An Empirical Analysis of Developer Collaboration Network

2013 ◽  
Vol 303-306 ◽  
pp. 2177-2181
Author(s):  
Cheng Xiang Peng
Keyword(s):  
Open Source Software ◽  
Network Theory ◽  
Collaboration Network ◽  
Node Degree ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Software Projects ◽  
Intrinsic Nature ◽  
Domain Experts ◽  
Social Collaboration

To further verify the uses of bipartite network theory and understand the intrinsic nature in social collaboration network. In this paper, we get the information of open source software projects from Source-Forge web and construct a project management collaboration network by analyzing the data of project and manager. Then, through the ordinary projection two kinds of one-mode network are made and the degree distribution of one-mode network and origin bipartite networks shows a power-law like. Finally we evaluate the node's importance on manager network to acquire the core nodes, namely domain experts, by using the metric of node degree, between and topological potential respectively, and provide some helpful applications.

scholarly journals Natural Language Use as Bipartite Networks in Psychology

2021 ◽  
Author(s):  
Juan C. Correa
Keyword(s):  
Natural Language ◽  
Network Theory ◽  
Language Use ◽  
Future Research ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Research Directions ◽  
Future Research Directions ◽  
Data Source ◽  
Opinion Article

Natural language as a data source is quite common in different divisions of psychology. Among the several ways to analyze the information conveyed by natural language, psychologists rarely use bipartite networks despite the strong potential that this network perspective has for enriching psychology's research toolbox. This opinion article aims to provide a viewpoint on current advances and promising future research directions on modeling natural language as a bipartite network structure, using word-of-mouth as the basis for a tutorial exposition that paves the way for others to leverage the opportunities provided by network theory.

scholarly journals The role of bipartite structure in R&D collaboration networks

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
D Vasques Filho ◽  
Dion R J O’Neale
Keyword(s):  
Collaboration Network ◽  
European Patent ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Different Types ◽  
Underlying Network ◽  
Network Properties ◽  
Using Data ◽  
The One

Abstract A great number of real-world networks are, in fact, one-mode projections of bipartite networks comprised of two different types of nodes. In the case of interactions between institutions engaging in collaboration for technological innovation, the underlying network is bipartite with institutions (agents) linked to the patents they have filed (artefacts), while the projection is the co-patenting network. Since projected network properties are highly affected by the underlying bipartite structure a lack of understanding of the bipartite network has consequences for the information that might be drawn from the one-mode co-patenting network. Here, we create an empirical bipartite network using data from 2.7 million patents recorded by the European Patent Office. We project this network onto the agents (institutions) and look at properties of both the bipartite and projected networks that may play a role in knowledge sharing and collaboration. We compare these empirical properties to those of synthetic bipartite networks and their projections. We show that understanding the bipartite network topology is critical for understanding the potential flow of technological knowledge. Properties of the bipartite structure, such as degree distributions and small cycles, affect the topology of the one-mode projected network—specifically degree and clustering distributions, and degree assortativity. We propose new network-based metrics as a way to quantify how collaborative agents are in the collaboration network. We find that several large corporations are the most collaborative agents in the network; however, such organizations tend to have a low diversity of collaborators. In contrast, the most prolific institutions tend to collaborate relatively little but with a diverse set of collaborators. This indicates that they concentrate the knowledge of their core technical research while seeking specific complementary knowledge via collaboration with smaller institutions.

A performative network theory of attitudes

2020 ◽  
Author(s):  
Michael Quayle
Keyword(s):  
Attitude Change ◽  
Network Structure ◽  
Network Theory ◽  
Social Group ◽  
Group Structure ◽  
Social Identities ◽  
Bipartite Network ◽  
Identity Change ◽  
Multilayer Network ◽  
Dynamic Phenomena

In this paper I propose a network theory of attitudes where attitude agreements and disagreements forge a multilayer network structure that simultaneously binds people into groups (via attitudes) and attitudes into clusters (via people who share them). This theory proposes that people have a range of possible attitudes (like cards in a hand) but these only become meaningful when expressed (like a card played). Attitudes are expressed with sensitivity to their potential audiences and are socially performative: when we express attitudes, or respond to those expressed by others, we tell people who we are, what groups we might belong to and what to think of us. Agreement and disagreement can be modelled as a bipartite network that provides a psychological basis for perceived ingroup similarity and outgroup difference and, more abstractly, group identity. Opinion-based groups and group-related opinions are therefore co-emergent dynamic phenomena. Dynamic fixing occurs when particular attitudes become associated with specific social identities. The theory provides a framework for understanding identity ecosystems in which social group structure and attitudes are co-constituted. The theory describes how attitude change is also identity change. This has broad relevance across disciplines and applications concerned with social influence and attitude change.

What Makes Open Source Software Projects Impactful: A Data-Driven Approach

2020 ◽  
Author(s):  
Huaiwei Yang ◽  
Shuang Liu ◽  
Lin Gui ◽  
Yongxin Zhao ◽  
Jun Sun ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Data Driven ◽  
Software Projects ◽  
Data Driven Approach

scholarly journals The Labor of Maintaining and Scaling Free and Open-Source Software Projects

2021 ◽  
Vol 5 (CSCW1) ◽  
pp. 1-28
Author(s):  
R. Stuart Geiger ◽  
Dorothy Howard ◽  
Lilly Irani
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Projects

scholarly journals Bipartite Network of Interest (BNOI): Extending Co-Word Network with Interest of Researchers Using Sensor Data and Corresponding Applications as an Example

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1668
Author(s):  
Zongming Dai ◽  
Kai Hu ◽  
Jie Xie ◽  
Shengyu Shen ◽  
Jie Zheng ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Extraction Methods ◽  
Knowledge Network ◽  
Sensor Data ◽  
Support Vector ◽  
Bipartite Network ◽  
Classification Models ◽  
Text Data ◽  
Domain Experts ◽  
Problems And Solutions

Traditional co-word networks do not discriminate keywords of researcher interest from general keywords. Co-word networks are therefore often too general to provide knowledge if interest to domain experts. Inspired by the recent work that uses an automatic method to identify the questions of interest to researchers like “problems” and “solutions”, we try to answer a similar question “what sensors can be used for what kind of applications”, which is great interest in sensor- related fields. By generalizing the specific questions as “questions of interest”, we built a knowledge network considering researcher interest, called bipartite network of interest (BNOI). Different from a co-word approaches using accurate keywords from a list, BNOI uses classification models to find possible entities of interest. A total of nine feature extraction methods including N-grams, Word2Vec, BERT, etc. were used to extract features to train the classification models, including naïve Bayes (NB), support vector machines (SVM) and logistic regression (LR). In addition, a multi-feature fusion strategy and a voting principle (VP) method are applied to assemble the capability of the features and the classification models. Using the abstract text data of 350 remote sensing articles, features are extracted and the models trained. The experiment results show that after removing the biased words and using the ten-fold cross-validation method, the F-measure of “sensors” and “applications” are 93.2% and 85.5%, respectively. It is thus demonstrated that researcher questions of interest can be better answered by the constructed BNOI based on classification results, comparedwith the traditional co-word network approach.

The State and Profile of Open Source Software Projects in health and medical informatics

2009 ◽  
Vol 78 (7) ◽  
pp. 457-472 ◽  
Author(s):  
Balaji Janamanchi ◽  
Evangelos Katsamakas ◽  
Wullianallur Raghupathi ◽  
Wei Gao
Keyword(s):  
Open Source ◽  
Medical Informatics ◽  
Open Source Software ◽  
The State ◽  
Software Projects

The Effects of Communication Patterns on the Success of Open Source Software Projects

2016 ◽  
Vol 24 (4) ◽  
pp. 22-44 ◽  
Author(s):  
Jing Wu ◽  
Khim-Yong Goh ◽  
He Li ◽  
Chuan Luo ◽  
Haichao Zheng
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Fixed Effects ◽  
Communication Patterns ◽  
Estimation Model ◽  
Software Projects ◽  
Team Members ◽  
Analysis Methodology ◽  
Model Following ◽  
Time Period

Drawing on the theoretical lens of communication patterns in organizational theory, this research analyzed the longitudinal success of open source software (OSS) projects by employing social network analysis method, based on extensive analyses of empirical data. This study is expected to provide an understanding on how communication patterns established in different roles and different levels. The authors not only measured OSS success from both developers and users' perspectives, but also extended the existing research by including the potential relationships among these success measures in the estimation model. Following the panel data econometric analysis methodology, they evaluated their research hypotheses using the Three-Stage Least Squares model, accounting for both time-period and project fixed effects. The authors' results indicated that according to the objectives of projects, a proper and planned control for the communication among team members is crucial for the success of OSS projects.

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