Topic-aware Social Influence Minimization

Yao, Qingxia; Shi, Ruisheng; Zhou, Chuan; Wang, Peng; Guo, Li

doi:10.1145/2740908.2742767

Cited by 36 publications

(19 citation statements)

References 3 publications

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“…This work used betweenness, edge betweenness, degree, and closeness to block influential nodes. Similarly, Yao et al [204] solved the same problem but by blocking a limited number of nodes where the centrality metrics considered are out-degree and betweenness. Luo et al [205] proposed an algorithm that identifies a set of critical nodes to minimize disinformation in time-varying online social networks.…”

Section: ) Influence Minimizationmentioning

confidence: 99%

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A Survey on Centrality Metrics and Their Network Resilience Analysis

et al. 2021

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Centrality metrics have been studied in the network science research. They have been used in various networks, such as communication, social, biological, geographic, or contact networks under different disciplines. In particular, centrality metrics have been used in order to study and analyze targeted attack behaviors and investigated their effect on network resilience. Although a rich volume of centrality metrics has been developed from 1940s, only some centrality metrics (e.g., degree, betweenness, or cluster coefficient) have been commonly in use. This paper aims to introduce various existing centrality metrics and discusses their applicabilities in various networks. In addition, we conducted extensive simulation study in order to demonstrate and analyze the network resilience of targeted attacks using the surveyed centrality metrics under four real network topologies. We also discussed algorithmic complexity of centrality metrics surveyed in this work. Through the extensive experiments and discussions of the surveyed centrality metrics, we encourage their use in solving various computing and engineering problems in networks.

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Section: ) Influence Minimizationmentioning

confidence: 99%

“…Betweenness; out-degree; degree; closeness [203], [202], [205], [204] Behavior adoption for marketing Degree; betweenness; closeness [206], [207], [171], [210], [209], [208]…”

Section: Influence Minimizationmentioning

confidence: 99%

A Survey on Centrality Metrics and Their Network Resilience Analysis

et al. 2021

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“…The influence minimization problem reduces the propagation of rumors or disinformation by blocking nodes from a topic modelling perspective [34]. When undesirable events propagate in a social network, reduce the size of the infected volume by blocking some nodes outside the infection area.…”

Section: Related Workmentioning

confidence: 99%

Exploring Intelligent Approach of Influence Minimization Considering the Node Surveillance in Online Social Networks

Cheriyan

Sajeev

2021

Preprint

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Attractive information such as innovations, awareness campaigns, branding, and advertising help people positively. Whereas, awful information such as rumors, malicious viruses, pornography, and revenge disturb people. The negative information contributes to chaos among people; therefore, it is to be blocked and hinder from further diffusion. This has motivated us towards the study of the problem named influence minimization. As the real world network can be modeled to a multilayer network, we focus our study towards the information diffusion through a multilayer network. Each node assigns a threshold, and its variation affects the rate of influence propagation across the network. In the influence minimization problem, the energy level of each node changes that help to formulate the function that minimizes the influence propagation. By applying two reduction policies, we are able to optimize our objective of minimizing the influence towards repulsive information. In this article, we consider the user response and its surveillance in the network. Repeated experiments on real networks has helped us to validate the proposed methods.

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“…The nodes having a high degree and a high clustering coefficient get preference over the low degree, low clustered nodes in the network for spreading information [19]. Moreover, for influence minimization problem the aim is to select lazy nodes in the network, which are very silent in nature and have the capability of blocking information propagation in the network [20]. However, it has not been established whether the seed nodes identified by k-core or node degree centrality play an important role for propagation of data through the network.…”

Section: Related Workmentioning

confidence: 99%

Influence maximization in online social network using different centrality measures as seed node of information propagation

2019

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Information propagation in the network is probabilistic in nature; simultaneously, it depends on the connecting paths of the propagation. Selection of seed nodes plays an important role in determining the levels and depth of the contagion in the network. This paper presents a comparative study when seed nodes for information propagation are selected through the properties of different centrality measures in the social network. This study captures the interaction measures of nodes in the social network, selects seed nodes based on five centrality measures, i.e. degree distribution, betweenness centrality, closeness centrality, Eigenvector and PageRank, and compares the affected nodes and levels of propagation within the network. We demonstrate the performance of the different centrality measures by processing three datasets of social network: Twitter network, Bitcoin network and author collaborative network. For the propagation of the information, we use breadth-first search (BFS) and susceptible-infectious-recovered (SIR) model and a detailed comparative study is also presented for each of the seed nodes selected using aforementioned network properties. Results show that the Eigenvector centrality and PageRank centrality measures outperform other centrality measures in all test cases in terms of propagation level and affected nodes during information propagation. Both Eigenvector and PageRank network data processing required a high computational overhead. For this reason we propose a hybrid model where using k-core the network is degenerated into a smaller network and centrality nodes are extracted from the smaller network. These centrality nodes, as compared to original centrality nodes, perform almost in the same manner in terms of influence maximization when k is chosen in a rational way.

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Topic-aware Social Influence Minimization

Cited by 36 publications

References 3 publications

A Survey on Centrality Metrics and Their Network Resilience Analysis

A Survey on Centrality Metrics and Their Network Resilience Analysis

Exploring Intelligent Approach of Influence Minimization Considering the Node Surveillance in Online Social Networks

Influence maximization in online social network using different centrality measures as seed node of information propagation

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