Multi-Attributed Graph Matching With Multi-Layer Graph Structure and Multi-Layer Random Walks - 2018


This Project addresses the multi-attributed graph matching drawback, which considers multiple attributes jointly while preserving the characteristics of each attribute for graph matching. Since most of typical graph matching algorithms integrate multiple attributes to construct a single unified attribute in an oversimplified manner, the knowledge from multiple attributes is often not utterly utilized. In order to solve this problem, we have a tendency to propose a completely unique multi-layer graph structure which will preserve the characteristics of each attribute in separated layers, and also propose a multi-attributed graph matching algorithm based on random walk centrality with the proposed multi-layer graph structure. We tend to compare the proposed algorithm with other state-of-the-art graph matching algorithms primarily based on a single-layer structure using artificial and real data sets and demonstrate the superior performance of the proposed multi-layer graph structure and therefore the multi-attributed graph matching algorithm.

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