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Identification of influential nodes based on temporal-aware modeling of multi-hop neighbor interactions for influence spread maximization

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  • Ullah, Farman
  • Lee, Sungchang

Abstract

This paper presents the identification of highly influential nodes based on temporal-aware modeling of multi-hop neighbor interactions to maximize the spread of information in online social networks (OSNs). The objective is to choose a set of influential nodes that have higher temporal multi-hop interactions and more topological connections in large-scale OSNs to maximize information dissemination and minimize spreading time. An influence diffusion process that is solely based on topology is not able to capture the influence spreading efficiently. A temporal multi-hops social interaction based centrality is proposed to choose nodes of higher spreading ability considering the nodes’ neighbors and neighbors-of-neighbors temporal modeled interactions and topological connections. The temporal-aware interactions are modeled to find users who are more active recently. First, we model the influence between users considering the temporal interactions of the user and its neighbors. A subset of nodes with a higher influence value and more topological connections with direct neighbors is selected. Secondly, we select the Top-K higher influential spreader nodes from the subset of nodes considering the node neighbors and neighbors-of-neighbors temporal modeled social interactions and topological connections. Finally, the proposed algorithm is evaluated using the epidemic spreading models. The experimental results show that the algorithm is able to extract highly influential nodes that maximize the spread of information and minimize contagion time.

Suggested Citation

  • Ullah, Farman & Lee, Sungchang, 2017. "Identification of influential nodes based on temporal-aware modeling of multi-hop neighbor interactions for influence spread maximization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 968-985.
    • Handle: RePEc:eee:phsmap:v:486:y:2017:i:c:p:968-985
    DOI: 10.1016/j.physa.2017.05.089
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    References listed on IDEAS

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    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Jalili, Mahdi, 2013. "Social power and opinion formation in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 959-966.
    3. Yan, Qiang & Wu, Lianren & Zheng, Lan, 2013. "Social network based microblog user behavior analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(7), pages 1712-1723.
    4. AskariSichani, Omid & Jalili, Mahdi, 2015. "Influence maximization of informed agents in social networks," Applied Mathematics and Computation, Elsevier, vol. 254(C), pages 229-239.
    5. Christel Kamp & Mathieu Moslonka-Lefebvre & Samuel Alizon, 2013. "Epidemic Spread on Weighted Networks," PLOS Computational Biology, Public Library of Science, vol. 9(12), pages 1-10, December.
    6. Gao, Shuai & Ma, Jun & Chen, Zhumin & Wang, Guanghui & Xing, Changming, 2014. "Ranking the spreading ability of nodes in complex networks based on local structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 130-147.
    7. Mahendra Piraveenan & Mikhail Prokopenko & Liaquat Hossain, 2013. "Percolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-14, January.
    8. Hu, Jiantao & Du, Yuxian & Mo, Hongming & Wei, Daijun & Deng, Yong, 2016. "A modified weighted TOPSIS to identify influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 73-85.
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