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Finding Influential Nodes in Social Networks Using Minimum k-Hop Dominating Set

机译:使用最小k跳主导集中在社交网络中找到有影响的节点

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Challenges in social interaction networks are often modelled as graph theoretic problems. One such problem is to find a group of influential individuals of minimum size or the initial seed set in a social network, so that all the nodes in the network can be reached with only one hop from the seeds. This problem is equivalent to finding a minimum dominating set for the network. In this paper, we address a problem which is similar to finding minimum dominating set but differs in terms of number of hops needed to reach all the nodes. We have generalized the problem as k-hop dominating set problem, where a maximum of k hops will be allowed to spread the information among all the nodes of the graph. We show that the decision version of the k-hop dominating set problem is NP-complete. Results show that, in order to reach the same percentage of nodes in the network, if one extra hop is allowed then the cardinality of the seed set i.e. the number of influential nodes needed, is considerably reduced. Also, the experimental results show that the influential nodes can be characterized by their high betweenness values.
机译:社交互动网络中的挑战通常是图形理论问题的建模。这样的问题是找到一组有影响力的最小尺寸或初始种子中的初始种子集合,使得网络中的所有节点只能从种子中跳跃到达。此问题相当于找到网络的最低​​主导集。在本文中,我们解决了一个类似于找到最小主导集的问题,但在到达所有节点所需的跳数方面不同。我们将问题概括为k跳主导集合问题,其中最多允许k跳将传播图表的所有节点之间的信息。我们展示了K-Hop主导集合问题的决策版本是NP完成的。结果表明,为了达到网络中的相同百分比,如果允许额外的跳跃,则种子组的基数即所需的有影响力的节点的数量。而且,实验结果表明,影响的节点可以在其高度之间表征。

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