Finding the Influential Overlap Nodes in Communities

Walia, Rhythm; Sarna, Geetika; Bhatia, M. P. S.

doi:10.1109/cict.2015.93

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…In fact, a considerable number of nodes in social networks can be overlapping ones (i.e. the node belonging to multiple communities), which are more likely to spread the influence in social networks [36,45]. In other words, the existing community-based algorithms ignore fully utilising the influential overlapping nodes in the network.…”

Section: Related Workmentioning

confidence: 99%

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Overlapping community‐based particle swarm optimization algorithm for influence maximization in social networks

Zhang

Liu

Yang

et al. 2023

CAAI Trans on Intel Tech

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Influence maximization, whose aim is to maximise the expected number of influenced nodes by selecting a seed set of k influential nodes from a social network, has many applications such as goods advertising and rumour suppression. Among the existing influence maximization methods, the community‐based ones can achieve a good balance between effectiveness and efficiency. However, this kind of algorithm usually utilise the network community structures by viewing each node as a non‐overlapping node. In fact, many nodes in social networks are overlapping ones, which play more important role in influence spreading. To this end, an overlapping community‐based particle swarm optimization algorithm named OCPSO for influence maximization in social networks, which can make full use of overlapping nodes, non‐overlapping nodes, and their interactive information is proposed. Specifically, an overlapping community detection algorithm is used to obtain the information of overlapping community structures, based on which three novel evolutionary strategies, such as initialisation, mutation, and local search are designed in OCPSO for better finding influential nodes. Experimental results in terms of influence spread and running time on nine real‐world social networks demonstrate that the proposed OCPSO is competitive and promising comparing to several state‐of‐the‐arts (e.g. CGA, CMA‐IM, CIM, CDH‐SHRINK, CNCG, and CFIN).

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Section: Related Workmentioning

confidence: 99%

“…Obviously, most of authors in this collaboration network usually belong to multiple communities such as Data mining and Deep learning. In fact, the nodes belonging to multiple social communities are more likely to influence more people in social networks [36].…”

Section: Introductionmentioning

confidence: 99%