Graph Mining Approaches to Study Volunteer Relationships in Sourceforge.net

2018 ◽  
pp. 117-139
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Information Flow ◽  
Graph Mining ◽  
Seed Set ◽  
Number Of Clusters ◽  
Small Set ◽  
Approaches To Study ◽  
Influential Nodes ◽  
Entire Network

The contribution of volunteers in the development of Free and Open Source Software in Sourceforge.net is studied in this paper. Using Social Network analysis, the small set of developers who can maximize the information flow in the network are discovered. The propagation of top developers across past three years are also studied. The four algorithms used to find top influential developers gives almost similar results. The movement of top developers over past years was also consistent. Influential nodes in a network are very important to diffuse influence on the rest of the network. They are most often highly connected within the network. The existing algorithms are efficient to identify them. However, the challenge is in selecting a seed set that can spread the influence instantaneously with least effort. In this paper, a method is defined to spread influence on the entire network by selecting the least number of non-overlapping influential nodes faster than a well known existing algorithm. Further to this, the number of clusters in the network is also determined simultaneously from the seed set of the networks.

Finding Influential Nodes in Sourceforge.net Using Social Network Analysis

2021 ◽  
pp. 156-166
Author(s):  
K.G. Srinivasa ◽  
Ganesh Chandra Deka ◽  
Krishnaraj P.M.
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Open Source ◽  
Information Flow ◽  
Open Source Software ◽  
Small Set ◽  
Influential Nodes

The contribution of volunteers in the development of Free and Open Source Software in Sourceforge.net is studied in this paper. Using Social Network analysis, the small set of developers who can maximize the information flow in the network are discovered. The propagation of top developers across past three years are also studied. The four algorithms used to find top influential developers gives almost similar results. The movement of top developers over past years years was also consistent.

Finding Influential Nodes in Sourceforge.net Using Social Network Analysis

2018 ◽  
pp. 104-116
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Open Source ◽  
Information Flow ◽  
Open Source Software ◽  
Small Set ◽  
Influential Nodes

The contribution of volunteers in the development of Free and Open Source Software in Sourceforge.net is studied in this paper. Using Social Network analysis, the small set of developers who can maximize the information flow in the network are discovered. The propagation of top developers across past three years are also studied. The four algorithms used to find top influential developers gives almost similar results. The movement of top developers over past years years was also consistent.

scholarly journals The Information Flow of the Sistema Nacional de Transplantes (National Transplant System): A Social Network Analysis - DOI: 10.3395/reciis.v6i2.572en

RECIIS ◽  
2012 ◽  
Vol 6 (2) ◽  
Author(s):  
André Luiz Dias de França ◽  
Isaac Newton Cesarino da Nóbrega Alves ◽  
Guilherme Ataíde Dias
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Flow ◽  
System A

Dyadic, Nodal, and Group-Level Approaches to Study the Antecedents and Consequences of Networks

2020 ◽  
pp. 187-218
Author(s):  
Filip Agneessens
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relations ◽  
Group Level ◽  
The Social ◽  
Approaches To Study ◽  
Antecedents And Consequences ◽  
Level Of Analysis

Social network analysis encompasses a variety of methods to study the social relations and social interactions between individual units in a group. This chapter offers an overview of the types of research questions that can be answered with social network analysis and discusses appropriate statistical methods and network sampling approaches to answer such questions. Six basic types of models are identified, based on two criteria: (1) whether the researchers are interested in the antecedents of networks and/or their consequences and (2) the appropriate level of analysis, in particular the dyadic, nodal, or group level. Extensions and variations of these six basic models are discussed, for example models where networks take on the role of mediator or moderator, as well as models that incorporate multiple levels of analysis and models that integrate network antecedents and network consequences simultaneously.

scholarly journals Sub-Community Graph Retrieval from a Compressed Community Graph using Graph Mining

2019 ◽  
Vol 9 (1) ◽  
pp. 6176-6182
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Graph Mining ◽  
Experimental Results ◽  
Large Community

Community detection and its retrieval is one of the most relevant and important topics in graph mining. Hence it is treated as one of the important applications in the field of social network analysis. Community detection plays an important role in a large community graph by enabling and selecting the desired community’s sub-graph. The proposed algorithm detects and extracts the desired sub-community graph from a compressed community graph for further analysis purpose. The authors present both theoretical and experimental results with three benchmark social networks. The proposed technique is efficient in terms of complexities.

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