A new community-based algorithm based on a “peak-slope-valley” structure for influence maximization on social networks

Yang, Pingle; Zhao, Laijun; Zhi, Lu; Zhou, Lixin; Meng, Fanyuan; Qian, Ying

doi:10.1016/j.chaos.2023.113720

Cited by 5 publications

(2 citation statements)

References 53 publications

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“…To address this problem, various algorithms have been put forward from different viewpoints. [48][49][50][51][52][53] Xie et al [48] designed an adaptive degree-based heuristic algorithm to identify some influential nodes as seed spreaders, considering that nodes have more neighbors with the seed set unlikely to be selected as seeds. Yang et al [49] developed a community-based influence maximization algorithm to choose several seed spreaders, they devised a recursive clustering approach to partition entire network into communities, and the topological potential "peakslope-valley" structure was introduced to determine seeds.…”

Section: Spreading Positive Informationmentioning

confidence: 99%

CRB: A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

Dong 董,

Xu 徐,

Meng 孟

2024

Chinese Phys. B

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The virtuality and openness of online social platforms make it a hotbed for the rapid propagation of various rumors. In order to block the outbreak of rumor, one of the most effective containment measures is spreading positive information to counterbalance the diffusion of rumor. The spreading mechanism of rumors and effective suppression strategies are significant and challenging research issues. Firstly, in order to simulate the dissemination of multiple types of information, we propose a competitive linear threshold model with state transition (CLTST) to describe the spreading process of rumor and anti-rumor in the same network. Subsequently, we put forward a community-based rumor blocking algorithm (CRB) based on influence maximization theory in social networks. Its crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes, which includes community detection, selection of candidate anti-rumor seeds and generation of anti-rumor seed set. Under CLTST model, CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the performance. Experimental results show that the proposed model can better reflect the process of rumor propagation, and review the propagation mechanism of rumor and anti-rumor in online social networks. Moreover, the proposed CRB algorithm has better performance in weakening the rumor dissemination ability, which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread, sensitivity analysis, seeds distribution and running time.

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Section: Spreading Positive Informationmentioning

confidence: 99%

CRB: A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

Dong 董,

Xu 徐,

Meng 孟

2024

Chinese Phys. B

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“…The reverse influence sampling method, which uses reverse reachability to acquire substantial samples, greatly improves the efficiency of identifying the most influential nodes [25]. In addition, other algorithms are also used to solve various practical influence maximization problems, such as evolutionary algorithms [26,27] and community-based strategies [28]. However, in many real-world scenarios, obtaining full network information is generally impractical and could be extremely expensive due to privacy protection and technical limitations [12,29,30].…”

Section: Introductionmentioning

confidence: 99%

Individual-centralized seeding strategy for influence maximization in information-limited networks

Liu,

Wang,

Wang

et al. 2024

J. R. Soc. Interface.

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Peer effects can directly or indirectly rely on interaction networks to drive people to follow ideas or behaviours triggered by a few individuals, and such effects can be largely improved by targeting the so-called influential individuals. In this article, we study the current most promising seeding strategy used in field experiments, the one-hop strategy, where the underlying interaction networks are generally too impractical or prohibitively expensive to be obtained, and propose an individual-centralized seeding approach to target influential seeds in information-limited networks. The presented strategy works by reasonable follow-up questions to respondents, such as Who do you think has more connections/friends? , and constructs the seeding set by those nodes with the most nominations. In this manner, the proposed method could acquire more information about the studied interaction network from the inference of respondents without surveying additional individuals. We evaluate our strategy on networks from various experimental datasets. Results show that the obtained seeds are much more influential compared to the one-hop strategy and other methods. We also show how the proposed approach could be implemented in field studies and potentially provide better interventions in real scenarios.

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High-quality community detection in complex networks based on node influence analysis

Wang,

Zhang,

Othman Yahya

2024

Chaos, Solitons & Fractals

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A new community-based algorithm based on a “peak-slope-valley” structure for influence maximization on social networks

Cited by 5 publications

References 53 publications

CRB: A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

CRB: A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

Individual-centralized seeding strategy for influence maximization in information-limited networks

High-quality community detection in complex networks based on node influence analysis

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