Influence Maximization in social networks using discretized Harris’ Hawks Optimization algorithm

Khatri, Inder; Choudhry, Arjun; Rao, Aryaman; Tyagi, Aryan; Vishwakarma, Dinesh Kumar; Prasad, Mukesh

doi:10.1016/j.asoc.2023.111037

Cited by 3 publications

(1 citation statement)

References 66 publications

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“…Discretization: This process involves the conversion of traditional MH algorithms into their discrete forms (Zareie, Sheikhahmadi, and Jalili 2020; Khatri et al 2023). The solution space of the IM is a set of discrete nodes, each with…”

Section: Proposed Methodologymentioning

confidence: 99%

DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)

Rao,

Singh,

Vishwakarma

et al. 2024

AAAI

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Influence Maximization is the task of selecting optimal nodes maximising the influence spread in social networks. This study proposes a Discretized Quantum-based Salp Swarm Algorithm (DQSSA) for optimizing influence diffusion in social networks. By discretizing meta-heuristic algorithms and infusing them with quantum-inspired enhancements, we address issues like premature convergence and low efficacy. The proposed method, guided by quantum principles, offers a promising solution for Influence Maximisation. Experiments on four real-world datasets reveal DQSSA's superior performance as compared to established cutting-edge algorithms.

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Section: Proposed Methodologymentioning

confidence: 99%

DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)

Rao,

Singh,

Vishwakarma

et al. 2024

AAAI

View full text Add to dashboard Cite

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Integrated improved Harris hawks optimization for global and engineering optimization

Ouyang,

Liao,

Zhu

et al. 2024

Sci Rep

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The original Harris hawks optimization (HHO) algorithm has the problems of unstable optimization effect and easy to fall into stagnation. However, most of the improved HHO algorithms can not effectively improve the ability of the algorithm to jump out of the local optimum. In this regard, an integrated improved HHO (IIHHO) algorithm is proposed. Firstly, the linear transformation escape energy used by the original HHO algorithm is relatively simple and lacks the escape law of the prey in the actual nature. Therefore, intermittent energy regulator is introduced to adjust the energy of Harris hawks, which is conducive to improving the local search ability of the algorithm while restoring the prey's rest mechanism; Secondly, to adjust the uncertainty of random vector, a more regular vector change mechanism is used instead, and the attenuation vector is obtained by modifying the composite function. Finally, the search scope of Levy flight is further clarified, which is conducive to the algorithm jumping out of the local optimum. Finally, in order to modify the calculation limitations caused by the fixed step size, Cardano formula function is introduced to adjust the step size setting and improve the accuracy of the algorithm. First, the performance of IIHHO algorithm is analyzed on the Computational Experimental Competition 2013 (CEC 2013) function test set and compared with seven improved evolutionary algorithms, and the convergence value of the iterative curve obtained is better than most of the improved algorithms, verifying the effectiveness of the proposed IIHHO algorithm. Second, the IIHHO is compared with another three state of the art (SOTA) algorithms with the Computational Experimental Competition 2022 (CEC 2022) function test set, the experiments show that the proposed IIHHO algorithm still has a strong ability to search for the optimal value. Third, IIHHO algorithm is applied in two different engineering experiments. The calculation results of minimum cost prove that IIHHO algorithm has certain advantages in dealing with the problem of search space. All these demonstrate that the proposed IIHHO is promising for numeric optimization and engineering applications.

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Context propagation based influence maximization model for dynamic link prediction

Shelke,

Jadhav

2024

IDT

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Influence maximization (IM) in dynamic social networks is an optimization problem to analyze the changes in social networks for different periods. However, the existing IM methods ignore the context propagation of interaction behaviors among users. Hence, context-based IM in multiplex networks is proposed here. Initially, multiplex networks along with their contextual data are taken as input. Community detection is performed for the network using the Wilcoxon Hypothesized K-Means (WH-KMA) algorithm. From the detected communities, the homogeneous network is used for extracting network topological features, and the heterogeneous networks are used for influence path analysis based on which the node connections are weighted. Then, the influence-path-based features along with contextual features are extracted. These extracted features are given for the link prediction model using the Parametric Probability Theory-based Long Short-Term Memory (PPT-LSTM) model. Finally, from the network graph, the most influencing nodes are identified using the Linear Scaling based Clique (LS-Clique) detection algorithm. The experimental outcomes reveal that the proposed model achieves an enhanced performance.

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Influence Maximization in social networks using discretized Harris’ Hawks Optimization algorithm

Cited by 3 publications

References 66 publications

DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)

DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)

Integrated improved Harris hawks optimization for global and engineering optimization

Context propagation based influence maximization model for dynamic link prediction

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