A Novel Community Detection Algorithm Based on Paring, Splitting and Aggregating in Internet of Things

Li, Yizhe; Xia, Hui; Zhang, Rui; Hu, Ben-xu; Cheng, Xiangguo

doi:10.1109/access.2020.3006029

Cited by 7 publications

(4 citation statements)

References 57 publications

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“…Zarandi et al [17] arbitrarily deleted some edges in the network according to the similarity between edges, resulting in some subgraphs as the main communities, and then some subgraphs were merged to get optimal communities. In the pairing, splitting and aggregating algorithm (PSA) [18], the whole network is split into several similar node sets as an essential step to detect the final communities.…”

Section: Related Workmentioning

confidence: 99%

Community Detection using Closeness Similarity based on Common Neighbor Node Clustering Entropy

2022

KSII TIIS

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In order to efficiently detect community structure in complex networks, community detection algorithms can be designed from the perspective of node similarity. However, the appropriate parameters should be chosen to achieve community division, furthermore, these existing algorithms based on the similarity of common neighbors have low discrimination between node pairs. To solve the above problems, a noval community detection algorithm using closeness similarity based on common neighbor node clustering entropy is proposed, shorted as CSCDA. Firstly, to improve detection accuracy, common neighbors and clustering coefficient are combined in the form of entropy, then a new closeness similarity measure is proposed. Through the designed similarity measure, the closeness similar node set of each node can be further accurately identified. Secondly, to reduce the randomness of the community detection result, based on the closeness similar node set, the node leadership is used to determine the most closeness similar first-order neighbor node for merging to create the initial communities. Thirdly, for the difficult problem of parameter selection in existing algorithms, the merging of two levels is used to iteratively detect the final communities with the idea of modularity optimization. Finally, experiments show that the normalized mutual information values are increased by an average of 8.06% and 5.94% on two scales of synthetic networks and real-world networks with real communities, and modularity is increased by an average of 0.80% on the real-world networks without real communities.

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Section: Related Workmentioning

confidence: 99%

Community Detection using Closeness Similarity based on Common Neighbor Node Clustering Entropy

2022

KSII TIIS

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“…Most studies reported that retain auditing protocols to avoid leakage of privacy [2], [21]- [28]. Simultaneously, lightweight methods [29]- [35] are being implemented to advance the IoT and Smart devices to meet the audit process's productivity needs.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Secure Auditing Framework for Big Data Storage in Cloud Computing Environment

Fatima¹,

Hussain²,

Bakar³

2021

IJCDS

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Cloud storage enables to use and manage remote data efficiently but increase the risk of tampering data. This proposed paper a public auditing-based framework to develop a novel system for secure data auditing in cloud storage. We build a dynamic index table with no requirement to transfer elements in the update operation for inserting or deleting. If data is not incorporate in the cloud, the auditor from a third party can identify the corrupted block. The authorization is implemented between cloud storage and third-party to prevent dos attack. The proposed model is flexible, efficient, and secure as per detailed analysis and simulation results.

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“…Because the data stored in cloud for sharing can face many privacy challenge such as identity privacy and sharing data privacy. Scheme [21]- [29] proposed privacy preserving auditing protocols to prevent privacy leaking. At the same time, with the development of Internet of things and mobile devices, lightweight schemes [30]- [36] are proposed to satisfying the efficiency needs of auditing process.…”

Section: The Related Workmentioning

confidence: 99%

An Authorized Public Auditing Scheme for Dynamic Big Data Storage in Cloud Computing

Pan

2020

IEEE Access

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Nowadays, to utilize the abundant resources of cloud computing, most enterprise users prefer to store their big data on cloud servers for sharing and utilization. However, storing data in remote cloud servers is out of user's control and exposes to lots of security problems such data availability, unauthorized access and data integrity, among which data integrity is a challenging and urgent task in cloud computing. Many auditing schemes have been proposed to check the integrity of data in cloud, but these schemes usually have some disadvantages. One is that these auditing schemes cannot check which block is corrupt when the data is not integrated. The other is that there's no efficient authenticated data structure helping to achieve accurate auditing when the data needs to update frequently. To solve the problems, we propose a public auditing scheme for dynamic big data storage in cloud computing. Firstly, we design a dynamic index table, in which no elements need to be moved in insertion or deletion update operations. Secondly, when data in cloud is not integrated, the third-party auditor can detect which block is corrupt. Finally, an authorization is employed between the third party and cloud servers to prevent denial of service attack. The theoretical analysis and the simulation results demonstrate that our scheme is more secure and efficient.

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A Novel Community Detection Algorithm Based on Paring, Splitting and Aggregating in Internet of Things

Cited by 7 publications

References 57 publications

Community Detection using Closeness Similarity based on Common Neighbor Node Clustering Entropy

Community Detection using Closeness Similarity based on Common Neighbor Node Clustering Entropy

An Efficient Secure Auditing Framework for Big Data Storage in Cloud Computing Environment

An Authorized Public Auditing Scheme for Dynamic Big Data Storage in Cloud Computing

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