Discovery and Visualization of Hierarchical Overlapping Communities from Bibliography Information

Kumar, Pushpa; Wang, Li; Chauhan, Janki; Zhang, Kang

doi:10.1109/dasc.2009.90

Cited by 5 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the variation of a resolution parameter, determining the average size of the communities, allows exploring hierarchical levels of the community structures in a network. Along the line of the CPM [38], Kumar et al [23] proposed a method, (HOC), to identify hierarchical and overlapping communities by finding maximal cliques in the underlying network. However, unlike CPM, HOC uses topological overlap criteria of equation 14 to define similarity between two arbitrary nodes in a network.…”

Section: Related Workmentioning

confidence: 99%

“…SLPA also considers the directed and weighted nature of networks to find overlapping community structures. The methods proposed in [27], [23], [41], and [59,58] can be considered as instances of the class multi-resolution methods that generally have a freely tunable parameter (resolution parameter) which allows to set the characteristic size of the clusters to be detected. This enables them to extract communities at varying levels of resolutions and thus form a community hierarchy.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

OCMiner: A density-based overlapping community detection method for social networks

Bhat¹,

Abulais²

2015

IDA

View full text Add to dashboard Cite

Community detection is an important task for identifying the structure and function of complex networks. The task is challenging as communities often show overlapping and hierarchical behavior, i.e., a node can belong to multiple communities, and multiple smaller communities can be embedded within a larger community. Moreover, real-world networks often contain communities of arbitrary size and shape, along with outliers. This paper presents a novel density-based overlapping community detection method, OCMiner, to identify overlapping community structures in social networks. Unlike other density-based community detection methods, OCMiner does not require the neighborhood threshold parameter (ε) to be set by the users. Determining an optimal value for ε is a longstanding and challenging task for density-based clustering methods. Instead, OCMiner automatically determines the neighborhood threshold parameter for each node locally from the underlying network. It also uses a novel distance function which utilizes the weights of the edges in weighted networks, besides being able to find communities in un-weighted networks. The efficacy of the proposed method has been established through experiments on various real-world and synthetic networks. In comparison to the existing state-of-the-art community detection methods, OCMiner is computationally faster, scalable to large-scale networks, and able to find significant community structures in social networks.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

OCMiner: A density-based overlapping community detection method for social networks

Bhat¹,

Abulais²

2015

IDA

View full text Add to dashboard Cite

show abstract

“…A single parameter relates the weight given to missing and existing links in the quality function and allows for an assessment of overlapping and hierarchical community structures. In line with CPM, Kumar et al propose a method hierarchical and overlapping communities (HOC) to identify hierarchical and overlapping communities by finding maximal cliques from the underlying network. However, unlike CPM, HOC uses the overlapping neighborhood criteria to define the similarity between two arbitrary nodes in a network.…”

Section: Community Analysismentioning

confidence: 99%

“…OSLOM is able to detect a hierarchical community structure by reapplying the algorithm on intermediate supernetworks of detected communities. The methods proposed by Lancichinetti et al, Kumar et al, and Reichardt and Bornholdt provide a tunable parameter (resolution parameter) whose value determines the size of the detected communities. This allows visualizing the community structure at different resolutions and thus forms a community hierarchy.…”

Section: Community Analysismentioning

confidence: 99%

Analysis and mining of online social networks: emerging trends and challenges

Bhat

Abulaish

2013

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Social network analysis (SNA) is a multidisciplinary field dedicated to the analysis and modeling of relations and diffusion processes among various objects in nature and society, and other information/knowledge processing entities with an aim of understanding how the behavior of individuals and their interactions translates into large‐scale social phenomenon. Because of exploding popularity of online social networks and availability of huge amount of user‐generated content, there is a great opportunity to analyze social networks and their dynamics at resolutions and levels not seen before. This has resulted in a significant increase in research literature at the intersection of the computing and social sciences leading to several techniques for social network modeling and analysis in the area of machine learning and data mining. Some of the current challenges in the analysis of large‐scale social network data include social network modeling and representation, link mining, sentiment analysis, semantic SNA, information diffusion, viral marketing, and influential node mining. WIREs Data Mining Knowl Discov 2013, 3:408–444. doi: 10.1002/widm.1105 This article is categorized under: Algorithmic Development > Web Mining Commercial, Legal, and Ethical Issues > Social Considerations Technologies > Structure Discovery and Clustering

show abstract

Communalyzer—Understanding Life Cycle of Community in Social Networks

Karthika

Geetha

2018

Innovations in Computer Science and Engineering

View full text Add to dashboard Cite

Discovery and Visualization of Hierarchical Overlapping Communities from Bibliography Information

Cited by 5 publications

References 19 publications

OCMiner: A density-based overlapping community detection method for social networks

OCMiner: A density-based overlapping community detection method for social networks

Analysis and mining of online social networks: emerging trends and challenges

Communalyzer—Understanding Life Cycle of Community in Social Networks

Contact Info

Product

Resources

About