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Stemming competitive influence spread in social networks through binary ions motion optimization

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Abstract

The rapid development of social networks has brought many conveniences, but it has also resulted in the wanton dissemination of negative information. Identifying key users in the network to block negative information in a timely and effective manner has become an urgent research task. For this purpose, this paper proposes a binary ions motion optimization algorithm to maximize the blocking of negative influence propagation under a competitive-based model. The algorithm adopts a degree-based heuristic initialization strategy by recoding search agents and blocking diffusion channels based on the negative seed location. To overcome the lack of crystal phase search ability, a crossover mechanism of anions and cations is introduced, which accelerates convergence and facilitates the discovery of optimal solution. Finally, the effectiveness of the proposed algorithm is demonstrated on real networks and synthetic networks, showing significant advancements compared to other algorithms.

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Data availability

The datasets generated during and/or analysed during the current study are available in the [43, 45] repository, [Network Repository,The KONECT Project].

Abbreviations

\(\Theta _v^{+}\) :

The positive activation threshold of node v

\(\Theta _v^{-}\) :

The negative activation threshold of node v

\(G = (V, E)\) :

A network with the node set V and the edge set E

\(IBS(P_0,N_0)\) :

The blocked negative influence

\(N^{+}(u)\) :

The active out-neighbour nodes of u

\(N^{-}(v)\) :

The active in-neighbour nodes of v

\(P_0, N_0\) :

Initial positive active seed set/negative active seed set

\(S_N\) :

The set of all negative active nodes

\(S_P\) :

The set of all positive active nodes

\(S_{P*}\) :

The selected positive seed set

\(w^+(u,v)\) :

The positive weight of edge (u, v)

\(w^-(u,v)\) :

The negative weight of edge (u, v)

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Acknowledgements

This study was supported by the Program for Synergy Innovation in the Anhui Higher Education Institutions of China (Grant no. GXXT-2021-044), the Excellent scientific research and innovation team of Anhui Provincial Education Department, China (no. 2022AH010027), Scientific Research Foundation of Education Department of Anhui Province, China (Grant nos. KJ2021A0506), Natural Science Foundation of Anhui Province, China (Grant no. 2108085MG237), Open Fund of Key Laboratory of Anhui Higher Education Institutes, China (Grant no. CS2021-02), Philosophy and Social Sciences Planning Project of Anhui Province, China (Grant no. AHSKQ2021D116) and Research Start-Up Fund for Introducing Talents from Anhui Polytechnic University (Grant no. 2021YQQ066).

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Authors and Affiliations

  1. Anhui Province Key Laboratory of Multidisciplinary Management and Control of Complex Systems, Anhui University of Technology, Ma’anshan, 243002, Anhui, People’s Republic of China

    Ping Kong, Chao Wang & Nenggang Xie

  2. Key Laboratory of Mechanics, Anhui Polytechnic University, Wuhu, 241000, Anhui, People’s Republic of China

    Chao Wang & Liangliang Ma

  3. Department of Management science and Engineering, Anhui University of Technology, Ma’anshan, 243002, Anhui, People’s Republic of China

    Nenggang Xie

  4. Department of Mechanical Engineering, Anhui University of Technology, Ma’anshan, 243002, Anhui, People’s Republic of China

    Ye Ye & Lu Wang

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Kong, P., Wang, C., Ma, L. et al. Stemming competitive influence spread in social networks through binary ions motion optimization. Int. J. Mach. Learn. & Cyber. 15, 719–737 (2024). https://doi.org/10.1007/s13042-023-01936-0

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