Analysis of virtual communities supporting OSS projects using social network analysis

This paper analyses the behaviour of virtual communities for Open Source Software (OSS) projects. The development of OSS projects relies on virtual communities, which are built on relationships among members, being their final objective sharing knowledge and improving the underlying project. This study addresses the interactive collaboration in these kinds of communities applying social network analysis (SNA). In particular, SNA techniques will be used to identify those members playing a middle-man role among other community members. Results will illustrate the importance of this role to achieve successful virtual communities.     

Self-organization patterns in wasp and open source communities

In this paper, we conducted a comparative study of how social organization takes place in a wasp colony and OSS developer communities. Both these systems display similar global organization patterns, such as hierarchies and clear labor divisions. As our analysis shows, both systems also define interacting agent networks with similar common features that reflect limited information sharing among agents. As far as we know, this is the first research study analyzing the patterns and functional significance of these systems' weighted-interaction networks. By illuminating the extent to which self-organization is responsible for patterns such as hierarchical structure, we can gain insight into the origins of organization in OSS communities.     

What sort of community is the European Conference on Information Systems? A social network analysis 1993–2005

This paper presents a social network analysis (SNA) of the European Conference on Information Systems (ECIS) community based on patterns of co-authorship. ECIS contributions are separated into research papers and panels to create social networks that are then analyzed using a range of global network level and individual ego (co-author, panellist) measures. The research community is found to have few properties of the ‘small world’ and to represent an agglomeration of co-authorships. The panels network has the properties of a ‘small world’ and displays a stronger sense of social cohesion. An analysis of individual actors (egos) provides insight into who is central to the ECIS community. Based on the SNA, a range of possible interventions are proposed that could aid the future development of the ECIS community. The paper concludes by considering the usefulness of SNA as a method to support IS research.     

大数据环境下精准客户定位的社交网络分析

大数据为企业进行精准营销提供了重要支撑,精准营销能提升营销效果,提高客户满意度,精准营销的前提是客户识别与选择。通过分析网络个体与群体特征,社交网络分析能够定位核心价值客户。首先对社交网络的中心性进行分析,探讨社交网络节点地位与营销效果的关系,运用社群识别方法,对社交网络进行分群,提出并用MapReduce实现了针对大规模社交网络的社群划分RMCL方法。在此基础上,构建了客户影响度与客户影响因子等指标,并结合中心度指标,定位社群的核心节点,并采用分类回归树方法,研究了社交网络结构与客户消费响应关系,并确定了变量重要性,为企业采取客户差异化营销组合策略提供指导。     

一种Twitter社区中社会网络分析方法

选取Twitter中文社区作为研究对象,提出一种社会网络分析方法。从微博客中用户之间的关注关系以及信息传播途径出发,采用社会网络分析方法,对微博客中的社会网络结构进行量化分析,研究其社会网络的密度、中心性、位置和角色。分析结果表明,该方法能避开信息碎片化、负面消息爆炸性传播等难题,可应用于有关国家安全问题的监控。     

Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis

The focus of this study is to explore the advances that Social Network Analysis (SNA) can bring, in combination with other methods, when studying Networked Learning/Computer-Supported Collaborative Learning (NL/CSCL). We present a general overview of how SNA is applied in NL/CSCL research; we then go on to illustrate how this research method can be integrated with existing studies on NL/CSCL, using an example from our own data, as a way to synthesize and extend our understanding of teaching and learning processes in NLCs. The example study reports empirical work using content analysis (CA), critical event recall (CER) and social network analysis (SNA). The aim is to use these methods to study the nature of the interaction patterns within a networked learning community (NLC), and the way its members share and construct knowledge. The paper also examines some of the current findings of SNA analysis work elsewhere in the literature, and discusses future prospects for SNA. This paper is part of a continuing international study that is investigating NL/CSCL among a community of learners engaged in a master’s program in e-learning.     

基于LDA模型的社交网络主题社区挖掘

以微博为代表的社交网络已成为社会舆情的战略要地。对于社交网络中隐含主题社区的发掘,具有较高的商业推广和舆情监控价值。近年来,概率生成主题模型LDA(Latent Dirichlet Allocation)在数据挖掘领域得到了广泛应用。但是,一般而言,LDA适用于处理文本、数字信号数据,并不能合理地用来处理社交网络用户的关系数据。对LDA进行修改,提出适用于处理用户关系数据的Tri-LDA模型,挖掘社交网络中的主题社区。实验结果表明,基于Tri-LDA模型,进行机器学习所得到的结果基本能够反映社交网络上真实的主题社区分布情况。     

Parallel quantum-inspired evolutionary algorithms for community detection in social networks

The world around us may be viewed as a network of entities interconnected via their social, economic, and political interactions. These entities and their interactions form a social network. A social network is often modeled as a graph whose nodes represent entities, and edges represent interactions between these entities. These networks are characterized by the collective latent behavior that does not follow trivially from the behaviors of the individual entities in the network. One such behavior is the existence of hierarchy in the network structure, the sub-networks being popularly known as communities. Discovery of the community structure in a social network is a key problem in social network analysis as it refines our understanding of the social fabric. Not surprisingly, the problem of detecting communities in social networks has received substantial attention from the researchers.In this paper, we propose parallel implementations of recently proposed community detection algorithms that employ variants of the well-known quantum-inspired evolutionary algorithm (QIEA). Like any other evolutionary algorithm, a quantum-inspired evolutionary algorithm is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, individual bits called qubits, are in a superposition of states. As chromosomes evolve individually, the quantum-inspired evolutionary algorithms (QIEAs) are intrinsically suitable for parallelization.In recent years, programmable graphics processing units — GPUs, have evolved into massively parallel environments with tremendous computational power. NVIDIA® compute unified device architecture (CUDA®) technology, one of the leading general-purpose parallel computing architectures with hundreds of cores, can concurrently run thousands of computing threads. The paper proposes novel parallel implementations of quantum-inspired evolutionary algorithms in the field of community detection on CUDA-enabled GPUs.The proposed implementations employ a single-population fine-grained approach that is suited for massively parallel computations. In the proposed approach, each element of a chromosome is assigned to a separate thread. It is observed that the proposed algorithms perform significantly better than the benchmark algorithms. Further, the proposed parallel implementations achieve significant speedup over the serial versions. Due to the highly parallel nature of the proposed algorithms, an increase in the number of multiprocessors and GPU devices may lead to a further speedup.     

Balancing systematic and flexible exploration of social networks

Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively. We present SocialAction, a system that uses attribute ranking and coordinated views to help users systematically examine numerous SNA measures. Users can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix overview. For each operation, a stable node layout is maintained in the network visualization so users can make comparisons. SocialAction offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks     

Markov-network based latent link analysis for community detection in social behavioral interactions

How to represent and discover social links from the perspective of implied behaviors, in particular latent links, is critical for social media analysis. In this paper, we discuss latent link analysis for community detection in social behavioral interactions. We adopt Markov network (MN) as the framework and propose the algorithm to discover latent links among social objects implied in their behavioral interactions without regard for the topological structures of social networks. First, starting from the frequent itemsets of the behavioral interactions, we propose the algorithm to construct the item-association Markov network (IAMN), which establishes the inherent relationship between frequent itemset and MN. Then, we propose the algorithm to detect communities by incorporating the concepts of k-clique and k-nearest neighbor set, as the typical application of the constructed IAMN Experimental results show the effectiveness and efficiency of the method proposed in this paper.     

A traffic analysis attack to compute social network measures

The development of digital media, the increasing use of social networks, the easier access to modern technological devices, is perturbing thousands of people in their public and private lives. People love posting their personal news without consider the risks involved. Privacy has never been more important. Privacy enhancing technologies research have attracted considerable international attention after the recent news against users personal data protection in social media websites like Facebook. It has been demonstrated that even when using an anonymous communication system, it is possible to reveal user’s identities through intersection attacks or traffic analysis attacks. Combining a traffic analysis attack with Analysis Social Networks (SNA) techniques, an adversary can be able to obtain important data from the whole network, topological network structure, subset of social data, revealing communities and its interactions. The aim of this work is to demonstrate how intersection attacks can disclose structural properties and significant details from an anonymous social network composed of a university community.

     

虚拟社群结构特征可视化呈现的社会网络布局算法研究

近年来,随着虚拟社区的发展,社会网络可视化软件逐渐走向普通社区成员的面前。然而现今社会网络可视化领域所采用的布局算法普遍与社会网络分析法相脱离,无法呈现社群结构特征。因此,提出凝聚子群布局算法与核心位置布局算法,它们分别以凝聚子群分析结果和成员整体中心度为布局依据,呈现社群子群和成员位置两种社群结构特征,并且依据实际数据给出布局效果。     

Diffusion dynamics of open source software: An agent-based computational economics (ACE) approach

The rising popularity of open source software (OSS) calls for a better understanding of the drivers of its adoption and diffusion. In this research, we propose an integrated framework that simultaneously investigates a broad range of social and economic factors on the diffusion dynamics of OSS using an Agent Based Computational Economics (ACE) approach. We find that interoperability costs, variability of OSS support costs, and duration of PS upgrade cycle are major determinants of OSS diffusion. Furthermore, there are interaction effects between network topology, network density and interoperability costs, which strongly influence the diffusion dynamics of OSS. The proposed model can be used as a building block to further investigate complex competitive dynamics in software markets.     

Learning and best practices for learning in open-source software communities

This research is about participants who use open-source software (OSS) discussion forums for learning. Learning in online communities of education as well as non-education-related online communities has been studied under the lens of social learning theory and situated learning for a long time. In this research, we draw parallels among these two types of communities and explore what can be learned from open-source software communities about online learning. Thematic network analysis was used to code the qualitative data from the open-ended questions in the survey and the interviews. The results indicate that learning in online open-source software communities encompasses much more than just learning about the software being discussed. 283 Open-source forum participants were surveyed, and 21 were interviewed to develop an understanding of the challenges to learning in these communities as well as to identify the practices that promote learning. Identifying these practices helps to understand online learning and enables the integration of best practices into online education.     

社交网络项目推荐模型及应用研究

将社交网络中目标用户和朋友之间相同兴趣产生的原因解释为潜在因子空间中的潜在因子,对社交网络中目标用户和朋友用户共同兴趣进行潜在因子分析,构建基于用户朋友关系的社交网络项目推荐模型,预测社交网络目标用户喜欢的项目。将基于社交网络项目推荐模型应用于实际应用场景中,研究表明与基于协同过滤技术的推荐方法相比较,该模型能够显著提高推荐质量,并具有良好的可扩展性。     

融合社会网络与信任度的个性化推荐方法研究

针对协同推荐技术存在的数据稀疏性和恶意评价行为等问题, 提出了一种新颖的基于社会网络的协同过滤推荐算法。该方法借助社会网络分析技术对协同推荐方法加以改进, 结合用户信任关系与用户自身兴趣, 通过计算网络节点的可信度来消减虚假评分或恶意评分给推荐系统带来的负面影响, 从而提高了推荐系统的准确度。实验表明, 相对于传统的协同过滤算法, 该算法可以有效缓解用户评分稀疏性及恶意评价行为带来的问题, 显著提高推荐系统的推荐质量。     

Social network analysis for optimal machining conditions in ultra-precision manufacturing

Ultra-precision machining (UPM) technology is extensively applied to manufacture top quality products with high precision level and complicated geometry. As complicated machining factors affect the surface quality of machined components in UPM, large numbers of experiments for understanding the influences from particular machining factors are needed, leading overestimate or underestimate of significance of machining factors at certain machining conditions and raising of experimental cost. For these reasons, a crucial approach is urged to adapt for providing a fast track to an optimal machining condition. In this study, social network analysis (SNA) is introduced firstly to develop UPM network, which the network shows the relationship between dominant machining factors in UPM. A complicated UPM network containing interdependencies between each machining factor is generated by SNA. The determinations of network metrics in the UPM network support the selection of optimal machining factors under various machining conditions. Furthermore, the constructed UPM network using SNA provides the complete framework of dependencies in UPM for well predicting the machining outcomes when particular machining factors are adjusted in practical situations. The study contributes to offering a detail guideline for constructing machining strategies or experimental plans to efficiently achieve desired machining outcomes.     

Newcomers’ Barriers. . . Is That All? An Analysis of Mentors’ and Newcomers’ Barriers in OSS Projects

Newcomers’ seamless onboarding is important for open collaboration communities, particularly those that leverage outsiders’ contributions to remain sustainable. Nevertheless, previous work shows that OSS newcomers often face several barriers to contribute, which lead them to lose motivation and even give up on contributing. A well-known way to help newcomers overcome initial contribution barriers is mentoring. This strategy has proven effective in offline and online communities, and to some extent has been employed in OSS projects. Studying mentors’ perspectives on the barriers that newcomers face play a vital role in improving onboarding processes; yet, OSS mentors face their own barriers, which hinder the effectiveness of the strategy. Since little is known about the barriers mentors face, in this paper, we investigate the barriers that affect mentors and their newcomer mentees. We interviewed mentors from OSS projects and qualitatively analyzed their answers. We found 44 barriers: 19 that affect mentors; and 34 that affect newcomers (9 affect both newcomers and mentors). Interestingly, most of the barriers we identified (66%) have a social nature. Additionally, we identified 10 strategies that mentors indicated to potentially alleviate some of the barriers. Since gender-related challenges emerged in our analysis, we conducted nine follow-up structured interviews to further explore this perspective. The contributions of this paper include: identifying the barriers mentors face; bringing the unique perspective of mentors on barriers faced by newcomers; unveiling strategies that can be used by mentors to support newcomers; and investigating gender-specific challenges in OSS mentorship. Mentors, newcomers, online communities, and educators can leverage this knowledge to foster new contributors to OSS projects.     

Social networks,communication styles,and learning performance in a CSCL community

The aim of this study is to empirically investigate the relationships between communication styles, social networks, and learning performance in a computer-supported collaborative learning (CSCL) community. Using social network analysis (SNA) and longitudinal survey data, we analyzed how 31 distributed learners developed collaborative learning social networks, when they had work together on the design of aerospace systems using online collaboration tools. The results showed that both individual and structural factors (i.e., communication styles and a pre-existing friendship network) significantly affected the way the learners developed collaborative learning social networks. More specifically, learners who possessed high willingness to communicate (WTC) or occupied initially peripheral network positions were more likely to explore new network linkages. We also found that the resultant social network properties significantly influenced learners’ performance to the extent that central actors in the emergent collaborative social network tended to get higher final grades. The study suggests that communication and social networks should be central elements in a distributed learning environment. We also propose that the addition of personality theory (operationalized here as communication styles) to structural analysis (SNA) contributes to an enhanced picture of how distributed learners build their social and intellectual capital in the context of CSCL.     

Virtual communities as a resource for the development of OSS projects: the case of Linux ports to embedded processors

Open source software (OSS) projects represent a new paradigm of software creation and development based on hundreds or even thousands of developers and users organised in the form of a virtual community. The success of an OSS project is closely linked to the successful organisation and development of the virtual community of support. The main objective of this article is to analyse the activity of virtual communities. Social network analysis is employed to analyse Linux ports to embedded processors as a case study to achieve this aim. The obtained results confirm the necessity of structuring the virtual community with a selection of active developers and core members to promote community activity and attract peripheral users, expanding the impact of the underlying software. The obtained result will be useful for the software industry migrating to the open source software paradigm.