Abstract
Traditional centrality measures such as degree, betweenness, closeness and eigenvector ignore the intrinsic impacts of a node on other nodes. This paper proposes a new algorithm, called HIPRank, to rank nodes based on their influences in the network. HIPRank includes two sub-procedures: one is to predefine the importance of an arbitrary small number of nodes with users’ preferences, and the other one is to propagate the influences of nodes with respect to authority and hub to other nodes based on HIP propagation model. Experiments on DBLP citation network (over 1.5 million nodes and 2.1 million edges) demonstrate that on the one hand, HIPRank can prioritize the nodes having close relation to the user-preferred nodes with higher ranking than other nodes, and on the other hand, HIPRank can retrieve the authoritative nodes (with authority) and directive nodes (with hub) from the network according to users’ preferences.
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Acknowledgements
This research was supported in part by National Natural Science Foundation of China under Grant Nos. 61073044, 71101138, 61003028 and 61379046; National Science and Technology Major Project under Grant Nos. 2012ZX01039-004; Beijing Natural Science Fund under Grant No.4122087; State Key Laboratory of Software Engineering of Wuhan University.
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Zhang, W., Wang, S., Han, G., Yang, Y., Wang, Q. (2015). HIPRank: Ranking Nodes by Influence Propagation Based on Authority and Hub. In: Bai, Q., Ren, F., Zhang, M., Ito, T., Tang, X. (eds) Smart Modeling and Simulation for Complex Systems. Studies in Computational Intelligence, vol 564. Springer, Tokyo. https://doi.org/10.1007/978-4-431-55209-3_1
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DOI: https://doi.org/10.1007/978-4-431-55209-3_1
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