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Centrality measures based algorithm for computing a maximal common connected edge subgraph of two chemical graphs
Abstract
Graph similarity plays an essential role in various fields of scientific and industry oriented applications such as bioinformatics and computational chemistry. The process of determining structural similarities between chemical structures of molecules helps to identify common behavior of these molecules. A promising approach to capture the structural similarity between two chemical compounds is to detect a maximal Common Connected Edge induced Subgraph (CCES) in their molecular graphs. This paper detects a large sized maximal CCES of two given chemical graphs using a new technique incorporating centrality measures. The idea is to use a DFS search tree whose root node is chosen as the one having the highest average of closeness and reach centrality measures from the tensor product graph of the input graphs. This measure of average narrows down the search space of the problem. The experimental results, on synthetic and real chemical database, further ensure the efficiency of the proposed algorithm when compared with the existing works.
The physical and biological properties of a chemical molecule entity are related to its structure. One of the basic widely accepted principles in chemistry is that compounds with similar structures frequently share similar physicochemical properties and biological activities. The process of finding structural similarities between chemical structures of molecules helps to identify the common behavior of these molecules. A familiar approach to capture the structural similarity between two chemical compounds is to detect a maximal Common Connected vertex induced Subgraph (CCS) in their molecular chemical graphs. The proposed algorithm detects a maximal CCS by checking the induced property of the vertices which are collected by performing a DFS search on the tensor product graph of two input molecular chemical graphs. The DFS search will start from the node which has the highest eigenvector centrality in the tensor product graph. The significance of the proposed work is that it uses eigenvector centrality to predict the root node of the DFS search tree, so that the resulting sugraph gets more number of nodes (i.e. large size maximal CCS). The experimental results on synthetic and real chemical database, further ensure the competence of the proposed algorithm when compared with the existing works.
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