Finding reliable subgraphs from large probabilistic graphs

• Published in: Data mining and knowledge discovery Vol. 17; no. 1; pp. 3 - 23
• Main Authors: Hintsanen, Petteri, Toivonen, Hannu
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.08.2008; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Data mining; Data Mining and Knowledge Discovery; Graphs; Heuristic; Hypotheses; Information Storage and Retrieval; Physics; Search engines; Search strategies; Statistics for Engineering; Graph visualization; Reliability; Link discovery and analysis; Graph mining
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-008-0106-1

Reliable subgraphs can be used, for example, to find and rank nontrivial links between given vertices, to concisely visualize large graphs, or to reduce the size of input for computationally demanding graph algorithms. We propose two new heuristics for solving the most reliable subgraph extraction problem on large, undirected probabilistic graphs. Such a problem is specified by a probabilistic graph G subject to random edge failures, a set of terminal vertices, and an integer K . The objective is to remove K edges from G such that the probability of connecting the terminals in the remaining subgraph is maximized. We provide some technical details and a rough analysis of the proposed algorithms. The practical performance of the methods is evaluated on real probabilistic graphs from the biological domain. The results indicate that the methods scale much better to large input graphs, both computationally and in terms of the quality of the result.