Novel Multiplex PageRank in Multilayer Networks

Tu, Xiao; Jiang, Guoping; Song, Yurong; Zhang, Xu

doi:10.1109/access.2018.2807778

Cited by 28 publications

(17 citation statements)

References 28 publications

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“…On the other hand, the effect of base network choice on the final multiplex PageRank integration was minor when analyzing T-cells ( Figure S7 ). Thus, one possible future direction might be developing “reciprocal” multiplex PageRank analysis for GRNs, in which feedbacks among multiplex networks are considered as reported in ( Tu et al., 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Prioritizing transcriptional factors in gene regulatory networks with PageRank

et al. 2021

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Section: Discussionmentioning

confidence: 99%

Prioritizing transcriptional factors in gene regulatory networks with PageRank

et al. 2021

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“…Extending our method to be able to use heterogeneous networks with multiple types of relationships is a direction for future work. A possible solution is to use multilayer-networkbased versions of PageRank (e.g., see [40], [41]) in CSR;…”

Section: Discussionmentioning

confidence: 99%

Search Personalization Based on Social-Network-Based Interestedness Measures

et al. 2019

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Personalized search methods try to tailor the search results to the user's needs and preferences. A popular source of information for personalization is social networks of users. In this paper, we aim at proposing a social-network-based personalized information retrieval (PIR) method that: i) is holistic (not just considering some local neighborhood of the searcher); ii) is efficient in term of computational/storage cost; iii) its personalization component has a probabilistic basis. To the best of our knowledge, no such method exists. We propose an interestedness measure and several variations of it for quantifying the interestedness of each user in another user based on a social network of users. The general idea is to try to mimic a searcher's behavior in the real world to estimate the interestedness. The measures are then exploited to personalize the retrieval results. We evaluate the resulting PIR methods and compare them in terms of retrieval effectiveness and computational/storage cost. We also compare them with some baseline methods. In summary, our analyses suggest that retrieval based on at least one of the measures performs well in terms of both retrieval effectiveness and computational/storage cost.INDEX TERMS Information retrieval, personalization, personalized pagerank, social networks.

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“…A node in a multilayer network resides at different layers, and these distinct positions constitute the rank of each node. The weight of the node in corresponding layers is aggregated with intra-layer influence and votes [28]. Rank depends on the coupling relation between layers and characteristics of interconnections.…”

Section: Solution Approachmentioning

confidence: 99%

Exploring Intelligent Approach of Influence Minimization Considering the Node Surveillance in Online Social Networks

Cheriyan

Sajeev

2021

Preprint

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Attractive information such as innovations, awareness campaigns, branding, and advertising help people positively. Whereas, awful information such as rumors, malicious viruses, pornography, and revenge disturb people. The negative information contributes to chaos among people; therefore, it is to be blocked and hinder from further diffusion. This has motivated us towards the study of the problem named influence minimization. As the real world network can be modeled to a multilayer network, we focus our study towards the information diffusion through a multilayer network. Each node assigns a threshold, and its variation affects the rate of influence propagation across the network. In the influence minimization problem, the energy level of each node changes that help to formulate the function that minimizes the influence propagation. By applying two reduction policies, we are able to optimize our objective of minimizing the influence towards repulsive information. In this article, we consider the user response and its surveillance in the network. Repeated experiments on real networks has helped us to validate the proposed methods.

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Novel Multiplex PageRank in Multilayer Networks

Cited by 28 publications

References 28 publications

Prioritizing transcriptional factors in gene regulatory networks with PageRank

Prioritizing transcriptional factors in gene regulatory networks with PageRank

Search Personalization Based on Social-Network-Based Interestedness Measures

Exploring Intelligent Approach of Influence Minimization Considering the Node Surveillance in Online Social Networks

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