Approximating clique is almost NP-complete (preliminary version)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Graph drawing by force-directed placement
Software—Practice & Experience
CSV: visualizing and mining cohesive subgraphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
On triangulation-based dense neighborhood graph discovery
Proceedings of the VLDB Endowment
Efficient core decomposition in massive networks
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Triangle listing in massive networks and its applications
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Visual Reasoning about Social Networks Using Centrality Sensitivity
IEEE Transactions on Visualization and Computer Graphics
The role of social networks in information diffusion
Proceedings of the 21st international conference on World Wide Web
Extracting Analyzing and Visualizing Triangle K-Core Motifs within Networks
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Truss decomposition in massive networks
Proceedings of the VLDB Endowment
Database research at the National University of Singapore
ACM SIGMOD Record
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Graphs are widely used in large scale social network analysis nowadays. Not only analysts need to focus on cohesive subgraphs to study patterns among social actors, but also normal users are interested in discovering what happening in their neighborhood. However, effectively storing large scale social network and efficiently identifying cohesive subgraphs is challenging. In this work we introduce a novel subgraph concept to capture the cohesion in social interactions, and propose an I/O efficient approach to discover cohesive subgraphs. Besides, we propose an analytic system which allows users to perform intuitive, visual browsing on large scale social networks. Our system stores the network as a social graph in the graph database, retrieves a local cohesive subgraph based on the input keywords, and then hierarchically visualizes the subgraph out on orbital layout, in which more important social actors are located in the center. By summarizing textual interactions between social actors as tag cloud, we provide a way to quickly locate active social communities and their interactions in a unified view.