Computational Geometry: Theory and Applications
Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
Journal of the ACM (JACM)
Efficient Algorithms for Shortest Paths in Sparse Networks
Journal of the ACM (JACM)
MFCS '00 Proceedings of the 25th International Symposium on Mathematical Foundations of Computer Science
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
Two flow network simplification algorithms
Information Processing Letters
Supporting creativity: towards associative discovery of new insights
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Link discovery in graphs derived from biological databases
DILS'06 Proceedings of the Third international conference on Data Integration in the Life Sciences
Sparsification of influence networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Compression of weighted graphs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Patterns and logic for reasoning with networks
Bisociative Knowledge Discovery
Simplification of networks by edge pruning
Bisociative Knowledge Discovery
Network compression by node and edge mergers
Bisociative Knowledge Discovery
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We propose a generic framework and methods for simplification of large networks. The methods can be used to improve the understandability of a given network, to complement user-centric analysis methods, or as a pre-processing step for computationally more complex methods. The approach is path-oriented: edges are pruned while keeping the original quality of best paths between all pairs of nodes (but not necessarily all best paths). The framework is applicable to different kinds of graphs (for instance flow networks and random graphs) and connections can be measured in different ways (for instance by the shortest path, maximum flow, or maximum probability). It has relative neighborhood graphs, spanning trees, and certain Pathfinder graphs as its special cases. We give four algorithmic variants and report on experiments with 60 real biological networks. The simplification methods are part of on-going projects for intelligent analysis of networked information.