Maximizing Contrasting Opinions in Signed Social Networks

Rawal, Kaivalya; Khan, Arijit

doi:10.1109/bigdata47090.2019.9005619

Cited by 7 publications

(2 citation statements)

References 33 publications

(56 reference statements)

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“…Rawal and Khan [117] formulated a method that employs the voter model for simulating the opinion diffusion of two groups with contradicting views. It also identifies the influential seed nodes that maximize the diffusion.…”

Section: 1) Voter Model Analysis Techniquementioning

confidence: 99%

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Taha

2022

IEEE Access

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Identifying the influential and spreader nodes in complex networks solves many types of complex scientific problems. In social networks, identifying the influential individuals can be useful for structuring techniques that accelerate or hinder information propagation. Each node in the network has unique characteristics that reflect its importance. These characteristics are used by researchers to design many different centrality algorithms. Unfortunately, current survey papers categorize these algorithms into broad classes and do not draw distinguishable boundaries among the specific techniques adopted by them. This can result in misclassifying unrelated algorithms into the same analysis category. To overcome this, we introduce a methodology-based taxonomy for classifying the algorithms that identify top-k influential spreaders into hierarchically nested, specific, and fine-grained categories. We survey 184 papers and discuss their algorithms, which fall under 26 specific techniques. Our methodological taxonomy classifies the algorithms hierarchically into the following manner: Analysis type analysis scope analysis approach analysis category analysis sub-category analysis specific technique. We introduce in this paper a comprehensive survey, review, and experimental evaluation of the recent and state-of-the-art algorithms that identify the top-k and influential spreader nodes in social networks.

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Section: 1) Voter Model Analysis Techniquementioning

confidence: 99%

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Taha

2022

IEEE Access

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“…[90] proposes strategies for manipulating users' opinions with the voter model. Opinion maximization with the voter model is considered in [175,173,137]. Conformity, an opposite notion of stubbornness (used in the FJ model), measures the likelihood of a user adopting the opinions of his/her neighbors.…”

Section: Opinion Manipulationmentioning

confidence: 99%

Efficient optimization of network metrics in large uncertain graphs

Saha¹

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They gave me useful feedback and suggestions that helped me in many aspects of my research and to develop new research skills. Without their guidance, the achievements of my research work would never have been possible.Next, I would like to express my heartfelt gratitude to all my collaborators, namely Prof. Laks V.S. Lakshmanan, Dr. Francesco Bonchi, Dr. Yllka Velaj, Dr. Xiangyu Ke and Ruben Brokkelkamp, for their valuable contributions in the projects in which we have worked together. I also thank my friends and colleagues in my laboratory (DMAL) for their warm companionship and enlightening discussions. They include (but are not limited to) Dr.

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Maximizing Contrasting Opinions in Signed Social Networks

Rawal

Khan

2019

2019 IEEE International Conference on Big Data (Big Data)

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The classic influence maximization problem finds a limited number of influential seed users in a social network such that the expected number of influenced users in the network, following an influence cascade model, is maximized. The problem has been studied in different settings, with further generalization of the graph structure, e.g., edge weights and polarities, target user categories, etc. In this paper, we introduce a unique influence diffusion scenario involving a population that split into two distinct groups, with opposing views. We aim at finding the top-k influential seed nodes so to simultaneously maximize the adoption of two distinct, antithetical opinions in the two groups, respectively. Efficiently finding such influential users is essential in a wide range of applications such as increasing voter engagement and turnout, steering public debates and discussions on societal issues with contentious opinions. We formulate this novel problem with the voter model to simulate opinion diffusion and dynamics, and then design a linear-time and exact algorithm COSiNeMax, while also investigating the long-term opinion characteristics in the network. Our experiments with several real-world datasets demonstrate the effectiveness and efficiency of the proposed algorithm, compared to various baselines.

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Maximizing Contrasting Opinions in Signed Social Networks

Cited by 7 publications

References 33 publications

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Efficient optimization of network metrics in large uncertain graphs

Maximizing Contrasting Opinions in Signed Social Networks

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