Influence maximization based on community structure and second-hop neighborhoods

Cheng, Jianjun; Yang, Ke; Yang, Zeyi; Zhang, Handong; Zhang, Wenbo; Chen, Xiaoyun

doi:10.1007/s10489-021-02880-8

Cited by 6 publications

(2 citation statements)

References 55 publications

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“…Further, most diffusion models only focus on the topological structure of users in OSN 22 , 25 , 38 . However, the social relationships between users and the behavior of user interactions can change with time because an activity performed by users or a behavior adopted by users may be influenced by the type and amount of behavior exhibited by their friends, acquaintances, or neighbors, and each user behaves differently over time.…”

Section: Related Workmentioning

confidence: 99%

A new stochastic diffusion model for influence maximization in social networks

Rezvanian

Vahidipour

Meybodi

2023

Sci Rep

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Most current studies on information diffusion in online social networks focus on the deterministic aspects of social networks. However, the behavioral parameters of online social networks are uncertain, unpredictable, and time-varying. Thus, deterministic graphs for modeling information diffusion in online social networks are too restrictive to solve most real network problems, such as influence maximization. Recently, stochastic graphs have been proposed as a graph model for social network applications where the weights associated with links in the stochastic graph are random variables. In this paper, we first propose a diffusion model based on a stochastic graph, in which influence probabilities associated with its links are unknown random variables. Then we develop an approach using the set of learning automata residing in the proposed diffusion model to estimate the influence probabilities by sampling from the links of the stochastic graph. Numerical simulations conducted on real and artificial stochastic networks demonstrate the effectiveness of the proposed stochastic diffusion model for influence maximization.

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Section: Related Workmentioning

confidence: 99%

A new stochastic diffusion model for influence maximization in social networks

Rezvanian

Vahidipour

Meybodi

2023

Sci Rep

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“…Besides the above greedy or heuristic algorithms, community-based algorithms [22][23][24][25][26][27][28][29][30][31][32][33][34] have also shown their promising performance in solving the social influence maximization problem, which can achieve a good balance between effectiveness and efficiency by utilising the attributes of community structures. These community-based algorithms usually consist of the following three stages: (1) detecting community structures by using a community detection algorithm; (2) utilising the detected community structures to generate candidate set of nodes; (3) selecting seed nodes from the obtained candidate set.…”

Section: Introductionmentioning

confidence: 99%

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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