MFCS '00 Proceedings of the 25th International Symposium on Mathematical Foundations of Computer Science
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A new variant of the Pathfinder algorithm to generate large visual science maps in cubic time
Information Processing and Management: an International Journal
Finding reliable subgraphs from large probabilistic graphs
Data Mining and Knowledge Discovery
Parallel PathFinder Algorithms for Mining Structures from Graphs
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Network Simplification with Minimal Loss of Connectivity
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Compression of weighted graphs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient algorithms for simplifying flow networks
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
Link discovery in graphs derived from biological databases
DILS'06 Proceedings of the Third international conference on Data Integration in the Life Sciences
A framework for path-oriented network simplification
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Towards creative information exploration based on koestler's concept of bisociation
Bisociative Knowledge Discovery
Review of bisonet abstraction techniques
Bisociative Knowledge Discovery
Bisociative Knowledge Discovery
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We propose a novel problem to simplify weighted graphs by pruning least important edges from them. Simplified graphs can be used to improve visualization of a network, to extract its main structure, or as a pre-processing step for other data mining algorithms. We define a graph connectivity function based on the best paths between all pairs of nodes. Given the number of edges to be pruned, the problem is then to select a subset of edges that best maintains the overall graph connectivity. Our model is applicable to a wide range of settings, including probabilistic graphs, flow graphs and distance graphs, since the path quality function that is used to find best paths can be defined by the user. We analyze the problem, and give lower bounds for the effect of individual edge removal in the case where the path quality function has a natural recursive property. We then propose a range of algorithms and report on experimental results on real networks derived from public biological databases. The results show that a large fraction of edges can be removed quite fast and with minimal effect on the overall graph connectivity. A rough semantic analysis of the removed edges indicates that few important edges were removed, and that the proposed approach could be a valuable tool in aiding users to view or explore weighted graphs.