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influence blocking maximization on networks: Models, methods and applications

PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS 2022年 976卷 1-54页

作者： Chen, Bo-Lun Jiang, Wen-Xin Chen, Yi-Xin Chen, Ling Wang, Rui-Jie Han, Shuai Lin, Jian-Hong Zhang, Yi-Cheng Huaiyin Inst Technol Fac Comp & Software Engn Huaian 233003 Peoples R China Univ Fribourg Dept Phys CH-1700 Fribourg Switzerland Univ Zurich Swiss Ctr Data & Network Sci CH-8050 Zurich Switzerland Washington Univ Dept Comp Sci St Louis MO 63130 USA Yangzhou Univ Coll Informat Engn Yangzhou 225127 Jiangsu Peoples R China Swiss Fed Inst Technol Dept Managem Technol & Econ CH-8092 Zurich Switzerland

Due to the continuous emergence of various social and trade networks, network influence analysis has aroused great interest of the researchers. Based on different influence propagation models, many new models and methods for influence maximization on networks have been proposed. As an extension and expansion of the traditional influence maximization problem, influence blocking maximization has become a hotspot of research, and has been widely applied in many areas such as physics, computer science and epidemiology. In recent years, various methods for influence blocking maximization problem have been reported. However, we still lack a comprehensive review to systematically analyze the methodological and theoretical advances in influence blocking maximization problem from the aspects of social networks influence analysis. This review aims to fill this gap by providing a comprehensive survey and analysis of the theory and applications of influence blocking maximization. Not only it advances the theoretical understanding of the influence maximization problem, but will be a point of reference for future researches. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Complex network Information diffusion Diffusion model influence spread influence maximization influence blocking maximization

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Progressive Community Merging Cooperative Coevolution Algorithm for influence blocking maximization in Social Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第3期12卷 2093-2106页

作者： Gu, Ming Zhao, Tian-Fang Zhong, Jinghui Chen, Wei-Neng South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol State Key Lab Subtrop Bldg & Urban Sci Guangzhou 510006 Peoples R China Jinan Univ Sch Journalism & Commun Guangzhou 510610 Peoples R China

The widespread adoption of online social networks (OSNs) has facilitated social interaction and knowledge dissemination while raising concerns about extensive negative information propagation. Competitive propagation of positive and negative information can mitigate negative impacts. influence blocking maximization (IBM) identifies a set of nodes initiating positive information propagation in OSNs to maximize blocking negative influence. This paper first introduces an effective influence blocking estimator (DPADV) that replaces computationally expensive Monte Carlo simulations. DPADV can calculate the approximate diffusion value for nodes activated by negative information in any hop neighborhood. Meanwhile, to address the challenges of complexity and computational efficiency brought about by the continuous expansion of OSNs, we propose the progressive community merging cooperative coevolution (PCMCC) algorithm. PCMCC divides the search space into communities and initializes a subpopulation for each community. Each subpopulation is responsible for optimizing a community, thereby implementing a divide-and-conquer approach. To enhance collaboration among communities and global exploration, we employed a progressive community merging strategy, supplemented by multi-population evolution strategies to guide the search towards global optima. Additionally, we developed an efficient heuristic metric for evaluating node importance, which is used to design population crossover and local search in the evolutionary scheme. Experimental results on six real-world and four synthetic networks demonstrate that PCMCC exhibits competitive performance compared to state-of-the-art algorithms, achieving near-greedy performance with lower time complexity.

关键词： influence blocking maximization influence maximization cooperative coevolution algorithm cooperative coevolution algorithm social networks social networks social networks

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Time and value aware influence blocking maximization in geo-social networks

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JOURNAL OF SUPERCOMPUTING 2024年第14期80卷 21149-21178页

作者： Zhu, Wenlong Peng, Chongyuan Miao, Yu Bai, Yufan Diao, Yingchun Yang, Shuangshuang Qiqihar Univ Coll Comp & Control Engn Wenhua St Qiqihar 161006 Heilongjiang Peoples R China Qiqihar Univ Heilongjiang Key Lab Big Data Network Secur Detect Wenhua St Qiqihar 161006 Heilongjiang Peoples R China Qiqihar Univ Coll Teacher Educ Wenhua St Qiqihar 161006 Heilongjiang Peoples R China

The influence blocking maximization (IBM) problem aims to identify the most influential set of positive nodes in a social network to prevent the propagation of negative information throughout the network. However, existing studies have not considered factors such as the time delay of influence propagation and the value factor of nodes. Therefore, we first propose an influence propagation model called Time-Delayed Campaign-Oblivious Independent Cascade Model (TD-COICM), which incorporates time delay to simulate the decay of influence over time in social networks. Additionally, we present the Time and Value aware influence blocking maximization (TVIBM) problem by considering the value factor of nodes under TD-COICM. More precisely, the value of a node is determined by its social influence and geographical distance from Rumor Source (RS). We demonstrate TVIBM is NP-hard and the influence function is monotonic and submodular. To overcome the low efficiency of the greedy algorithm, we combine the technique of Reverse influence Sampling (RIS) and present the Reverse influence Sampling with Time Delay (RIS-TD) algorithm. We prove that the RIS-TD algorithm has a high probability to return a 1-1/e-& varepsilon;\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left( 1-1/e-\epsilon \right) $$\end{document} approximate guarantee. To improve the efficiency, we further present the ERIS-TD algorithm by reducing the number of samples. Experiments on three real geographical social networks further confirm that our algorithms outperform other baseline algorithms in effectiveness, and outperform RIS-based algorithms in efficiency.

关键词： Time delay influence blocking maximization Geo-social networks Reverse influence sampling

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influence blocking maximization under refutation

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SOCIAL NETWORK ANALYSIS AND MINING 2023年第1期13卷 143-143页

作者： Luo, Qi Yu, Dongxiao Wang, Dongbiao Zhang, Yafei Zheng, Yanwei Cai, Zhipeng Shandong Univ Sch Comp Sci & Technol 72 Binhai Qingdao Shandong Peoples R China City Univ Hong Kong Dept Media & Commun Tat Chee Ave Hong Kong Peoples R China Georgia State Univ Dept Comp Sci 25 Pk Pl Atlanta GA USA

In social networks, a phenomenon termed the refutation mechanism arises when certain users spontaneously counter negative information based on their knowledge and experience. To the best of our knowledge, this paper focuses on the influence blocking maximization under the refutation mechanism for the first time. Specifically, incorporating the refutation mechanism with the Competitive Independent Cascade model, we introduce the Refutation Competitive Independent Cascade model, while also considering real-time delay. Under the proposed model, we study the Joint influence blocking maximization (JIBM) problem. The objective of JIBM is to maximize the expected number of nonnegatives by finding a set of positive seeds in a network. We show that the problem is NP-hard. We present a scalable approximation algorithm, named RR-JIBM, by making a non-trivial adaptation of the generation process of reverse reachable sets. We prove that the given algorithms achieve a (1 - 1/e - epsilon)-approximation for any epsilon > 0 for JIBM problem. An improved algorithm named RR-JIBM+ is also proposed to improve the efficiency of RR-JIBM in reality. Experiments on real-world social networks show that our algorithms outperform other intuitive baselines in reducing the number of nodes influenced by negative seed nodes. Meanwhile, the RR-JIBM+ algorithm has a higher efficiency advantage than RR-JIBM on different datasets.

关键词： influence blocking maximization Refutation mechanism Competitive independent cascade model Reverse reachable set

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An influence blocking maximization Algorithm Based on Community Division in Social Networks 20th

An Influence Blocking Maximization Algorithm Based on Commun...

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20th International Conference on Intelligent Computing (ICIC)

作者： Liu, Wei Guo, Zhen Chen, Ling He, Jie Yangzhou Univ Yangzhou 225127 Jiangsu Peoples R China

ISBN: (纸本)9789819756179;9789819756186

The spread of negative influences such as rumors and misinformation in Online Social Networks (OSNs) can threaten public safety. Therefore, the influence blocking maximization (IBM) problem has received extensive attention in recent years. Although many researchers have investigated IBM problem, but there were some issues, including the imbalance between time consumption and performance in seed selection, as well as the influence overlap among selected seed nodes. In this paper, we present an IBM algorithm called IBM-CD based on community division to solve the IBM problem efficiently. This algorithm initially employs our proposed membership propagation approach to divide community centered around source nodes. Subsequently, the communities with low link strength will be merged. Additionally, we use the independent path to calculate the activation probability between node pairs, deriving the blocking effect on nodes based on this concept. Finally, we use the derived blocking effect as the metric to select positive seed nodes for influence blocking within each community. Through experimentation on real world datasets, we demonstrate that our proposed algorithm achieves faster and more effective suppression compared to existing methods.

关键词： influence blocking maximization Community Division Membership Propagation Independent Path

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Negative influence blocking maximization with uncertain sources under the independent cascade model

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INFORMATION SCIENCES 2021年 564卷 343-367页

作者： Chen, Ling Zhang, Yuliang Chen, Yixin Li, Bin Liu, Wei Yangzhou Univ Coll Informat Engn Yangzhou 225127 Jiangsu Peoples R China Washington Univ Dept Comp Sci St Louis MO 63130 USA

The propagation of negative influences, such as epidemic spreading, rumors, and false information in social networks and computer viruses, may lead to serious consequences. The issue of negative influence blocking maximization (IBM) has aroused intense interest from researchers. However, in real-world social network environments, the source of negative influence is typically unknown. In most cases, we only know the distribution of negative seeds and the probability for each node to be a negative seed. In this paper, this problem is defined as negative influence blocking maximization with an uncertain source (IBM-US), and a model is shown to approximately describe opposing effects that proliferate in the IBM-US problem. To calculate the blocking effect of the joint impact of positive and negative seed sets, a blocking function is defined, and an algorithm called IBM-Seed is used for the IBM problem in the independent cascade (IC) propagation model. A sampling-based algorithm IBM-US-SB-Seed is proposed to achieve an approximate solution for the IBM-US problem. The convergence of the IBM-US-SB-Seed algorithm is proven, and the convergence speed and the number of samples are analyzed. An extended sampling-based algorithm IBM-US-Seed for the IBM-US problem is shown to achieve a proper balance between result precision and computation time. The proposed algorithms are tested on real datasets, and the experimental results demonstrate that the proposed algorithms can yield higher quality results than other similar methods. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Social network Negative influence Sampling influence blocking maximization Independent cascade model

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The generalized influence blocking maximization problem

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SOCIAL NETWORK ANALYSIS AND MINING 2021年第1期11卷 55页

作者： Erd, Fernando C. Vignatti, Andre L. Silva, Murilo V. G. da Univ Fed Parana Curitiba Parana Brazil

Given a network N and a set of nodes that are the starting point for the spread of misinformation across N and an integer k, in the influence blocking maximization problem the goal is to find k nodes in N as the starting point for a competing information (say, a correct information) across N such that the reach of the misinformation is minimized. In this paper, we deal with a generalized version of this problem that corresponds to a more realistic scenario, where different nodes have different costs and the counter strategy has a "budget" for picking nodes for a solution. Our experimental results show that the success of a given strategy varies substantially depending on the cost function in the model. In particular, we investigate the cost function implicitly used in all previous works in the field (i.e., all nodes have cost 1), and a cost function that assigns higher costs to higher-degree nodes. We show that, even though strategies that perform well in these two diverse cases are very different from each other, both correlate well with simple (but different) strategies: greedily choose high-degree nodes and choose nodes uniformly at random. Furthermore, we show properties and approximations results for the influence function in several diffusion models .

关键词： influence blocking maximization Misinformation Complex networks

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influence blocking maximization in Social Network Using Centrality Measures 5

Influence Blocking Maximization in Social Network Using Cent...

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IEEE 5th Conference on Knowledge Based Engineering and Innovation (KBEI)

作者： Arazkhani, Niloofar Meybodi, Mohammad Reza Rezvanian, Alireza Amirkabir Univ Technol Tehran Polytech Dept Comp Engn & Informat Technol Tehran Iran Inst Res Fundamental Sci IPM Sch Comp Sci Tehran Iran

ISBN: (纸本)9781728108728

Online social networks play an important role as a suitable platform for information diffusion. While positive news diffusion on social network has a great impact in people's life, the negative news can also spread as fast as positive ones. To make the social network a reliable place, it is necessary to block inappropriate, unwanted and contamination diffusion. In this paper, we study the notion of competing negative and positive campaigns in a social network and address the influence blocking maximization (IBM) problem to minimize the bad effect of misinformation. IBM problem can be summarized as identifying a subset of nodes to adopt the positive influence under Multi-campaign Independent Cascade Model (MCICM) as diffusion model to minimize the number of nodes that adopt the negative influence at the end of both propagation processes. We proposed Centrality_IBM algorithm based on centrality measures for finding an appropriate candidate subset of nodes for spreading positive diffusion in order to minimizing the IBM problem. Then, we experimentally compare the performance of the proposed algorithm using some centrality measures to choose the appropriate subset of positive influential nodes. The experiments on different real datasets reveal that the closeness centrality measure outperforms the alternative centrality measures in most of the cases.

关键词： centrality measure degree centrality closeness centrality betweenness centrality influence blocking maximization social network

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Location-Based Seeds Selection for influence blocking maximization in Social Networks

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IEEE ACCESS 2019年 7卷 27272-27287页

作者： Zhu, Wenlong Yang, Wu Xuan, Shichang Man, Dapeng Wang, Wei Du, Xiaojiang Guizani, Mohsen Harbin Engn Univ Informat Secur Res Ctr Harbin 165001 Heilongjiang Peoples R China Qiqihar Univ Coll Comp & Control Engn Qiqihar 165301 Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA Qatar Univ Coll Engn Doha 2713 Qatar

influence blocking maximization (IBM) is a key problem for viral marketing in competitive social networks. Although the IBM problem has been extensively studied, existing works neglect the fact that the location information can play an important role in influence propagation. In this paper, we study the location-based seeds selection for IBM problem, which aims to find a positive seed set in a given query region to block the negative influence propagation in a given block region as much as possible. In order to overcome the low efficiency of the simulation-based greedy algorithm, we propose a heuristic algorithm IS-LSS and its improved version IS-LSS+, both of which are based on the maximum influence arborescence structure and Quadtree index, while IS-LSS+ further improves the efficiency of IS-LSS by using an upper bound method and Quadtree cell lists. The experimental results on real-world datasets demonstrate that our proposed algorithms are able to achieve matching blocking effect to the greedy algorithm as the increase in the number of positive seeds and often better than other heuristic algorithms, whereas they are four orders of magnitude faster than the greedy algorithm.

关键词： influence blocking maximization location-based competitive social networks

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A community-based algorithm for influence blocking maximization in social networks

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第3-Sup期22卷 S5587-S5602页

作者： Lv, Jiaguo Yang, Bin Yang, Zhen Zhang, Wei Zaozhuang Univ Sch Informat Sci & Engn Zaozhuang 277100 Shandong Peoples R China

With the increasing popularity of social networking sites and the convenience of information diffusion in social network, social network has been a huge platform for information diffusion and knowledge sharing. However, the incapability of the supervision over the content of networks usually leads to the threats of negative influence, which may lead to undesirable effects. influence blocking maximization (IBM) problem which aims to find a subset of nodes that need to adopt the positive influence (L) to minimize the number of nodes that adopt the negative influence (C) at the end of both propagation processes is addressed in this work. Under a well-known Campaign-Oblivious Independent Cascade Model, the objective function of IBM is submodular, and thus an approximation algorithm Greedy is obtained. Subsequently, based on the locality of influence diffusion in social networks, an efficient algorithm CB_IBM is proposed, which is based on the community structure of the network. Extensive simulations of CB_IBM, Greedy, and other baseline algorithms have been conducted on two real-world datasets, and experiments show that, in terms of the blocking effect, CB_IBM consistently matches the performance of Greedy, however, it is much faster than Greedy.

关键词： influence blocking maximization Competitive influence diffusion Community structure Social networks

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