Community detection in social networks

Bedi, Punam; Sharma, C.B.

doi:10.1002/widm.1178

Cited by 221 publications

(105 citation statements)

References 108 publications

Supporting

Mentioning

102

Contrasting

Unclassified

Order By: Relevance

“…Community detection is the process of discovering communities in networks and is widely used in network analysis (Bedi and Sharma 2016). We use the walktrap algorithm (Pons and Latapy 2005) to detect communities in the university knowledge transfer network.…”

Section: Community Detection Measuresmentioning

confidence: 99%

Network dynamics of Chinese university knowledge transfer

Moortel

Crispeels

2019

J Technol Transf

View full text Add to dashboard Cite

Social networks are increasingly considered to be influential in explaining the knowledge transfer process. Despite scholarly efforts to integrate knowledge transfer and social network research, we lack understanding on how knowledge transfer networks emerge and evolve. We draw upon resource dependency theory and inter-organizational networks and collect patent data of 42 Double-First Class (DFC) universities to study structural properties of the Chinese university knowledge transfer network over time. Our results point to the existence of an increasingly complex yet remarkably efficient network. Universities and co-patent collaborations emerge in the network and act as knowledge bridges between other universities. The network moves from an early-stage single-centered network to a mature multi-centered network through a power-law pattern. Such movement allows for an aggregation phenomenon to appear as oligopolistic communities emerge and rule the network. While knowledge is more easily shared and accessible within communities, their existence also brings along control over knowledge bases embedded in those communities. Key universities take central positions within the expanding network, which allows them to gain control and easier access to knowledge. It also hints that it might be difficult for other DFC universities to become key players in the network. On an inter-regional level, our findings point to steadily increasing knowledge transfer activity, which is key to overcome the underdevelopment of some Chinese regions. Overall, this paper contributes to our understanding on the theoretical connection between knowledge transfer and social network dynamics, on how universities evolve through knowledge transfer networks, and on how their embeddedness translates into knowledge control, knowledge access, and knowledge bridges.

show abstract

Section: Community Detection Measuresmentioning

confidence: 99%

Network dynamics of Chinese university knowledge transfer

Moortel

Crispeels

2019

J Technol Transf

View full text Add to dashboard Cite

show abstract

“…, B k−1 be the clockwise sequence of groups along R(C), starting from an arbitrary group B 0 . For each group B i , let f i and l i be its first and its last element, respectively, i.e., l i and f i+1 (indexes taken (3,4), (4,5). In (a) the chords form 3 crossings, while in (b) they do not cross, due to a more convenient choice of the representative pair of arcs for the edges (1, 2) and (3,4).…”

Section: Algorithmsmentioning

confidence: 99%

ChordLink: A New Hybrid Visualization Model

Angori

Didimo

Montecchiani

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Many real-world networks are globally sparse but locally dense. Typical examples are social networks, biological networks, and information networks. This double structural nature makes it difficult to adopt a homogeneous visualization model that clearly conveys an overview of the network and the internal structure of its communities at the same time. As a consequence, the use of hybrid visualizations has been proposed. For instance, NodeTrix combines node-link and matrixbased representations (Henry et al., 2007). In this paper we describe ChordLink, a hybrid visualization model that embeds chord diagrams, used to represent dense subgraphs, into a node-link diagram, which shows the global network structure. The visualization is intuitive and makes it possible to interactively highlight the structure of a community while keeping the rest of the layout stable. We discuss the intriguing algorithmic challenges behind the ChordLink model, present a prototype system, and illustrate case studies on real-world networks.

show abstract

“…The algorithm consists of three stages: community detection, merging, and refining. OCMiner is a density‐based community detection algorithm proposed by Bhat et al Further overlapping community detection algorithms were surveyed in details in Ref .…”

Section: Related Workmentioning

confidence: 99%

Leader‐based community detection algorithm for social networks

Helal

Ismail

Badr

et al. 2017

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Community detection has become a crucial task in social network mining. Detecting communities summarizes interactions between members for gaining deep understanding of interesting characteristics shared between members of the same community. In this research, we propose a novel community detection algorithm for the purpose of revealing and analyzing hidden similar behavior of online users. The proposed algorithm is based mainly on similar members’ actions rather than the structure similarity only for the aim of detecting communities that are closely mapped to the underlying behavioral communities in real social networks. First, leaders of the social network are discovered, then, communities are detected based on those leaders. The idea is grounded on the assumption that communities could be formed around people with great influence. Extensive experiments and analysis show the ability of the proposed algorithm to successfully detect real‐world communities with improved accuracy. WIREs Data Mining Knowl Discov 2017, 7:e1213. doi: 10.1002/widm.1213 This article is categorized under: Technologies > Structure Discovery and Clustering

show abstract

Community detection in social networks

Cited by 221 publications

References 108 publications

Network dynamics of Chinese university knowledge transfer

Network dynamics of Chinese university knowledge transfer

ChordLink: A New Hybrid Visualization Model

Leader‐based community detection algorithm for social networks

Contact Info

Product

Resources

About