Maximizing influence via link prediction in evolving networks

IF 2.3 Q2 COMPUTER SCIENCE, THEORY & METHODS Array Pub Date : 2024-09-28 DOI:10.1016/j.array.2024.100366
Kexin Zhang , Mo Li , Shuang Teng , Lingling Li , Yi Wang , Xuezhuan Zhao , Jinhong Di , Ji Zhang
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Abstract

Influence Maximization (IM), targeting the optimal selection of k seed nodes to maximize potential information dissemination in prospectively social networks, garners pivotal interest in diverse realms like viral marketing and political discourse dissemination. Despite receiving substantial scholarly attention, prevailing research predominantly addresses the IM problem within the confines of existing networks, thereby neglecting the dynamic evolutionary character of social networks. An inevitable requisite arises to explore the IM problem in social networks of future contexts, which is imperative for certain application scenarios. In this light, we introduce a novel problem, Influence Maximization in Future Networks (IMFN), aimed at resolving the IM problem within an anticipated future network framework. We establish that the IMFN problem is NP-hard and advocate a prospective solution framework, employing judiciously selected link prediction methods to forecast the future network, and subsequently applying a greedy algorithm to select the k most influential nodes. Moreover, we present SCOL (Sketch-based Cost-effective lazy forward selection algorithm Optimized with Labeling technique), a well-designed algorithm to accelerate the query of our IMFN problem. Extensive experimental results, rooted in five real-world datasets, are provided, affirming the efficacy and efficiency of the proffered solution and algorithms.
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通过演化网络中的链接预测实现影响力最大化
影响最大化(Influence Maximization,IM)的目标是优化 k 个种子节点的选择,以最大限度地提高潜在信息在前瞻性社交网络中的传播,它在病毒式营销和政治言论传播等不同领域引起了举足轻重的关注。尽管受到大量学者的关注,但目前的研究主要是在现有网络的范围内解决 IM 问题,从而忽视了社交网络的动态演化特性。探索未来社会网络中的即时通讯问题是一个必然的要求,这在某些应用场景中势在必行。有鉴于此,我们引入了一个新问题--未来网络中的影响力最大化(IMFN),旨在解决预期未来网络框架中的 IM 问题。我们发现 IMFN 问题具有 NP 难度,并提出了一种前瞻性的解决框架,即采用明智选择的链接预测方法来预测未来网络,然后应用贪婪算法来选择 k 个最具影响力的节点。此外,我们还提出了 SCOL(基于草图的高成本效益懒惰前向选择算法(Scetch-based Cost-effective lazy forward selection algorithm Optimized with Labeling technique),这是一种精心设计的算法,可加速对 IMFN 问题的查询。我们提供了基于五个真实数据集的大量实验结果,肯定了所提出的解决方案和算法的功效和效率。
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来源期刊
Array
Array Computer Science-General Computer Science
CiteScore
4.40
自引率
0.00%
发文量
93
审稿时长
45 days
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