An Unique and Novel Graph Matrix for Efficient Extraction of Structural Information of Networks
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In this article, we propose a new type of square matrix associated with an undirected graph by trading off the naturally imbedded symmetry in them. The proposed matrix is defined using the neighbourhood sets of the vertices. It is called as neighbourhood matrix and it is denoted by NM(G) as this proposed matrix also exhibits a bijection between the product of the two graph matrices, namely the adjacency matrix and the graph Laplacian. This matrix can also be obtained by looking at every vertex and the subgraph with vertices from the first two levels in the level decomposition from that vertex. The two levels in the level decomposition of the graph give us more information about the neighbour of a vertex along with the neighbour of neighbour of a vertex. This insight is required and is found useful in studying the impact of broadcasting on social networks, in particular, and complex networks, in general. We establish several interesting properties of the NM(G) . In addition, we also show how to reconstruct a graph G, given a NM (G). The proposed matrix is also found to solve many graph theoretic problems using less time complexity in comparison to the existing algorithms.
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