An adaptive approach for handling two‐dimension influence maximization in social networks

Wang, Xingwei; Huang, Min; Cai, Yuliang; Zhang, Chuangchuang; Ma, Lianbo

doi:10.1002/dac.3780

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…Moreover, there are some other influence maximization methods, including the adaptive algorithms, data‐based algorithms, time‐restricted model–based algorithm, competitive model–based algorithms, and voter model–based algorithms …”

Section: Background and Related Workmentioning

confidence: 99%

An effective scheme to address influence maximization for opinion formation in social networks

Lei

Wang

Huang

et al. 2019

Trans Emerging Tel Tech

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Influence maximization for opinion formation (IMOF) is an important problem in social networks, which aims to select most influential nodes and obtains the maximal propagation of the most ideal opinions. The existing studies on the IMOF primarily concentrate on the effective selection scheme of the most influential nodes (ie, seed nodes) and the improved opinion formation models. However, there is little work describing and defining the IMOF mathematically. In this paper, we formulate the IMOF problem mathematically and propose the weighted cooperation model to compute the opinions of network nodes. To determine the most influential nodes, an effective scheme (including the score of potential nodes (SPNs) and elimination of overlapping influence (EOI)) is proposed. By using the reverse propagation process of public opinion and dynamically adjusting the influence score of each node, SPN determines the sorting of network node within the finite iterations. Moreover, to better determine the initial seed nodes, the EOI is adopted to deal with the similarity and overlapping influence among these nodes with high influence score. Finally, to evaluate the performance of SPN, the number of positive nodes with the change of seed nodes and the times of iteration by SPN is compared with three mainstream algorithms in social network data sets. Simulation results show that the proposed scheme obtains superior number of positive nodes than the comparison algorithms.

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Section: Background and Related Workmentioning

confidence: 99%

An effective scheme to address influence maximization for opinion formation in social networks

Lei

Wang

Huang

et al. 2019

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…Except the IC and LT models, Ganesh et al proposed the epidemic model [17,18], which uses the graph's topological properties to simulate the persistence of epidemics. Tzoumas et al proposed a gametheoretic model [19,20] which using the known linear threshold model to simulate the diffusion of 2-player games. Meanwhile, some greedy algorithms [21][22][23], heuristic algorithms [24,25] and their extensions [26,27] have been presented to find the most influential seed sets.…”

Section: Introductionmentioning

confidence: 99%

A New Strategy in Boosting Information Spread

Zhang

Liu

Gong

2022

Entropy

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Finding a seed set to propagate more information within a specific budget is defined as the influence maximization (IM) problem. The traditional IM model contains two cardinal aspects: (i) the influence propagation model and (ii) effective/efficient seed-seeking algorithms. However, most of models only consider one kind of node (i.e., influential nodes), ignoring the role of other nodes (e.g., boosting nodes) in the spreading process, which are irrational. Specifically, in the real-world propagation scenario, the boosting nodes always improve the spread of influence from the initial activated seeds, which is an efficient and cost-economic measure. In this paper, we consider the realistic budgeted influence maximization (RBIM) problem, which contains two kind of nodes to improve the diffusion of influence. Facing the newly modified objective function, we propose a novel B-degree discount algorithm to solve it. The novel B-degree discount algorithm which adopts the cost-economic boosting nodes to retweet the influence from the predecessor nodes can greatly reduce the cost, and performs better than other state-of-the-art algorithms in both effect and efficiency on RBIM problem solving.

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TIFIM: A Two-stage Iterative Framework for Influence Maximization in Social Networks

Wang

Lei

et al. 2019

Applied Mathematics and Computation

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An adaptive approach for handling two‐dimension influence maximization in social networks

Cited by 4 publications

References 38 publications

An effective scheme to address influence maximization for opinion formation in social networks

An effective scheme to address influence maximization for opinion formation in social networks

A New Strategy in Boosting Information Spread

TIFIM: A Two-stage Iterative Framework for Influence Maximization in Social Networks

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