A new algorithm for positive influence maximization in signed networks

Ju, Weijia; Chen, Ling; Li, Bin; Liu, Wei; Jun, Shen; Wang, Yuwei

doi:10.1016/j.ins.2019.10.061

Cited by 61 publications

(18 citation statements)

References 23 publications

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“…These constraints try to make all the nodes that are active in each stage also active at last stage. Constraints ( 10) and (11) indicate that if a node is active or inactive at both t and t + 1 in scenario s , its outgoing links should become inactive. These constraints prevent against unreasonable activation of links by limiting the period of time that they can activate others.…”

Section: Parameters a Ijsmentioning

confidence: 99%

“…In this problem, there exists a social agent who wants to diffuse something (such as a piece of information about advantages of a good) by way of existing social ties in a network [8]. Influence maximization is the problem of selecting a small set of seed nodes in a social network, such that their overall influence on other nodes in the network is maximized [9][10][11]. The selection of a minimal set of seed nodes is…”

mentioning

confidence: 99%

“…Due to the probabilistic nature of the problem which is related to "social tie", "mathematical models of processes on social networks", "human behavior", "incompleteness of observational data" and "the model parameter" [11,16,17], it is important to provide a solution which is capable to be robust against any realization of the probabilistic uncertainty. In the other words, the proposed solution should be immunized against uncertainty.…”

mentioning

confidence: 99%

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A robust optimization model for influence maximization in social networks with heterogeneous nodes

2021

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Influence maximization is the problem of trying to maximize the number of influenced nodes by selecting optimal seed nodes, given that influencing these nodes is costly. Due to the probabilistic nature of the problem, existing approaches deal with the concept of the expected number of nodes. In the current research, a scenario-based robust optimization approach is taken to finding the most influential nodes. The proposed robust optimization model maximizes the number of infected nodes in the last step of diffusion while minimizing the number of seed nodes. Nodes, however, are treated as heterogeneous with regard to their propensity to pass messages along; or as having varying activation thresholds. Experiments are performed on a real text-messaging social network. The model developed here significantly outperforms some of the well-known existing heuristic approaches which are proposed in previous works.

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Section: Parameters a Ijsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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A robust optimization model for influence maximization in social networks with heterogeneous nodes

2021

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“…To summarize, most role detection studies focus on (i) estimating users' importance by ranking them based on their capability to spread information; and (ii) finding a minimal set of users to maximize information spread in terms of reach size, often referred to as the Influence Maximization problem [160][161][162][163]. The spread of information in OSNs that leads to influence is discussed in detail in the following section with an emphasis on user roles, where we review approaches and models of information spread in OSNs, discuss their shortcomings, indicate open questions, and summarize them in a taxonomy (Figure 2).…”

Section: Role Discovery Modelsmentioning

confidence: 99%

Role-Aware Information Spread in Online Social Networks

Bartal

Jagodnik

2021

Entropy

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Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information. However, they neglected to analyze non-viral information with low reach size that can also spread globally beyond OSN edges (links) via non-neighbors through, for example, pushed information via content recommendation algorithms. Previous reviews have also not fully considered user roles in the spread of information. To address these gaps, we: (i) provide a comprehensive survey of the latest studies on role-aware information spread in OSNs, also addressing the different temporal spreading patterns of viral and non-viral information; (ii) survey modeling approaches that consider structural, non-structural, and hybrid features, and provide a taxonomy of these approaches; (iii) review software platforms for the analysis and visualization of role-aware information spread in OSNs; and (iv) describe how information spread models enable useful applications in OSNs such as detecting influential users. We conclude by highlighting future research directions for studying information spread in OSNs, accounting for dynamic user roles.

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“…In the experiment, the influence propagation of the EN-IM algorithm was tested and compared with that of five other algorithms, Random, MaxDegree [40], Effective Degree [14], Greedy [41], MIA-N [42]and PIMSN [43].…”

Section: A Data Sets and The Comparison Algorithmsmentioning

confidence: 99%

Influence Maximization in Signed Networks by Enhancing the Negative Influence

Dai

2021

IEEE Access

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With the advancement of science and technology, research on influence maximization in networks has become a hot spot. In social networks, there is not only a positive relationship among nodes but also a negative impact, and the negative impact often plays a greater role than the positive impact, as observed for shopping websites or online votes. This paper proposes a method based on an independent cascade model by emphasizing the negative impact in symbolic networks to solve the problem of influence maximization. First, an algorithm that is based on an independent path and that emphasizes negative influence is designed to obtain the probability among nodes. Based on the activation probability, an algorithm is proposed to identify nodes that could have the greatest impact on the influence increment from the seeds. Finally, the seed set is confirmed based on the influence in the corresponding symbol network. In the experiment performed on real-world network data, the result indicate that the proposed algorithm causes more substantial influence propagation than do other algorithms.

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A new algorithm for positive influence maximization in signed networks

Cited by 61 publications

References 23 publications

A robust optimization model for influence maximization in social networks with heterogeneous nodes

A robust optimization model for influence maximization in social networks with heterogeneous nodes

Role-Aware Information Spread in Online Social Networks

Influence Maximization in Signed Networks by Enhancing the Negative Influence

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