WSM: a novel algorithm for subgraph matching in large weighted graphs

Abstract

Given an undirected/directed large weighted data graph and a similar smaller weighted pattern graph, the problem of weighted subgraph matching is to find a mapping of the nodes in the pattern graph to a subset of nodes in the data graph such that the sum of edge weight differences is minimum. Biological interaction networks such as protein-protein interaction networks and molecular pathways are often modeled as weighted graphs in order to account for the high false positive rate occurring intrinsically during the detection process of the interactions. Nonetheless, complex biological problems such as disease gene prioritization and conserved phylogenetic tree construction largely depend on the similarity calculation among the networks. Although several existing methods provide efficient methods for graph and subgraph similarity measurement, they produce nonintuitive results due to the underlying unweighted graph model assumption. Moreover, very few algorithms exist for weighted graph matching that are applicable with the restriction that the data and pattern graph sizes are equal. In this paper, we introduce a novel algorithm for weighted subgraph matching which can effectively be applied to directed/undirected weighted subgraph matching. Experimental results demonstrate the superiority and relative scalability of the algorithm over available state of the art methods.

Navigating Traditional Chinese Medicine Network Pharmacology and Computational Tools

“…Compared with the LM-based approach, LMMA could significantly eliminate false positive relations to obtain a more reliable interaction network. Some recent subgroup analysis-based approaches, such as MIClique [103], WSM [104], and CPM [105], can identify the disease-gene relationship. Most of these approaches were not applied on TCM network pharmacology, but they are all instructive.…”

Anatomy of Graph Matching based on an XQuery and RDF Implementation

“…In addition to the minimum hub covers, we computed the number of solutions explored when processing all of the query graphlets using all possible orderings. The best ordering among these is (3,5,8,6,1,2,4,7), in which our proposal explores 309 solutions. Note that the initial vertices correspond to the best ordering of the minimum hub cover, which entails that we have already computed all of the solutions after processing these three vertices.…”

“…Therefore, we are performing an extra computation that it is not mandatory. On the contrary, the worst ordering is (5,2,7,1,8,4,6,3), which explores 8815 solutions.…”

“…This is due to the fact that graphs allow to model complex relationships that cannot be easily captured using traditional data models like the relational model [4]. One of the crucial tasks of graph management is graph matching, which aims to find all of the subgraphs in a data graph that match a query graph [1].…”

A semantic framework for intelligent matchmaking for clinical trial eligibility criteria

“…An example of such efforts is the previous work on weighted subgraph matching in biological network [Bhattacharjee and Jamil 2012]. Le Clément et al [2009] proposed a new constraint-based modeling language to define graphic similarity measures for programming.…”