Location- and relation-based clustering on privacy-preserving social networks

Yin, Dong; Shen, Yiran

doi:10.26599/tst.2018.9010017

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…A long‐standing issue makes it difficult to identify all groups of vertices that are related in their inner nodes but have a lower density in their outside nodes 20 . In addition to the structural information on the social network, the location information cluster quality is identified as the map is closely linked and can be upgraded to compact clusters in the cloud.…”

Section: Literature Surveymentioning

confidence: 99%

Design of efficient location‐based multipath self‐adaptive balancer router using particle swarm optimization in wireless sensor network

Sakthivel¹,

Manikandan²,

Vivekanandhan³

2022

Int J Communication

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Summary The power sensor terminals control in wireless sensor network (WSN) communicate within a small number of its radio range. As a branch operating based on the cluster routing protocol, it has proven to be effective in network topology management, energy reduction, and path connection link quality of the path. Location‐based clusters are categorized into member nodes and group/cluster heads (CHs). The CH election process increases the overhead of the network. The processing and energy limitations of the nodes are considered for the CH election process. The allocation of system resources is the major issue in the area of networks. In this work, the proposed multipath self‐adaptive balancer routing‐based particle swarm optimization (MLBAR‐PSO) achieves load balancing by means of the link quality of the route and the calculated average latency and reliability of the path. In this way, different trust values are set in each category. The forwarding node value is the value of each node based on the weight value, which is calculated based on the energy ratio of the node and the distance between the hiding node and the target node. Particle swarm optimization (PSO) selects the head cluster from a specific location within the cluster to enable other nodes to interact with the CH without spending energy. Also, the algorithm is used to find the optimal path for the base station and significantly increases the energy to reduce the distance and delay over the lifetime of the network.

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Section: Literature Surveymentioning

confidence: 99%

Design of efficient location‐based multipath self‐adaptive balancer router using particle swarm optimization in wireless sensor network

Sakthivel¹,

Manikandan²,

Vivekanandhan³

2022

Int J Communication

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“…In the digital age, online social networks have become a key communication platform for millions of internet users. Every day, huge number of posts (tweets, messages) are emerging from and disseminating among online Social Network Sites (SNS) [1] . Usually, users are eager to make sense of the information propagation process around them.…”

Section: Introductionmentioning

confidence: 99%

Propagation history ranking in social networks: A causality-based approach

Wang¹,

Wang²,

Yao³

et al. 2020

Tinshhua Sci. Technol.

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Information diffusion is one of the most important issues in social network analysis. Unlike most existing works, which either rely on network topology or node profiles, this study focuses on the diffusion itself, i.e., the recorded propagation histories. These histories are the evidence of diffusion and can be used to explain to users what happened in their networks. However, these histories can quickly grow in size and complexity, limiting their capacity to be intuitively understood. To reduce this information overload, in this paper we present the problem of propagation history ranking. The goal is to rank participant edges/nodes by their contribution to the diffusion. We first discuss and adapt a causal measure, Difference of Causal Effects (DCE), as the ranking criterion. Then, to avoid the complex calculation of DCE, we propose two integrated ranking strategies by adopting two indicators. One is responsibility, which captures the necessity aspect of causal effects. We further give an approximate algorithm, which could guarantee a feasible solution, for this indicator. The other is capability, which captures the sufficiency aspect of causal effects. Finally, promising experimental results are presented to verify the feasibility of the proposed ranking strategies.

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