The influence maximization(IM)problem aims to find a set of seed nodes that maximizes the spread of their influence in a social *** positive influence maximization(PIM)problem is an extension of the IM problem,which c...
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The influence maximization(IM)problem aims to find a set of seed nodes that maximizes the spread of their influence in a social *** positive influence maximization(PIM)problem is an extension of the IM problem,which consider the polar relation of nodes in signed social networks so that the positive influence of seeds can be the most widely *** solve the PIM problem,this paper proposes the polar and decay related independent cascade(IC-PD)model to simulate the influence propagation of nodes and the decay of information during the influence propagation in signed social *** overcome the low efficiency of the greedy based algorithm,this paper defines the polar reverse reachable(PRR)set and devises a signed reverse influence sampling(SRIS)*** algorithm utilizes the ICPD model as well as the PRR set to select *** are two phases in *** is the sampling phase,which utilizes the IC-PD model to generate the PRR set and a binary search algorithm to calculate the number of needed PRR *** other is the node selection phase,which uses a greedy coverage algorithm to select optimal ***,Experiments on three real-world polar social network datasets demonstrate that SRIS outperforms the baseline algorithms in *** on the Slashdot dataset,SRIS achieves 24.7% higher performance than the best-performing compared algorithm under the weighted cascade model when the seed set size is 25.
作者:
Zhou, JingShang, JunChen, TongwenUniversity of Alberta
Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Tongji University
Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China
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