OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Epidemic thresholds in real networks
ACM Transactions on Information and System Security (TISSEC)
A novel similarity-based modularity function for graph partitioning
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Community detection in incomplete information networks
Proceedings of the 21st international conference on World Wide Web
Document hierarchies from text and links
Proceedings of the 21st international conference on World Wide Web
Predicting aggregate social activities using continuous-time stochastic process
Proceedings of the 21st ACM international conference on Information and knowledge management
Like-Minded communities: bringing the familiarity and similarity together
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
TODMIS: mining communities from trajectories
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Hierarchical community decomposition via oblivious routing techniques
Proceedings of the first ACM conference on Online social networks
Modeling and detecting community hierarchies
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
Hi-index | 0.00 |
Community detection is an important task for mining the structure and function of complex networks. Generally, there are several different kinds of nodes in a network which are cluster nodes densely connected within communities, as well as some special nodes like hubs bridging multiple communities and outliers marginally connected with a community. In addition, it has been shown that there is a hierarchical structure in complex networks with communities embedded within other communities. Therefore, a good algorithm is desirable to be able to not only detect hierarchical communities, but also identify hubs and outliers. In this paper, we propose a parameter-free hierarchical network clustering algorithm SHRINK by combining the advantages of density-based clustering and modularity optimization methods. Based on the structural connectivity information, the proposed algorithm can effectively reveal the embedded hierarchical community structure with multiresolution in large-scale weighted undirected networks, and identify hubs and outliers as well. Moreover, it overcomes the sensitive threshold problem of density-based clustering algorithms and the resolution limit possessed by other modularity-based methods. To illustrate our methodology, we conduct experiments with both real-world and synthetic datasets for community detection, and compare with many other baseline methods. Experimental results demonstrate that SHRINK achieves the best performance with consistent improvements.