Detecting community structure based on traffic at node in networks

Sihag, Vikas; Anand, Abhineet; Tomar, Ravi; Chandra, Jagdish; Tiwari, Rajeev; Dumka, Ankur; Poonia, Ajeet Singh

doi:10.1109/sceecs.2014.6804502

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…There are many software for social network analysis. GEPHI (Bastian et al, 2009), is an open source alternative software. By importing nodes and edges in the Brightkite dataset, it is possible to divide the available data into groups according to modularity values.…”

Section: Methodsmentioning

confidence: 99%

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Investigation of Spatial Data With Open Source Social Network Analysis and Geographic Information Systems Applications

Sabah

Şimşek

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Social networks are the real social experience of individuals in the online environment. In this environment, people use symbolic gestures and mimics, sharing thoughts and content. Social network analysis is the visualization of complex and large quantities of data to ensure that the overall picture appears. It is the understanding, development, quantitative and qualitative analysis of the relations in the social networks of Graph theory. Social networks are expressed in the form of nodes and edges. Nodes are people / organizations, and edges are relationships between nodes. Relations are directional, non-directional, weighted, and weightless. The purpose of this study is to examine the effects of social networks on the evaluation of person data with spatial coordinates. For this, the cluster size and the effect on the geographical area of the circle where the placements of the individual are influenced by the frequently used placeholder feature in the social networks have been studied.

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Section: Methodsmentioning

confidence: 99%

“…It was designed to measure the strength of division of a network into modules and used for community structure detections in networks. (V. K. Sihag et al, 2014). Networks with high modularity have dense connections between the nodes within modules but sparse connections between nodes in different modules.…”

Section: Methodsmentioning

confidence: 99%

Investigation of Spatial Data With Open Source Social Network Analysis and Geographic Information Systems Applications

Sabah

Şimşek

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In In this study, we also examine the ego network of several enterprises and use community discovery algorithms to analyze their coinnovation patterns. Due to the sparseness of the ego network, we employ a greedy search algorithm for community detection and validate the results using other community discovery algorithms, such as the Edge-betweenness algorithm (Sihag et al, 2014). We also display the names of nodes with more than half of the sample number of partners.…”

Section: Embedding Analysis Of Technology Innovation Performance and ...mentioning

confidence: 99%

Boundary spanning strategy and tacit knowledge recombination: Analysis of joint‐patent networks of patent‐intensive firms

Jiang

Zhang

2023

Manage Decis Econ

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This paper collects 103 sample firms' 2015 to 2019 data through data envelopment analysis to measure their technical innovation performance and social network analysis to build joint‐patent networks and explore knowledge‐creating alliance relationships. The article highlights how tacit knowledge recombination works as a mechanism of boundary spanning. It emphasizes the significance of strategic balancing networks' position to improve technical innovation performance and competitiveness of patent‐intensive (PI) firms. This research contributes to shedding additional light on the nature of the boundary‐spanning mechanism and PI firms' knowledge exchanges. Specifically, it examines how balancing boundaries spanning through joint‐patent networks can influence the processes of tacit knowledge transmission and recombination between knowledge‐based actors.

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Affinity spaces on a Twitter hashtag for teacher learning

Marcelo-Martínez

Marcelo

2023

Globalisation, Societies and Education

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Detecting community structure based on traffic at node in networks

Cited by 5 publications

References 19 publications

Investigation of Spatial Data With Open Source Social Network Analysis and Geographic Information Systems Applications

Investigation of Spatial Data With Open Source Social Network Analysis and Geographic Information Systems Applications

Boundary spanning strategy and tacit knowledge recombination: Analysis of joint‐patent networks of patent‐intensive firms

Affinity spaces on a Twitter hashtag for teacher learning

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