Influential nodes selection to enhance data dissemination in mobile social networks: A survey

Tulu, Muluneh Mekonnen; Mkiramweni, Mbazingwa E.; Hou, Ronghui; Feisso, Sultan; Younas, Talha

doi:10.1016/j.jnca.2020.102768

Cited by 20 publications

(9 citation statements)

References 105 publications

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“…Our classifications are more comprehensive and finer-grained (7 categories) than those of other approaches, as illustrated in Section 4 and Section 5. As for evaluation metrics, references [6,81,121] only analyzed the correctness metrics, and references [39,109] didn't address the evaluation metrics. Only reference [85] involved part of evaluation metrics in capability and correctness set we defined.…”

Section: Discussionmentioning

confidence: 99%

“…Approaches proposed in references [6,81,85,121] involved the validation metrics, but they focused on the classification of methods [39,61,72,109], and did not systematically analyzing and summarize the evaluation metrics and their fitness for different type of networks. References [6,39,109] summarized and categorized influential node detection methods designed just for social networks, and reference [121] surveyed the evaluation methods for node importance in world city networks. Reference [85] systematically surveyed INRMs based on k-shell decomposition with part capability metrics.…”

Section: Introductionmentioning

confidence: 99%

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Epidemic Model-based Network Influential Node Ranking Methods: A Ranking Rationality Perspective

Zhang¹,

Zhao²,

Nie³

et al. 2023

Preprint

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Most recent surveys and reviews on Influential Node Ranking Methods (INRMs) hightlight discussions on the methods' technical details, but there still lacks in-depth research on the fundamental issue of how to verify the considerable influence of these nodes in a network. Compared to conventional verification models such as cascade failure and linear threshold, the epidemic model is more widely used. Accordingly, we conducted a survey of INRM based on epidemic model on 81 primary studies and analyzed their Capability and Correctness which we defined in our work. Our study categorized 4 types of networks used by INRM, classified 7 categories of INRMs for analyzing the networks and defined 2 evaluation metrics set of Capability and Correctness for evaluating INRM from Ranking Rationality Perspective. We also discussed particular real-world networks that were used to evaluate INRM and the Capability and Correctness of different INRMs on ranking nodes in specific networks. This is, as far as we know, the first survey aimed at systematically summarizing the Capability and Correctness of INRM. Our findings can assist practitioners and researchers in choosing and comparing INRMs and identifying research gaps.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Epidemic Model-based Network Influential Node Ranking Methods: A Ranking Rationality Perspective

Zhang¹,

Zhao²,

Nie³

et al. 2023

Preprint

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show abstract

“…Regarding opinion leader identification, Bamakan et al pointed out that it is an important task and summarized the corresponding identification methods [ 53 ]. Existing researchers effectively proposed approaches to identify central nodes in the real world [ 54 ] and mobile social networks [ 55 ]. The concept of top-k nodes is also proposed to identify the influential actors in a social network [ 56 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Heterogeneous Diffusion of Government Microblogs and Public Agenda Networks during Public Policy Communication in China

Cai

Gong

Liu

2023

Entropy

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During public policy information diffusion, policy interpretation on government microblogs and public attention interact, but there are certain differences. We construct a research framework for the heterogeneous diffusion of public policy information on government microblogs. An empirical study is conducted based on the Network Agenda Setting (NAS) model. First, a combination of topic mining and content analysis is used to identify the issues discussed by government microblogs and citizens. Then, we use the importance of nodes in Degree Structure (DS) and Flow Structure (FS) entropy to measure their attention to different issues. Finally, the Quadratic Assignment Procedure (QAP) correlation and regression analysis explore the degree of heterogeneity and causal relationship between government microblog agenda networks (GMANs) and public agenda networks (PANs). We find that GMANs influence PANs and the degree of heterogeneity between them is relatively low at the beginning of policy implementation. However, as government microblogs reveal positive effects of policy implementation, they fail to influence PANs effectively, and there is a greater degree of heterogeneity between them. Moreover, PANs do not significantly affect GMANs. The dynamic leading relationship between GMANs and PANs in public policy diffusion is clarified, helping to shape the image of digital government in public opinion.

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“…The concept of centrality-based community is employed for opportunistic routing where a message is pushed towards a central community with the understanding that nodes in such a community have a high probability to get connected with the destination of the message [17]. It has also been argued that nodes with a high value of betweenness centrality are susceptible to face traffic congestion as well as energy depletion because of their high probability of participation in the routing process [18]. Miralda et al [19] have employed a variant of betweenness centrality based on fuzzy logic with the aim of energy conservation that is crucial in Io nodes in opportunistic networks.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Congestion Aware Social Metrics for Centrality‐Based Routing

Islam

Iqbal

Aleem

et al. 2021

Wireless Communications and Mobile Computing

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Opportunistic networks utilize pocket switching for routing where each node forwards its messages to a suitable next node. The selection of the forwarder node is crucial for the efficient performance of a routing protocol. In any opportunistic network, some nodes have a paramount role in the routing process and these nodes could be identified with the assistance of the existing centrality measures available in network theory. However, the central nodes tend to suffer from congestion because a large number of nodes that are relatively less central attempt to forward their payload to the central nodes to increase the probability of the message delivery. This paper evaluates mechanisms to transform the social encounters into congestion aware metrics so that high-ranking central nodes are downgraded when they encounter congestion. The network transformations are aimed at aggregating the connectivity patterns of the nodes to implicitly accumulate the network information to be utilized by centrality measures for routing purposes. We have analyzed the performance of the metrics’ computed centrality measures using routing simulation on three real-world network traces. The results revealed that betweenness centrality along with the congestion aware network metrics holds the potential to deliver a competitive number of messages. Additionally, the proposed congestion aware metrics significantly balance the routing load among the central nodes.

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Influential nodes selection to enhance data dissemination in mobile social networks: A survey

Cited by 20 publications

References 105 publications

Epidemic Model-based Network Influential Node Ranking Methods: A Ranking Rationality Perspective

Epidemic Model-based Network Influential Node Ranking Methods: A Ranking Rationality Perspective

Heterogeneous Diffusion of Government Microblogs and Public Agenda Networks during Public Policy Communication in China

Evaluation of Congestion Aware Social Metrics for Centrality‐Based Routing

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