Chapter

Bisociative Knowledge Discovery

Volume 7250 of the series Lecture Notes in Computer Science pp 179-198

Open Access This content is freely available online to anyone, anywhere at any time.

Simplification of Networks by Edge Pruning

  • Fang ZhouAffiliated withDepartment of Computer Science and HIIT, University of Helsinki
  • , Sébastien MahlerAffiliated withDepartment of Computer Science and HIIT, University of Helsinki
  • , Hannu ToivonenAffiliated withDepartment of Computer Science and HIIT, University of Helsinki

Abstract

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.