Efficient Algorithms for Node Disjoint Subgraph Homeomorphism Determination

TL;DR

This paper introduces two efficient algorithms for determining node disjoint subgraph homeomorphism, which are scalable and effective for large, dense, and fuzzy-matching graph data, addressing a gap in existing methods.

Contribution

The paper presents the first efficient algorithms for node disjoint subgraph homeomorphism determination using heuristic-based backtracking search.

Findings

Algorithms are computationally efficient and require little time.

Scalable to large and dense graphs.

Effective in complex fuzzy matching scenarios.

Abstract

Recently, great efforts have been dedicated to researches on the management of large scale graph based data such as WWW, social networks, biological networks. In the study of graph based data management, node disjoint subgraph homeomorphism relation between graphs is more suitable than (sub)graph isomorphism in many cases, especially in those cases that node skipping and node mismatching are allowed. However, no efficient node disjoint subgraph homeomorphism determination (ndSHD) algorithms have been available. In this paper, we propose two computationally efficient ndSHD algorithms based on state spaces searching with backtracking, which employ many heuristics to prune the search spaces. Experimental results on synthetic data sets show that the proposed algorithms are efficient, require relative little time in most of the testing cases, can scale to large or dense graphs, and can accommodate to more complex fuzzy matching cases.