Multi-diffusion Degree Centrality Measure to Maximize the Influence Spread in the Multilayer Social Networks

Gaye, Ibrahima; Mendy, Gervais; Ouya, Samuel; Diop, Idy; Seck, Diaraf

doi:10.1007/978-3-319-66742-3_6

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…In recent years, there have been successive works of maximizing influence in multilayer networks. Ibrahima Gaye et al [21] proposed a centralized measurement method called 'Multi-Diffusion Degree' to select seeds in multilayer networks to maximize the influence. Li Guoliang et al [22] used the maximum propagation path to approximate the influence between nodes and obtained several solutions of influence-maximization problem of multilayer networks.…”

Section: Influence Maximization In Multilayer Networkmentioning

confidence: 99%

Maximizing multiple influences and fair seed allocation on multilayer social networks

et al. 2020

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The dissemination of information on networks involves many important practical issues, such as the spread and containment of rumors in social networks, the spread of infectious diseases among the population, commercial propaganda and promotion, the expansion of political influence and so on. One of the most important problems is the influence-maximization problem which is to find out k most influential nodes under a certain propagate mechanism. Since the problem was proposed in 2001, many works have focused on maximizing the influence in a single network. It is a NP-hard problem and the state-of-art algorithm IMM proposed by Youze Tang et al. achieves a ratio of 63.2% of the optimum with nearly linear time complexity. In recent years, there have been some works of maximizing influence on multilayer networks, either in the situation of single or multiple influences. But most of them study seed selection strategies to maximize their own influence from the perspective of participants. In fact, the problem from the perspective of network owners is also worthy of attention. Since network participants have not had access to all information of the network for reasons such as privacy protection and corporate interests, they may have access to only part of the social network. The owners of networks can get the whole picture of the networks, and they need not only to maximize the overall influence, but also to consider allocating seeds to their customers fairly, i.e., the Fair Seed Allocation (FSA) problem. As far as we know, FSA problem has been studied on a single network, but not on multilayer networks yet. From the perspective of network owners, we propose a multiple-influence diffusion model MMIC on multilayer networks and its FSA problem. Two solutions of FSA problem are given in this paper, and we prove theoretically that our seed allocation schemes are greedy. Subsequent experiments also validate the effectiveness of our approaches.

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Section: Influence Maximization In Multilayer Networkmentioning

confidence: 99%

Maximizing multiple influences and fair seed allocation on multilayer social networks

et al. 2020

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“…In [22], Berlingerio proposed a multilevel network model to analyze the complex system of the real world and defined the multilevel network relationships. In [23], Gayel et al proposed a general framework of network quality function, which allows the community in any multilevel network to be studied. In this framework, the network is a combination of coupled links which connects each node of a network slice with each node of other slices.…”

Section: Multilevel Network Community Detectionmentioning

confidence: 99%

Consumers Team Detection Model Based on Trust for Multi-Level

Armoogum

et al. 2019

Mobile Information Systems

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Due to rapid advances in technology, social networks have become important platforms for daily communication, product marketing, and information dissemination. Targeted delivery of social network advertisement can considerably improve the efficacy of the advertisement and maximize the profits from it. In this context, managing the specific audience of a social network advertisement and achieving targeted advertisement delivery have been the ultimate goals of the social network advertising sector. Identifying user groups with similar properties is critical to increasing targeted sales. When both the scale of mobile social network and the coplexity of social network user behaviors grow, similar groups are hidden in user behaviors. In order to analyze community structure with user trust relationship more appropriately in the large-scale multilevel social network environment, a novel local community detection model E-MLCD is proposed in this paper. It is jointly based on the multilevel properties and the strength of similarity of multilevel social interaction among communities. By studying three real-world multilevel social networks and specific QQ Zone marketing data, the model defines a new metric of community trust based on similarity. Comparison between other state-of-the-art detection methods demonstrate E-MLCD’s ability to detect communities more effectively.

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“…The authors in this study (Yang et al, 2014) proposed a node prominence profile-based method to effectively predict the degree centrality in a network. In another study (Gaye et al, 2016), authors propose a solution to find the top-K influential person in an MLN social network using diffusion probability. More recently there has been some work in developing algorithms for MLNs using the decoupling-based approach (Santra et al, 2017b).…”

Section: Introductionmentioning

confidence: 99%

Degree Centrality Algorithms For Homogeneous Multilayer Networks

Pavel¹,

Santra²,

Chakravarthy³

2022

Preprint

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Centrality measures for simple graphs/networks are well-defined and each has numerous main-memory algorithms. However, for modeling complex data sets with multiple types of entities and relationships, simple graphs are not ideal. Multilayer networks (or MLNs) have been proposed for modeling them and have been shown to be better suited in many ways. Since there are no algorithms for computing centrality measures directly on MLNs, existing strategies reduce (aggregate or collapse) the MLN layers to simple networks using Boolean AND or OR operators. This approach negates the benefits of MLN modeling as these computations tend to be expensive and furthermore results in loss of structure and semantics. In this paper, we propose heuristic-based algorithms for computing centrality measures (specifically, degree centrality) on MLNs directly (i.e., without reducing them to simple graphs) using a newly-proposed decoupling-based approach which is efficient as well as structure and semantics preserving. We propose multiple heuristics to calculate the degree centrality using the network decoupling-based approach and compare accuracy and precision with Boolean OR aggregated Homogeneous MLNs (HoMLN) for ground truth. The network decoupling approach can take advantage of parallelism and is more efficient compared to aggregationbased approaches. Extensive experimental analysis is performed on large synthetic and real-world data sets of varying characteristics to validate the accuracy and efficiency of our proposed algorithms.

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Multi-diffusion Degree Centrality Measure to Maximize the Influence Spread in the Multilayer Social Networks

Cited by 7 publications

References 15 publications

Maximizing multiple influences and fair seed allocation on multilayer social networks

Maximizing multiple influences and fair seed allocation on multilayer social networks

Consumers Team Detection Model Based on Trust for Multi-Level

Degree Centrality Algorithms For Homogeneous Multilayer Networks

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