Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Patterns of influence in a recommendation network
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Pruthak: mining and analyzing graph substructures
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Interactive searching and visualization of patterns in attributed graphs
Proceedings of Graphics Interface 2010
GViewer: GPU-accelerated graph visualization and mining
SocInfo'11 Proceedings of the Third international conference on Social informatics
Expertise ranking using activity and contextual link measures
Data & Knowledge Engineering
TourViz: interactive visualization of connection pathways in large graphs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Several graph visualization tools exist. However, they are not able to handle large graphs, and/or they do not allow interaction. We are interested on large graphs, with hundreds of thousands of nodes. Such graphs bring two challenges: the first one is that any straightforward interactive manipulation will be prohibitively slow. The second one is sensory overload: even if we could plot and replot the graph quickly, the user would be overwhelmed with the vast volume of information because the screen would be too cluttered as nodes and edges overlap each other.Our GMine system addresses both these issues, by using summarization and multi-resolution. GMine offers multi-resolution graph exploration by partitioning a given graph into a hierarchy of communities-within-communities and storing it into a novel R-treelike structure which we name G-Tree. GMine offers summarization by implementing an innovative subgraph extraction algorithm and then visualizing its output.