks
Information Processing and Management: an International Journal
Overlapping community detection in networks: The state-of-the-art and comparative study
ACM Computing Surveys (CSUR)
Hierarchical parallel algorithm for modularity-based community detection using GPUs
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
| Hi-index | 12.05 |
Detecting communities in social networks represents a significant task in understanding the structures and functions of networks. Several methods are developed to detect disjoint partitions. However, in real graphs vertices are often shared between communities, hence the notion of overlap. The study of this case has attracted, recently, an increasing attention and many algorithms have been designed to solve it. In this paper, we propose an overlapping communities detecting algorithm called DOCNet (Detecting overlapping communities in Networks). The main strategy of this algorithm is to find an initial core and add suitable nodes to expand it until a stopping criterion is met. Experimental results on real-world social networks and computer-generated artificial graphs demonstrate that DOCNet is efficient and highly reliable for detecting overlapping groups, compared with four newly known proposals.