The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
A Graph Based Approach to Extract a Neighborhood Customer Community for Collaborative Filtering
DNIS '02 Proceedings of the Second International Workshop on Databases in Networked Information Systems
Fully automatic cross-associations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
AutoPart: parameter-free graph partitioning and outlier detection
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Web Intelligence and Agent Systems
Temporal multi-page summarization
Web Intelligence and Agent Systems
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th 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
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
Identifying a hierarchy of bipartite subgraphs for web site abstraction
Web Intelligence and Agent Systems
Relation discovery from web data for competency management
Web Intelligence and Agent Systems
An Algorithm to Find Overlapping Community Structure in Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
A Fast Algorithm to Find Overlapping Communities in Networks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Exploring local community structures in large networks
Web Intelligence and Agent Systems
On finding graph clusterings with maximum modularity
WG'07 Proceedings of the 33rd international conference on Graph-theoretic concepts in computer science
Discovering community-oriented roles of nodes in a social network
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Role discovery for graph clustering
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
A parameter-free method for discovering generalized clusters in a network
DS'11 Proceedings of the 14th international conference on Discovery science
Computer Science Review
New spectral methods for ratio cut partitioning and clustering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Graph clustering, or community detection, is an important task of discovering the underlying structure in a network by clustering vertices in a graph into communities. In the past decades, non-overlapping methods such as normalized cuts and modularity-based methods, which assume that each vertex belongs to a single community, are proposed to discover disjoint communities. On the other hand, overlapping methods such as CPM, which assume that each vertex can belong to multiple communities, have been drawing increasing attention as the assumption fits the reality. In this paper, we show that existing non-overlapping and overlapping methods lack consideration to edges that link a vertex to its neighbors belonging to different communities, which often leads to counter-intuitive results of vertices located near borders of communities. Therefore, we propose a new graph clustering methods named RoClust, which uses three roles, bridges, gateways and hubs to discover communities. Each of the three roles represents a kind of vertices that connect communities. Experimental results show that RoClust outperforms state-of-the-art methods of graph clustering including non-overlapping and overlapping methods.