TY - GEN
T1 - A Distributed Method for Negative Content Spread Minimization on Social Networks
AU - Yan, Ruidong
AU - Guo, Zhenhua
AU - Wu, Weili
AU - Fan, Baoyu
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Currently, social networks have emerged as significant platforms for individuals to share personal information and social content. However, it is important to recognize that social networks have both positive and negative aspects. To effectively address the dissemination of negative social content such as rumors and misinformation, it is crucial to implement strategies that involve immediate blocking of associated links. This paper introduces a Negative Content Spread Minimization (NCSM) problem, which aims to minimize the spread of negative content by removing a set of edges from the network. We begin by demonstrating the NP-hardness of the NCSM problem through reduction from the Knapsack Problem. Furthermore, we establish that the objective function is not submodular under the Independent Cascade model. To address, we employ a distributed method which includes community partition and influential edges selection. The advantage of this approach is to reduce computational overhead by selecting key edges in parallel in each community. To evaluate proposed algorithm, we conduct experiments using real-world datasets and compare them against existing methods. The experimental results demonstrate that our method outperforms state-of-the-art algorithms.
AB - Currently, social networks have emerged as significant platforms for individuals to share personal information and social content. However, it is important to recognize that social networks have both positive and negative aspects. To effectively address the dissemination of negative social content such as rumors and misinformation, it is crucial to implement strategies that involve immediate blocking of associated links. This paper introduces a Negative Content Spread Minimization (NCSM) problem, which aims to minimize the spread of negative content by removing a set of edges from the network. We begin by demonstrating the NP-hardness of the NCSM problem through reduction from the Knapsack Problem. Furthermore, we establish that the objective function is not submodular under the Independent Cascade model. To address, we employ a distributed method which includes community partition and influential edges selection. The advantage of this approach is to reduce computational overhead by selecting key edges in parallel in each community. To evaluate proposed algorithm, we conduct experiments using real-world datasets and compare them against existing methods. The experimental results demonstrate that our method outperforms state-of-the-art algorithms.
KW - Social network
KW - negative content spread
KW - non-submodularity
UR - http://www.scopus.com/inward/record.url?scp=85205382077&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-7798-3_14
DO - 10.1007/978-981-97-7798-3_14
M3 - Conference contribution
AN - SCOPUS:85205382077
SN - 9789819777976
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 157
EP - 169
BT - Algorithmic Aspects in Information and Management - 18th International Conference, AAIM 2024, Proceedings
A2 - Ghosh, Smita
A2 - Zhang, Zhao
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Algorithmic Aspects in Information and Management, AAIM 2024
Y2 - 21 September 2024 through 23 September 2024
ER -