Maximizing the spread of influence via the collective intelligence of discrete bat algorithm

Tang, Jianxin; Zhang, Ruisheng; Yao, Yabing; Zhao, Zhili; Wang, Ping; Li, Huan; Yuan, Jinliang

doi:10.1016/j.knosys.2018.06.013

Cited by 62 publications

(9 citation statements)

References 29 publications

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“…Du* is the number of edges for node u within the range of one-hop and two-hop. It has been proved in some literature [36] that the LIE evaluation function has good approximate results. In this way, the problem of identifying the top-k influence nodes becomes an optimization problem of the objective function LIE, that is, the optimal k nodes are selected to form the seed node-set by the maximization principle of the LIE function value.…”

Section: B Local Influence Evaluatormentioning

confidence: 99%

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Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization With Local Neighborhood Degree Centrality

et al. 2021

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The top-k influential individuals in a social network under a specific topic play an important role in reality. Identifying top-k influential nodes of a social network is still an open and deeply-felt problem. In recent years, some researchers adopt the swarm intelligence algorithm to solve such problems and obtain competitive results. There are two main algorithm models for swarm intelligence, namely Ant Colony System (ACS) and Particle Swarm Optimization (PSO). The discretized basic Particle Swarm Algorithm (DPSO) shows comparable performance in identifying top-k influential nodes of a social network. However, the performance of the DPSO algorithm is directly related to the choice of its local search strategy. The local search strategy based on greedy mechanism of the initial DPSO can easily lead to the global suboptimal solution due to the premature convergence of the algorithm. In this paper, we adopt the degree centrality based on different neighbourhoods to enhance its local search ability. Through experiments, we find that local search strategies based on different neighbourhoods have significant differences in the improvement of the algorithm's global exploration capabilities, and the enhancement of the DPSO algorithm based on the degree centrality of different neighbourhoods has a saturation effect. Finally, based on the degree centrality of the best neighbourhood with improved local search ability, we propose the DPSO_NDC algorithm. Experimental results in six real-world social networks show that the proposed algorithm outperforms the initial DPSO algorithm and other state-of-the-art algorithms in identifying the top-k influence nodes.

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Section: B Local Influence Evaluatormentioning

confidence: 99%

“…DBA [36] Discrete Bat Algorithm (DBA) is a discrete metaheuristic algorithm, which derives from the basic bat algorithm and is used to identify the most influential node sets in social networks.…”

Section: Algorithm Brief Overviewmentioning

confidence: 99%

Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization With Local Neighborhood Degree Centrality

et al. 2021

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“…Community-based algorithms tend to approximate the influence of a node within its own community to the influence on the whole network, and some smaller communities are directly ignored when selecting influential communities to select seed nodes. In addition to the methods described above, there are many other influence maximization algorithms, such as reverse influence sampling based algorithms [23]- [25], [36], swarm intelligent optimal algorithms [27]- [29], [37], Influence path based algorithms [38]- [40].…”

Section: Community-based Algorithmsmentioning

confidence: 99%

An Efficient Influence Maximization Algorithm Based on Clique in Social Networks

Zhang

Zhao

et al. 2019

IEEE Access

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Influence Maximization is to find a subset of influential nodes so that they can spread influence to the largest range in a network. The study on influence maximization is of great importance, and many solutions have been developed, including greedy algorithm which provides the provable approximate guarantee. However, greedy algorithm is very time-consuming, it is unrealistic to apply it to large-scale networks. Heuristic algorithms, which are efficient in influential nodes identifying, usually cannot provide any performance guarantee. To solve above problems, we propose an efficient influence maximization algorithm based on clique (called IMC for short). Our proposed algorithm extracts the cliques in a network and utilizes the information of clique to reduce network size and obtains candidate node set, finally, k most influential nodes are identified from the candidate set. Extensive experiments on 14 real-world networks based on independent cascade model show that our proposed algorithm outperforms state-of-the-art influence maximization algorithms, and achieves comparable influence spread to CELF with less running time.

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“…This is later addressed with an enhanced discrete particle swarm optimization (ELDPSO) algorithm, that exploits network topology around the seed nodes [29]. A discrete bat algorithm (DBA) based on DPSO's discrete coding criterion had also been explored [30] for promising results. The algorithm generates a candidate pool of nodes with potential influence according to the contribution of nodes to the network topology to accelerate convergence.…”

Section: Introductionmentioning

confidence: 99%

Identifying Influential Spreaders in Social Networks Through Discrete Moth-Flame Optimization

Wang

Ма

Wang

et al. 2021

IEEE Trans. Evol. Computat.

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Influence maximization in a social network refers to the selection of node sets that support the fastest and broadest propagation of information under a chosen transmission model. The efficient identification of such influence-maximizing groups is an active area of research with diverse practical relevance. Greedy-based methods can provide solutions of reliable accuracy, but the computational cost of the required Monte Carlo simulations renders them infeasible for large networks. Meanwhile, although network structure-based centrality methods can be efficient, they typically achieve poor recognition accuracy.Here we establish an effective influence assessment model based both on the total valuation and variance in valuation of neighbor nodes, motivated by the possibility of unreliable communication channels. We then develop a discrete moth-flame optimization method to search for influence-maximizing node sets, using local crossover and mutation evolution scheme atop the canonical moth position updates. To accelerate convergence, a search area selection scheme derived from a degree-based heuristic is used. Experimental results on five real-world social networks, comparing our proposed method against several alternatives in current literature, indicates our approach to be effective and robust in tackling the influence maximization problem.

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Maximizing the spread of influence via the collective intelligence of discrete bat algorithm

Cited by 62 publications

References 29 publications

Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization With Local Neighborhood Degree Centrality

Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization With Local Neighborhood Degree Centrality

An Efficient Influence Maximization Algorithm Based on Clique in Social Networks

Identifying Influential Spreaders in Social Networks Through Discrete Moth-Flame Optimization

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