A Structure Similarity Determination Method for Aggregation Label Information of k-tuple Group in Node Neighborhood
In complex networks,nodes often form higher-order substructures with specific local connectivity patterns that frequently appear.These higher-order substructures can better characterize the network's topological features and related functional modules.Measuring the structural similarity between nodes aids in studying the interaction patterns within the network's topology and under-standing the local structures and functions of complex networks.To fully utilize the higher-order structural information in node neighborhoods,we propose a method for determining structural similarity using the label information of k-tuple groups in node neighborhoods,called GANNLI(Group-based Aggregated Neighborhood Label Information).This method first forms non-isomor-phic subgraphs as group labels for k-tuple node groups,and then uses the Weisfeiler-Lehman(WL)method to aggregate and update the neighborhood group label information of k-tuple node groups.It counts the label information of different k-tuples formed by nodes to obtain node representations and calculates the structural similarity between nodes using cosine similarity.Compared to methods that only consider low-order information such as node degree and closeness centrality,our approach leverages higher-order k-tuple structural information to more effectively measure structural similarity between nodes.Experimental results on real network datasets demonstrate that the proposed GANNLI algorithm can more effectively calculate structural similarity between nodes,there-by improving the performance of node classification tasks.Specifically,the GANNLI shows a performance improvement of 2%to 6%over Struc2vec algorithm and an improvement of 8%to 14%over Node2vec algorithm.These results indicate that the GANN-LI's ability to incorporate higher-order structural information into the analysis of node neighborhoods allows for more accurate and insightful modeling of complex networks,leading to enhanced understanding and better performance in practical applications.
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