PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Framework for Path-Oriented Network Simplification
Hannu Toivonen, Sebastien Mahler and Fang Zhou
In: The Ninth International Symposium on Intelligent Data Analysis (IDA), May 2010, Tucson, USA.

Abstract

We propose a generic framework and methods for simplifi- cation of large networks. The methods can be used to improve the un- derstandability 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 keep- ing 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 con- nections can be measured in different ways (for instance by the shortest path, maximum flow, or maximum probability). It has relative neighbor- hood 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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:7887
Deposited By:Hannu Toivonen
Deposited On:17 March 2011