Abstract
Most research on information propagation in social networks does not consider how to find information dissemination paths from the information source node to a set of influential nodes. In this paper, we introduce a multicast information propagation model which disseminates information from the information source node to a set of designated influential nodes in social networks, and formulate the problem with the objective to maximize the social influence on the information propagation paths. We then propose a Parallel Multicast information Propagation algorithm (PMP), which concurrently constructs a subgraph for each influential node, joins all the subgraphs into a merge graph, and finds the information propagation paths with the maximum social influence in the merge graph. The simulation results demonstrate that the proposed algorithm can achieve competitive performance in terms of the social influence on the information propagation paths.
This work was partly supported by the National Natural Science Foundation of China (61701162), the Anhui Provincial Natural Science Foundation (1608085MF142), and the open project of State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE2018Z0102B).
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Fan, Y., Wang, L., Shi, L., Du, D. (2019). Parallel Multicast Information Propagation Based on Social Influence. In: Biagioni, E., Zheng, Y., Cheng, S. (eds) Wireless Algorithms, Systems, and Applications. WASA 2019. Lecture Notes in Computer Science(), vol 11604. Springer, Cham. https://doi.org/10.1007/978-3-030-23597-0_46
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DOI: https://doi.org/10.1007/978-3-030-23597-0_46
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