Influence Maximization in Independent Cascade Networks Based on Activation Probability Computation

Yang, Wenjing; Brenner, Léonardo; Giua, Alessandro

doi:10.1109/access.2019.2894073

Cited by 29 publications

(14 citation statements)

References 24 publications

(38 reference statements)

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“…Then it is possible to compute the activation probability π j ({i}) of each node j ∈ V, and select the S nodes that gave the higher influence spread σ({i}) = j∈V π j ({i}). The formal definition of the SelectTopS algorithm is reported in Algorithm 1 [28].…”

Section: A Selecttops Algorithmmentioning

confidence: 99%

On Fast and Effective Influence Spread Maximization in Social Networks

Scarabaggio¹,

Carli²,

Dotoli³

2022

Preprint

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The main characteristic of social networks is their ability to quickly spread information between a large group of people. This phenomenon is generated by the social influence that individuals induce on each other.<br>The widespread use of online social networks (e.g., Facebook) increases researchers' interest in how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network).<br>Due to its practical importance in various applications (e.g., viral marketing, target advertisement, personalized recommendation), such a problem has been studied in several variants. Different solution methodologies have been proposed. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. <br>In this context, based on the well-known independent cascade and the linear threshold models of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. Application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, comparison with the best performing state of the art algorithms demonstrates that in large scale scenarios, the proposed approach shows higher performance in terms of influence spread and running time.

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Section: A Selecttops Algorithmmentioning

confidence: 99%

On Fast and Effective Influence Spread Maximization in Social Networks

Scarabaggio¹,

Carli²,

Dotoli³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The authors then presented a greedy algorithm to solve the problem that can achieve a good sub-optimal solution. Following this work, improved greedy algorithms [8,9,13,24], community based approaches [7,23,36], new centrality measures [35,37] and efficient heuristics [15,25,31,45] have been proposed to solve the problem aiming to balance the time complexity of algorithms and the influence propagation and trying to make them scalable to large datasets. In recent years, researchers have studied the influence maximization problem in more complex networks by taking the heterogeneity of individual relationships [18] or multiplexity [42] into consideration.…”

Section: Related Workmentioning

confidence: 99%

Containment of rumor spread in complex social networks

Lan

Giua

2020

Information Sciences

Self Cite

144

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Rumors can propagate at great speed through social networks and produce significant damages. In order to control rumor propagation, spreading correct information to counterbalance the effect of the rumor seems more appropriate than simply blocking rumors by censorship or network disruption. In this paper, a competitive diffusion model, namely Linear Threshold model with One Direction state Transition (LT1DT), is proposed for modeling competitive information propagation of two different types in a same network. The problem of minimizing rumor spread in social networks is explored and a novel heuristic based on diffusion dynamics is proposed to solve this problem under the LT1DT. Experimental analysis on four different networks shows that the novel heuristic outperforms pagerank centrality. By seeding correct information in the proximity of rumor seeds, the novel heuristic performs as well as the greedy approach in scale-free and small-world networks but runs three orders of magnitude faster than the greedy approach.

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“…Yang et al [40], [41] performs the activity profit maximization in social networks; whereas, Tejaswi et al [42] maximize profit by increasing product adoption. Again, Du et al [43] maximize profit for multiple products whereas, Nguyen et al [44] maximize profit in multiple social networks.…”

Section: Profit Maximization Modelsmentioning

confidence: 99%

CM2D: Cost Minimization Under the Competitive Market in Dynamic Social Networks

Talukder

Hong

2020

IEEE Access

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The Reverse Influence Maximization (RIM) model deals with the viral marketing cost minimization in social networks. On the other hand, the Influence maximization (IM) technique finds the small number of influential users that maximize the viral marketing profit. Here, the profit is defined by the maximum number of nodes that can be influenced by seed users when they are initially activated. On the other hand, the cost is measured by the minimum number of nodes required to activate all the seed nodes. However, most of the existing studies focus on profit maximization without considering the seeding cost. Moreover, the most profitable node may not always be a cost-effective one. Thus, in this research, we introduce an epidemic Cost Minimization model under the Competitive Market in Dynamic social networks (CMCMD, i.e., CM2D). In the CM2D model, we modify the Susceptible-Infected-Recovered (SIR) technique as the Reverse SIR (RSIR) model to employ in the node activation process. The proposed CM2D model also uses the greedy Set Cover approximation technique to optimize the seeding cost. Moreover, the CM2D model tackles the challenging issues of the RIM problem more efficiently than the existing models. Finally, we simulate our model using datasets of two synthesized and four real datasets, and the results show that the CM2D model outperforms the existing models.

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Influence Maximization in Independent Cascade Networks Based on Activation Probability Computation

Cited by 29 publications

References 24 publications

On Fast and Effective Influence Spread Maximization in Social Networks

On Fast and Effective Influence Spread Maximization in Social Networks

Containment of rumor spread in complex social networks

CM2D: Cost Minimization Under the Competitive Market in Dynamic Social Networks

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