Efficient and effective influence maximization in large-scale social networks via two frameworks

Yuan, Jinliang; Zhang, Ruisheng; Tang, Jianxin; Hu, Rongjing; Wang, Zepeng; Li, Huan

doi:10.1016/j.physa.2019.04.202

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…In general, both IVI and CELF++ seed selection methods performed poorly with the large, national‐scale Sustainable Rivers Program network under the LT model, and with the small, regional‐scale Red River network under the IC model. Because these heuristic estimates of the influence of each node are designed to be used on large‐scale networks, they aim to provide a result in a reasonable amount of time at the sacrifice of a sub‐optimal solution (Arora et al, 2017; Goyal et al, 2011; Yuan et al, 2019). Our results suggest a brute‐force MC simulation approach identified the most influential seed nodes that resulted in the highest adoption outcomes more effectively than IVI and CELF++ that require additional computational time.…”

Section: Discussionmentioning

confidence: 99%

Maximizing the spread of conservation initiatives in social networks

Wineland

Neeson

2022

Conservat Sci and Prac

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Conservation programs and policies can preserve biodiversity and boost ecosystem services, but only when widely adopted. While thousands of conservation initiatives exist globally, most fail to spread beyond a few initial adopters. Here, we use network science to (1) determine the topology and structure of two networks of conservation actors (one regional, one national), (2) identify influential individuals in those networks, and (3) test whether the adoption of a conservation initiative by influential individuals could increase the spread of that initiative across the network. We find that initial adoption by influential individuals results in sharp improvements in the total number of adopters of a conservation initiative network-wide, particularly when a linear threshold diffusion model is used. Under an independent cascade diffusion model, the benefits of targeting influencers are smaller but still substantial. These benefits occurred in both networks despite very different network structures: the regional network resembles a random network comprised mostly of state agencies and local entities, while the national network has a scale-free structure with highly influential hubs of federal agency and NGO entities. Given that many conservation programs fail to reach critical mass, our findings highlight the importance of strategically targeting influential individuals to boost the spread of conservation initiatives.

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Section: Discussionmentioning

confidence: 99%

Maximizing the spread of conservation initiatives in social networks

Wineland

Neeson

2022

Conservat Sci and Prac

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“…Some typical literature is as follows: Pallis ( 12 ) deleted k edges from the original network to diffuse rumors as little as possible, and explained which edge should be deleted depended on the eigenvalues of the network adjacency matrix. Yuan et al ( 13 ) proposed a fine-grained heuristic algorithm to solve the rumor propagation minimization problem. The experiment showed that the heuristics based on betweenness and out-degree were orders of magnitude faster than the greedy algorithm in terms of running time.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Rumor Diffusion Process With the Consideration of Individual Heterogeneity: Take the Imported Food Safety Issue as an Example During the COVID-19 Pandemic

Chen

Rong

Yang

et al. 2022

Front. Public Health

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At present, rumors appear frequently in social platforms. The rumor diffusion will cause a great impact on the network order and the stability of the society. So it's necessary to study the diffusion process and develop the rumor control strategies. This article integrates three heterogeneous factors into the SEIR model and designs an individual state transition mode at first. Secondly, based on the influencing factors such as the trust degree among individuals, an individual information interaction mode is constructed. Finally, an improved SEIR model named SEIR-OM model is established, and the diffusion process of rumors are simulated and analyzed. The results show that: (1) when the average value of the interest correlation is greater, the information content deviation is lower, but the rumor diffusion range will be wider. (2) The increase of the average network degree intensifies influence of rumors, but its impact on the diffusion has a peak. (3) Adopting strategies in advance can effectively reduce the influence of rumors. In addition, the government should enforce rumor-refuting strategies right after the event. Also, the number of rumor-refuting individuals must be paid attention to. Finally, the article verifies the rationality and effectiveness of the SEIR-OM model through the real case.

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“…To be specific, instead of selecting the most influential node at each round, the within-round greedy selection searching a set of seed nodes for each iteration. Yuan et al [40] improved the greedy algorithm by using a two-stage framework, in which finding seed candidates and filtering connected nodes before the seed selection. Ding et al [6] raised three greedy algorithms based on a proposed influence cascade model which considers the activation probability of potential influencers.…”

Section: Related Workmentioning

confidence: 99%

ABEM: An Adaptive Agent-based Evolutionary Approach for Mining Influencers in Online Social Networks

Li¹,

Hu²,

Wu³

et al. 2021

Preprint

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A key step in influence maximization in online social networks is the identification of a small number of users, known as influencers, who are able to spread influence quickly and widely to other users. The evolving nature of the topological structure of these networks makes it difficult to locate and identify these influencers. In this paper, we propose an adaptive agent-based evolutionary approach to address this problem in the context of both static and dynamic networks. This approach is shown to be able to adapt the solution as the network evolves. It is also applicable to large-scale networks due to its distributed framework. Evaluation of our approach is performed by using both synthetic networks and real-world datasets. Experimental results demonstrate that the proposed approach outperforms state-of-the-art seeding algorithms in terms of maximizing influence.

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Efficient and effective influence maximization in large-scale social networks via two frameworks

Cited by 7 publications

References 29 publications

Maximizing the spread of conservation initiatives in social networks

Maximizing the spread of conservation initiatives in social networks

Modeling Rumor Diffusion Process With the Consideration of Individual Heterogeneity: Take the Imported Food Safety Issue as an Example During the COVID-19 Pandemic

ABEM: An Adaptive Agent-based Evolutionary Approach for Mining Influencers in Online Social Networks

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