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INCIM: A community-based algorithm for influence maximization problem under the linear threshold model

机译：INCIM：线性阈值模型下基于社区的影响最大化问题算法

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摘要

With the proliferation of graph applications in social network analysis, biological networks, WWW and many other areas, a great demand of efficient and scalable algorithms for graph mining is rising. In many applications, finding the most influential nodes in the network is informative for the network analyzers in order to track the spread of information, disease and rumors. The problem of finding the top k influential nodes of a directed graph G = (V, E) such that the influence spread of these nodes will be maximized has long been exposed and many algorithms have been proposed to deal with this problem. Despite the useful characteristics of community structure in social networks, only a few works have studied the role of communities in the spread of influence in social networks.In this paper we propose an efficient algorithm (which has an acceptable response time even for large graphs) for finding the influential nodes in the graph under linear threshold model. We exploit the community structure of graph to find the influential communities, and then find the influence of each node as a combination of its local and global influences. We compare our algorithm with the state-of-the-art methods for influence maximization problem and the results of our experiments on real world datasets show that our approach outperforms the other ones in the quality of outputted influential nodes while still has acceptable running time and memory usage for large graphs.
机译：随着图在社交网络分析，生物网络，WWW和许多其他领域中的应用激增，对图挖掘的高效和可扩展算法的需求日益增长。在许多应用中，查找网络中最具影响力的节点对于网络分析仪而言是很有帮助的，以便跟踪信息，疾病和谣言的传播。长期以来一直存在寻找有向图G =（V，E）的前k个影响节点以使这些节点的影响范围最大化的问题，并且已经提出了许多算法来解决此问题。尽管社交网络中社区结构具有有用的特性，但只有少数工作研究了社区在社交网络中影响力传播中的作用。本文提出了一种有效的算法（即使对于大型图，响应时间也可以接受）在线性阈值模型下查找图中的影响节点。我们利用图的社区结构找到有影响力的社区，然后将每个节点的影响作为其局部和全局影响的组合来查找。我们将我们的算法与影响最大的问题的最新方法进行了比较，我们在现实世界数据集上的实验结果表明，我们的方法在输出有影响力节点的质量上胜过其他方法，同时仍具有可接受的运行时间和大图的内存使用情况。

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