LICOD: Leaders Identification for Community Detection in Complex Networks

Kanawati, Rushed

doi:10.1109/passat/socialcom.2011.206

Cited by 20 publications

(28 citation statements)

References 14 publications

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“…Their main strategy is to select a central influential node and add proper nodes to expand the community until a stopping criterion is met [27]. Since in these works, the initial seed nodes are high central members, they usually referred as community core or community leaders [24]. With the assumption that every group of individuals in communities is composed of two types of members: leaders and followers, Rabbany et al [28] found the k-most central nodes as top leaders which could be followed by non-leader nodes to form communities.…”

Section: A Seed-centric Approachmentioning

confidence: 99%

“…The central position of a leader makes it a good option to be chosen as the initial source node in the seed-centric local community detection methods. Hence, different definitions of centrality were applied to distinguish desirable leaders from non-leader members [24]. Following this idea, we designed an efficient method to incrementally detect the communities in the dynamic social networks using the intuitive idea of importance and persistence of community leaders over time.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership

Javadi

Gharani

Khadivi

2018

Studies in Systems, Decision and Control

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Abstract-Detecting community structure in social networks is a fundamental problem empowering us to identify groups of actors with similar interests. There have been extensive works focusing on finding communities in static networks, however, in reality, due to dynamic nature of social networks, they are evolving continuously. Ignoring the dynamic aspect of social networks, neither allows us to capture evolutionary behavior of the network nor to predict the future status of individuals. Aside from being dynamic, another significant characteristic of realworld social networks is the presence of leaders, i.e. nodes with high degree centrality having a high attraction to absorb other members and hence to form a local community. In this paper, we devised an efficient method to incrementally detect communities in highly dynamic social networks using the intuitive idea of importance and persistence of community leaders over time. Our proposed method is able to find new communities based on the previous structure of the network without recomputing them from scratch. This unique feature, enables us to efficiently detect and track communities over time rapidly. Experimental results on the synthetic and real-world social networks demonstrate that our method is both effective and efficient in discovering communities in dynamic social networks.

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Section: A Seed-centric Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership

Javadi

Gharani

Khadivi

2018

Studies in Systems, Decision and Control

View full text Add to dashboard Cite

show abstract

“…Hence, community detection is referred often as graph clustering problem. Much prior works has been done on clustering as well as community detection in several domains (Blondel, Guillaume, Lambiotte, & Lefebvre, 2008;Boykov & Funka-Lea, 2006;Grady, 2006;He & Chen, 2015;Jeevan et al, 2011;Kanawati, 2011;Li & Wu, 2015;Liu & juan Ban, 2015;Lu, Sun, Wen, Cao, & La Porta, 2015;Shah & Zaman, 2010;Xu, Yuruk, Feng, & Schweiger, 2007;Wei et al, 2015). During community exploration process, properties of network elements are generally analyzed in three levels of abstraction.…”

Section: Related Workmentioning

confidence: 99%

“…Measuring similarity between two nodes (Moradi et al, 2014;Shang, Luo, Li, Jiao, & Stolkin, 2015) is also a kind of node level property. Popularly used centrality measure, the degree centrality (Kanawati, 2011) is defined simply utilizing degree or number of connections associated with any node. Simple logic behind this centrality measure is that the center of star or hub are definitely more central with respect to other nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Besides detecting communities SCAN (Xu et al, 2007) also detects hubs and outliers with respect to communities. Leader based agglomerative approaches such as Leader-Follower (LeadF) (Shah & Zaman, 2010) and LICOD (Kanawati, 2011) draws attention in recent years. Prior to detect communities, these approaches first identifies potential leaders of respective communities based on node and community level properties.…”

Section: Related Workmentioning

confidence: 99%

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Investigating community structure in perspective of ego network

Biswas

2015

Expert Systems with Applications

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Graph‐based tag recommendations using clusters of patients in clinical decision support system

Hafidi

Abdelwahed

Qassimi

2020

Concurrency and Computation

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Summary To support health professionals in making decisions, CDSS are developed to manage the patients' EHR, improve the way of diagnosis, and treatment of diseases. The process of analyzing EHRs is based on reading free‐text notes. However, it spends time and physicians' efforts. In this case, the most used solution is describing the EHRs with shortcut tags, representing pathologies or diseases, which are well‐defined and meaningful information. Still, this solution remains insufficient. The exploration of the relationship between those tags, the EHRs and their belonging patients will improve the analysis and then the CDSS. In this paper, we present a graph‐based tag recommendation approach that suggests relevant tags (diseases and pathologies) by analyzing the tagged medical images. We use graph analytics to generate graphs of tags, patients, and images by inspecting similar medical images descriptive. We have also created sub‐communities of patients with the same diseases by applying the Louvain clustering method. The tag recommendation aims to enhance the computer‐aided diagnosis in medical imaging. The tag recommendation approach will allow radiologists to detect and interpret invisible diseases of the underlying anatomical structure. It will also help in early revealing and diagnosis. The dataset ChestX‐Ray14 has been conducted to evaluate and test the accuracy and effectiveness of the proposed approach. Future perspective will focus on the deployment of our proposal within a Moroccan e‐health project.

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LICOD: Leaders Identification for Community Detection in Complex Networks

Cited by 20 publications

References 14 publications

Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership

Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership

Investigating community structure in perspective of ego network

Graph‐based tag recommendations using clusters of patients in clinical decision support system

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