AD-C: A New Node Anomaly Detection based on Community Detection in Social Networks

Keyvanpour, Mohammad Reza; Shirzad, Mehrnoush Barani; Ghaderi, Maryam

doi:10.1504/ijics.2019.10022066

Cited by 3 publications

(3 citation statements)

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“…(Jie et al, 2021;Jinjun et al, 2021) used community detection technology to analyze travel demand in the research of urban public transport system. (Keyvanpour et al, 2020) proposed an anomaly recognition method of social network graph based on community detection (AD-C) and applied it to identify anomalies in social networks. (Kamakshi and Sriram, 2020) used the community detection algorithm to form a stable group of vehicles, reduced the system overhead and time delay, and improved the safety, tra c e ciency and convenience of vehicles and roads.…”

Section: Introductionmentioning

confidence: 99%

Analysis of deformation characteristics of reverse slope under the influence of reservoir water based on community detection

et al. 2022

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In order to study the deformation characteristics of reverse slope, this paper took the slope of Xiaodongcao as the research object, applied the Louvain community detection algorithm, considered the in uence of reservoir water level change, partitioned the slope deformation characteristics. The deformation characteristic zoning result was superimposed with the slope displacement cloud map and three types of geological geometric characteristic factor zoning map obtained by ArcGIS. The results show that Community detection can quickly identify the closely connected part of slope network, and the speci c location of this part is affected by reservoir water. After the community detection result is superimposed with the displacement cloud map, the areas with large deformation and close connection in the slope can be identi ed. It is found that the community with severe deformation have at least 5% more displacement and up to 21% more displacement than that with slow deformation. In addition, the location of leader nodes can be identi ed, and the number of leader nodes does not exceed 20% of the total nodes in the community, and its average displacement is at least 10% more than that of ordinary nodes, up to 36%. After the community detection result is superimposed with the zoning map of slope grade, it can be concluded that the slope grade within the community with severe deformation is greater than 60°, indicating that the larger slope grade is more sensitive to the bank slope deformation.

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Section: Introductionmentioning

confidence: 99%

Analysis of deformation characteristics of reverse slope under the influence of reservoir water based on community detection

et al. 2022

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“…Mining network structures is also the key to revealing and comprehending organizational principles and operational functions of complex network systems. For example, community detection has been applied to applications such as recommendation [2], [3], anomaly detection [4], [5], and terrorist organization identification [6], just to name a few. Much effort has also been devoted to the analysis of network structural properties, e.g., the small world effect (i.e., the average distance between nodes is short [8]) and the scale free property (i.e., the distribution of node degrees follows a power law distribution [9]).…”

Section: Introductionmentioning

confidence: 99%

A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

Jin

Jiao

et al. 2021

Preprint

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Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions. Community detection has been extensively studied in and broadly applied to many real-world network problems. Classical approaches to community detection typically utilize probabilistic graphical models and adopt a variety of prior knowledge to infer community structures. As the problems that network methods try to solve and the network data to be analyzed become increasingly more sophisticated, new approaches have also been proposed and developed, particularly those that utilize deep learning and convert networked data into low dimensional representation. Despite all the recent advancement, there is still a lack of insightful understanding of the theoretical and methodological underpinning of community detection, which will be critically important for future development of the area of network analysis. In this paper, we develop and present a unified architecture of network communityfinding methods to characterize the state-of-the-art of the field of community detection. Specifically, we provide a comprehensive review of the existing community detection methods and introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We then discuss in detail the main idea behind each method in the two categories. Furthermore, to promote future development of community detection, we release several benchmark datasets from several problem domains and highlight their applications to various network analysis tasks. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.

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Section: Introductionmentioning

confidence: 99%

Analysis of Deformation Characteristics of Reverse Slope Under The Influence of Reservoir Water Based On Community Detection

Xie

Cui

Qin

et al. 2021

Preprint

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In order to study the deformation characteristics of reverse slope, this paper took the slope of Xiaodongcao as the research object, applied the Louvain community detection algorithm, considered the influence of reservoir water level change, partitioned the slope deformation characteristics. The deformation characteristic zoning result was superimposed with the slope displacement cloud map and three types of geological geometric characteristic factor zoning map obtained by ArcGIS. The results show that：Community detection can quickly identify the closely connected part of slope network, and the specific location of this part is affected by reservoir water. After the community detection result is superimposed with the displacement cloud map, the areas with large deformation and close connection in the slope can be identified. It is found that the community with severe deformation have at least 5% more displacement and up to 21% more displacement than that with slow deformation. In addition, the location of leader nodes can be identified, and the number of leader nodes does not exceed 20% of the total nodes in the community, and its average displacement is at least 10% more than that of ordinary nodes, up to 36%. After the community detection result is superimposed with the zoning map of slope grade, it can be concluded that the slope grade within the community with severe deformation is greater than 60°, indicating that the larger slope grade is more sensitive to the bank slope deformation.

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AD-C: A New Node Anomaly Detection based on Community Detection in Social Networks

Cited by 3 publications

References 0 publications

Analysis of deformation characteristics of reverse slope under the influence of reservoir water based on community detection

Analysis of deformation characteristics of reverse slope under the influence of reservoir water based on community detection

A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

Analysis of Deformation Characteristics of Reverse Slope Under The Influence of Reservoir Water Based On Community Detection

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