大规模社交网络中的低直径聚合子群

Niloufar Daemi, J. S. Borrero, Balabhaskar Balasundaram
{"title":"大规模社交网络中的低直径聚合子群","authors":"Niloufar Daemi, J. S. Borrero, Balabhaskar Balasundaram","doi":"10.1287/ijoo.2021.0068","DOIUrl":null,"url":null,"abstract":"The s-clubs model cohesive social subgroups as vertex subsets that induce subgraphs of diameter at most s. In defender-attacker settings, for low values of s, they can represent tightly knit communities, whose operation is undesirable for the defender. For instance, in online social networks, large communities of malicious accounts can effectively propagate undesirable rumors. In this article, we consider a defender that can disrupt vertices of the adversarial network to minimize its threat, which leads us to consider a maximum s-club interdiction problem, where interdiction is penalized in the objective function. Using a new notion of H-heredity in s-clubs, we provide a mixed-integer linear programming formulation for this problem that uses far fewer constraints than the formulation based on standard techniques. We show that the linear programming relaxation of this formulation has no redundant constraints and identify facets of the convex hull of integral feasible solutions under special conditions. We further relate H-heredity to latency-s-connected dominating sets and design a decomposition branch-and-cut algorithm for the problem. Our implementation solves benchmark instances with more than 10,000 vertices in a matter of minutes and is orders of magnitude faster than algorithms based on the standard formulation.","PeriodicalId":73382,"journal":{"name":"INFORMS journal on optimization","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Interdicting Low-Diameter Cohesive Subgroups in Large-Scale Social Networks\",\"authors\":\"Niloufar Daemi, J. S. Borrero, Balabhaskar Balasundaram\",\"doi\":\"10.1287/ijoo.2021.0068\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The s-clubs model cohesive social subgroups as vertex subsets that induce subgraphs of diameter at most s. In defender-attacker settings, for low values of s, they can represent tightly knit communities, whose operation is undesirable for the defender. For instance, in online social networks, large communities of malicious accounts can effectively propagate undesirable rumors. In this article, we consider a defender that can disrupt vertices of the adversarial network to minimize its threat, which leads us to consider a maximum s-club interdiction problem, where interdiction is penalized in the objective function. Using a new notion of H-heredity in s-clubs, we provide a mixed-integer linear programming formulation for this problem that uses far fewer constraints than the formulation based on standard techniques. We show that the linear programming relaxation of this formulation has no redundant constraints and identify facets of the convex hull of integral feasible solutions under special conditions. We further relate H-heredity to latency-s-connected dominating sets and design a decomposition branch-and-cut algorithm for the problem. Our implementation solves benchmark instances with more than 10,000 vertices in a matter of minutes and is orders of magnitude faster than algorithms based on the standard formulation.\",\"PeriodicalId\":73382,\"journal\":{\"name\":\"INFORMS journal on optimization\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"INFORMS journal on optimization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1287/ijoo.2021.0068\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"INFORMS journal on optimization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1287/ijoo.2021.0068","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

s-clubs将有凝聚力的社会子群体建模为顶点子集,这些顶点子集诱导的子图的直径最多为s。在防御者-攻击者的设置中,对于较低的s值,它们可以表示紧密结合的社区,其操作对防御者来说是不希望的。例如,在在线社交网络中,恶意帐户的大型社区可以有效地传播不良谣言。在本文中,我们考虑一个可以破坏对抗网络顶点以最小化其威胁的防御者,这导致我们考虑一个最大s俱乐部拦截问题,其中拦截在目标函数中受到惩罚。利用s俱乐部中h遗传的新概念,我们提供了一个混合整数线性规划公式,该公式使用的约束比基于标准技术的公式少得多。我们证明了该公式的线性规划松弛没有冗余约束,并在特殊条件下识别了积分可行解的凸壳面。我们进一步将h-遗传与延迟-s-连通控制集联系起来,并设计了一个分解的分支切算法。我们的实现在几分钟内解决了具有超过10,000个顶点的基准实例,并且比基于标准公式的算法快了几个数量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Interdicting Low-Diameter Cohesive Subgroups in Large-Scale Social Networks
The s-clubs model cohesive social subgroups as vertex subsets that induce subgraphs of diameter at most s. In defender-attacker settings, for low values of s, they can represent tightly knit communities, whose operation is undesirable for the defender. For instance, in online social networks, large communities of malicious accounts can effectively propagate undesirable rumors. In this article, we consider a defender that can disrupt vertices of the adversarial network to minimize its threat, which leads us to consider a maximum s-club interdiction problem, where interdiction is penalized in the objective function. Using a new notion of H-heredity in s-clubs, we provide a mixed-integer linear programming formulation for this problem that uses far fewer constraints than the formulation based on standard techniques. We show that the linear programming relaxation of this formulation has no redundant constraints and identify facets of the convex hull of integral feasible solutions under special conditions. We further relate H-heredity to latency-s-connected dominating sets and design a decomposition branch-and-cut algorithm for the problem. Our implementation solves benchmark instances with more than 10,000 vertices in a matter of minutes and is orders of magnitude faster than algorithms based on the standard formulation.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Stochastic Inexact Sequential Quadratic Optimization Algorithm for Nonlinear Equality-Constrained Optimization Scenario-Based Robust Optimization for Two-Stage Decision Making Under Binary Uncertainty On the Hardness of Learning from Censored and Nonstationary Demand Temporal Bin Packing with Half-Capacity Jobs Editorial Board
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1