A review on overlapping community detection methodologies

C, Rashmi; Kodabagi, M. M.

doi:10.1109/smarttechcon.2017.8358576

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…As described earlier, this attribute has importance when considered with the school name, degree, and industry of a person. Therefore, the similarity is computed in equation (12).…”

Section: Education-field-of-studymentioning

confidence: 99%

“…These issues need to be addressed to create a network of social network users. Such connected networks are used in various platforms such as network creation [10], community detection [11], [12] structural clustering [13], [14] influential node detection [15], [16] link prediction [17], useful for the development of various technological solutions such as recommendation system [18], [19], recruitment system [20], [21] crime detection [22], and demography [23], etc. Therefore, establishing a connection between LinkedIn profiles of any online social network is a paramount task.…”

Section: Introductionmentioning

confidence: 99%

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Connecting User Profiles of Social Networks Using Proximity-Based Clustering

Kodabagi

2022

MJCS

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The establishment of connections among social network users using their profile information is an important task in social network analysis, which facilitates the development of various technological solutions such as stock market analysis, crime detection, tracking system of fraudulent events, etc. In this work, a proximity-based clustering method for networking LinkedIn profiles is presented. The proposed system computes proximity value between users using various attributes of user profiles. The proximity measures are computed by analyzing unstructured data of user profiles to connect users. The method addresses various issues such as comparison of familiar sentences, finding patterns, and sub-patterns among user profiles, assigning weights on attributes similarity, and computing total similarity which is associated with unstructured data. After computing proximity measures on various attributes of user profiles, the connecting edges between nodes are determined by employing artificial intelligence and a network graph is formed. The method is evaluated on a LinkedIn data-set to form a connected graph. The strength of the proposed methodology lies in the formation of multi-layered network graphs, as it uses various attributes of the user profiles to connect them. The proposed methodology helps various applications like recommendation systems to form network graphs of selected attributes and perform the social network analysis. The method achieves an accuracy of 96%. However, the profiles containing abbreviations of important information are not matched and the system accuracy drops down in such cases.

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“…As described earlier, this attribute has importance when considered with the school name, degree, and industry of a person. Therefore, the similarity is computed in equation (12).…”

Section: Education-field-of-studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Connecting User Profiles of Social Networks Using Proximity-Based Clustering

Kodabagi

2022

MJCS

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“…In terms of network topology, this is known as overlapping communities. In current scenario, people have diversified interest areas, so one individual can belong to different communities [1]. Therefore, overlapping community detection has been considered as key research area over the last decade.…”

Section: Introductionmentioning

confidence: 99%

SLPA-IF1: Label Propagation Based Overlapping Community Detection

Monika

Mangat

2022

IEEE Access

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Detection of overlapping communities over a network is imperative due to its applicability in multiple domains starting from geographical to online networks. This paper proposes an effective overlapping community detection method SLPA-IF1. Initially, nodes label initialization is done during pre-processing of data. Label updation and propagation is performed during the evolution phase which consists of selection of listener node, speaker rule and listener rule. Speaker rule is modified to consider the mean of occurring frequency of labels instead of random label selection. We have also proposed a new measure named label specificity for listener rule which is calculated as the mean of occurring frequency of labels minus probability of occurrence of that label. The proposed method leads to more accurate label selection during detection of communities over a network. The run time computation has shown the scalability of the proposed method with respect to increasing network size. For large scale networks, the computing time of the proposed method is less than other state-of-theart methods.

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