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• W2745199377 abstract "The analysis of node influence plays important role in product marketing, public opinion analysis, disease transmission and other fields. Researchers have proposed a variety of methods to measure node influence, with the rapid expansion of the scale of social networks, Degree Centrality algorithm attracts much attention for its lowest time complexity, however, its result is not sufficiently accurate because it considers only the local node information and not reflects the impact of topological connections among neighbor nodes. To solve this problem, we proposed a novel measure based on Structural Holes and Degree Centrality(SHDC). Our measure combines Structural Hole and Degree Centrality to measure the node influence. It uses Degree Centrality to make a fast and coarse distinction between the influence of nodes and uses Structure Hole to reflect the impact of topological connections among neighbor nodes, which improves the ability to distinguish the influence of nodes in the low time complexity. Experimental results show that the SHDC algorithm can more accurately measure the influence of nodes than Degree Centrality and Structural Hole and it has stronger applicability." @default.
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• W2745199377 date "2017-07-01" @default.
• W2745199377 modified "2023-09-23" @default.
• W2745199377 title "The Node Influence Analysis in Social Networks Based on Structural Holes and Degree Centrality" @default.
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• W2745199377 doi "https://doi.org/10.1109/cse-euc.2017.137" @default.
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