LGM: mining frequent subgraphs from linear graphs

Authors:
Yasuo Tabei;Daisuke Okanohara;Shuichi Hirose;Koji Tsuda
Affiliations:
Japan Science and Technology Agency, Sapporo, Japan;Preferred Infrastructure, Inc, Tokyo, Japan;Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan;Japan Science and Technology Agency, Sapporo, Japan and Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
Venue:
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Year:
2011

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Abstract

A linear graph is a graph whose vertices are totally ordered. Biological and linguistic sequences with interactions among symbols are naturally represented as linear graphs. Examples include protein contact maps, RNA secondary structures and predicate-argument structures. Our algorithm, linear graph miner (LGM), leverages the vertex order for efficient enumeration of frequent subgraphs. Based on the reverse search principle, the pattern space is systematically traversed without expensive duplication checking. Disconnected subgraph patterns are particularly important in linear graphs due to their sequential nature. Unlike conventional graph mining algorithms detecting connected patterns only, LGM can detect disconnected patterns as well. The utility and efficiency of LGM are demonstrated in experiments on protein contact maps.