Traditional Social Network Analysis Research Articles

Mixbiotic society measures: Assessment of community well-going as living system.

Social isolation and fragmentation represent global challenges, with the former stemming from a lack of interaction and the latter from exclusive mobs-both rooted in communication issues. Addressing these challenges, the philosophical realm introduces the concept of the "mixbiotic society." In this framework, individuals with diverse freedoms and values mix together in physical proximity with diverse mingling, recognizing their respective "fundamental incapacities" and uniting in solidarity. This study aims to provide novel measures to balance freedom and solidarity, specifically the intermediate between isolation and mobbing, within a mixbiotic society. To achieve this, we introduce simplified measures to evaluate dynamic communication patterns. These measures complement traditional social network analysis of static structures and conventional entropy-based assessments of dynamic patterns. Our specific hypothesis posits that the measures corresponding to four distinct phases are established by representing communication patterns as multidimensional vectors. These measures include the mean of Euclidean distance to quantify "mobism" for fragmentation, the relative distance change for "atomism" indicating isolation, and a composite measure derived from multiplying the mean and variance of cosine similarity for "mixism," reflecting the well-going state of a mixbiotic society. Additionally, nearly negligible measures correspond to "nihilism." Through the evaluation of seven real-society datasets (high school, primary school, workplace, village, conference, online community, and email), we demonstrate the utility of the "mixism" measure in assessing freedom and solidarity in society. These measures can be employed to typify communities on a radar chart and a communication trajectory graph. The superiority of the measures lies in their ability to evaluate dynamic patterns, ease of calculation, and easily interpretable meanings compared to conventional analyses. As a future development, alongside additional validation using diverse datasets, the mixbiotic society measures will be employed to analyze social issues and applied in the fields of digital democracy and platform cooperativism.

Mixed Methods for Research on Support Networks of People Experiencing Chronic Illness and Social Marginalization.

Substantial research has focused on how social networks help individuals navigate the illness experience. Sociologists have begun to theorize beyond the binary of strong and weak social network ties (e.g., compartmental, elastic, and disposable ties), citing the social, economic, and health conditions that shape their formation. However, limited research has employed mixed social network methods, which we argue is especially critical for examining the "non-traditional" social support networks of marginalized individuals. We employ quantitative social network methods (i.e., the egocentric network approach) in addition to in-depth interviews and observations, with a novel tool for capturing network data about social groups, to surface these kinds of supportive relationships. Using the case of "nameless ties"-non-kin, non-provider ties who were unidentifiable by given name or were grouped by context or activity rather than individually distinguished-we show how mixed social network methods can illuminate supporters who are commonly overlooked when only using traditional social network analysis. We conclude with a proposal for mixed methods and group alter approaches to successfully observe liminal support ties that is ideal for research about individuals experiencing chronic disability, poverty, housing insecurity, and other forms of social marginalization.

Locating community smells in software development processes using higher-order network centralities

Community smells are negative patterns in software development teams’ interactions that impede their ability to successfully create software. Examples are team members working in isolation, lack of communication and collaboration across departments or sub-teams, or areas of the codebase where only a few team members can work on. Current approaches aim to detect community smells by analysing static network representations of software teams’ interaction structures. In doing so, they are insufficient to locate community smells within development processes. Extending beyond the capabilities of traditional social network analysis, we show that higher-order network models provide a robust means of revealing such hidden patterns and complex relationships. To this end, we develop a set of centrality measures based on the MOGen higher-order network model and show their effectiveness in predicting influential nodes using five empirical datasets. We then employ these measures for a comprehensive analysis of a product team at the German IT security company genua GmbH, showcasing our method’s success in identifying and locating community smells. Specifically, we uncover critical community smells in two areas of the team’s development process. Semi-structured interviews with five team members validate our findings: while the team was aware of one community smell and employed measures to address it, it was not aware of the second. This highlights the potential of our approach as a robust tool for identifying and addressing community smells in software development teams. More generally, our work contributes to the social network analysis field with a powerful set of higher-order network centralities that effectively capture community dynamics and indirect relationships.

Comment on the article: The state of social science research on COVID‑19.

A well-written and interesting article was published on November 21, 2021.Future relevant studies, however, may be improved by implementing (1) a framework that outlines the overall research; (2) an author-weighted scheme (AWS) that accurately quantifies the contributions of entities to articles; and (3) a more appropriate size for the nodes representing the proportional counts for each entity in social network analysis (SNA).VOSviewer was used to construct and visualize the scientometric networks and the relation-based analyses included three categories: (1) citation relations, (2) word cooccurrences, and (3) coauthorship relations. Nevertheless, the counts for each topical entity have not been consistently integrated.As a result, the nodes of the keyword co-occurrence network are large when compared to the number of connections between the entities or terms (i.e., the total number of relationships between co-occurring terms or entities). Additionally, all weighted counts in keywords (or the total link strength of a country/region) should equal the total number of documents (e.g., n = 9954 in that article).This would lead to biases in the calculation of publications (or citations) for entities, as is common in traditional SNA.This node illustrates a study framework and a couple of AWSs (i.e., equal and nonequal AWSs) to improve the article, and discusses the need to understand the requirement that the total centrality degree in SNA equals the total number of documents (or citations).

The Whole Is Greater than the Sum of the Parts: A Multilayer Approach on Criminal Networks

Traditional social network analysis can be generalized to model some networked systems by multilayer structures where the individual nodes develop relationships in multiple layers. A multilayer network is called multiplex if each layer shares at least one node with some other layer. In this paper, we built a unique criminal multiplex network from the pre-trial detention order by the Preliminary Investigation Judge of the Court of Messina (Sicily) issued at the end of the Montagna anti-mafia operation in 2007. Montagna focused on two families who infiltrated several economic activities through a cartel of entrepreneurs close to the Sicilian Mafia. Our network possesses three layers which share 20 nodes. The first captures meetings between suspected criminals, the second records phone calls and the third detects crimes committed by pairs of individuals. We used measures from multilayer network analysis to characterize the actors in the network based on their local edges and their relevance to each specific layer. Then, we used measures of layer similarity to study the relationships between different layers. By studying the actor connectivity and the layer correlation, we demonstrated that a complete picture of the structure and the activities of a criminal organization can be obtained only considering the three layers as a whole multilayer network and not as single-layer networks. Specifically, we showed the usefulness of the multilayer approach by bringing out the importance of actors that does not emerge by studying the three layers separately.

Using Three Social Network Analysis Approaches to Understand Computer-Supported Collaborative Learning

Sharing the same philosophy of “relations matter” with computer-supported collaborative learning (CSCL), social network analysis (SNA) has become a common methodology in the CSCL research. In this research, I use SNA methods from relational ties, network modes, and integrated methods perspectives to understand attributes of relations in CSCL. I design, conduct, and evaluate three SNA analytics on the same dataset from an online course to understand CSCL entities, relations, and processes. This online collaborative discussion in this course stresses students’ knowledge inquiry, construction, and building through peer interactions. Results show that compared to traditional SNA methods, these three SNA approaches can reveal more detailed, richer picture of the collaborative learning processes, particularly, the interactional, multi-modal, and temporal aspects. Moreover, these SNA approaches are generalizable for understanding similar CSCL settings. Based on the results, this research proposes methodological implications to further apply and develop SNA in the CSCL field.

Evolutionary dynamics modeling of symbolic social network structure equilibrium

The use of symbol attributes on the side of symbolic social networks to analyze, understand, and predict the topology, function, and dynamic behaviour of complex networks, and has important theoretical significance for personalized recommendations, attitude prediction, user feature analysis, and clustering and application value. However, due to the huge scale of online social networks, this poses a challenge to traditional symbolic social network analysis methods. Based on the theory of structural equilibrium, this paper studies the evolutionary dynamics of symbolic social networks, proposes the energy function of weak structural equilibrium theory, and uses the evolution of evolutionary algorithms to obtain the weak imbalance of the network. The simulation experiment results show that the calculation method in this paper can get the optimal solution faster. It provides an idea for the study of real and complex social networks.

Tourists’ digital footprint: The spatial patterns of tourist flows in Qingdao, China

Spatial patterns of tourist flows represent the movement of tourists and show differences in tourism resources giving advice for promoting balanced and sustainable tourism development. This paper proposes a novel framework for analyzing these patterns based on tourists' digital footprint data collected from online travel diaries. Based on illustrative case study data from Qingdao (China), the framework, combining traditional quantitative and social network analysis, is able to pinpoint: (1) The influence of distance decay and attractions’ popularity on the spatial patterns of tourist flows; (2) The uneven distribution of the core tourist nodes and the existence of the structural hole phenomenon, which form a network pattern with unbalanced power and intense internal competition; (3) The formation of the core area for tourism along the coastline – as is typical for coastal tourism cities. This difference of tourism resources between coastal and inland areas, thus, remains a challenge for future tourism development in Qingdao.

Efficiency in knowledge transmission in R&D project networks: European renewable energy sector

This study analyses the efficiency in knowledge transmission of organizations and local regions participating in European R&D projects in 2000–2013 within renewable energy (RE) sectors (wind, solar, sea, geothermal, and biomass) using social network analysis (SNA). A review of the collaborative R&D consortium networks as technological transfer structures and public policy support issues was carried out. Then, not only is the traditional SNA centrality perspective of actors employed to identify key players who bridge less connected areas, but also the structural hole approach is applied based on the relative position, role, and potential redundancy of collaborations from the overall network perspective. It reveals that networks of organizations and local regions are neither completely random nor homogenous in terms of cohesion and efficiency. Additionally, the existence of areas between core and peripheral nodes (structural holes) is confirmed. Higher education and research centers, which show a greater influential position and higher experience, take advantage of them. Research concludes that the efficiency in terms of knowledge transmission is not always positively correlated with high centrality values. The most emergent RE sectors still appear less efficient according to the rankings produced integrating both approaches. This paper makes a novel academic contribution to RE policy makers since a new application of centrality and efficiency perspectives is applied. As a result, policy makers should consider it in detail when designing public RE projects with the aim of building an efficient European Research Area.

On Direct vs. Indirect Peer Influence in Large Social Networks

With the availability of large-scale network data, peer influence in social networks can be more rigorously examined and understood than before. Peer influence can arise from immediate neighbors in the network (formally defined as cohesion or direct ties with one-hop neighbors) and from indirect peers who share common neighbors (formally defined as structural equivalence or indirect ties with two-hop neighbors). While the literature examined the role of each peer influence (direct or indirect) separately, the study of both peer network effects acting simultaneously was ignored, largely because of methodological constraints. This paper attempts to fill this gap by evaluating the simultaneous effect of both direct and indirect peer influences in technology adoption in the context of caller ringback tone (CRBT) in a cellular telephone network, using data from 200 million calls by 1.4 million users. Given that such a large-scale network makes traditional social network analysis intractable, we extract many densely connected and self-contained subpopulations from the network. We find a regularity in these subpopulations in that they consist either of about 200 nodes or about 500 nodes. Using these subpopulations and panel data, we analyze direct and indirect peer influences using a novel autoprobit model with multiple network terms (direct and indirect peer influence, with homophily as a control variable). Our identification strategy relies on a newly designed spatial autoregressive model, allowing us to identify the direct and indirect peer influences on each of the extracted subpopulations. We use meta-analysis to summarize the estimated parameters from all subpopulations. The results show CRBT adoption to be simultaneously determined by both direct and indirect peer influence (while controlling for homophily and centrality). Robustness checks show model fit to improve when both peer influences are included. The size and direction of the two peer influences, however, differ by group size. Interestingly, indirect peer influence (structural equivalence) plays a negative role in diffusion when group size is about 200, but a positive role when group size is about 500. The role of direct peer influence (cohesion), on the other hand, is always positive, irrespective of group size. Our findings imply that businesses must design different target strategies for large versus small groups: for large groups, businesses should focus on consumers with both multiple one-hop and two-hop neighbors; for small groups, businesses should only focus on consumers with multiple one-hop neighbors. The online appendix is available at https://doi.org/10.1287/isre.2017.0753 .

Statistical mechanics approach for collaborative business social network reconstruction

The role of human resources has become a key factor for the success of an organization. Based on a research collaboration with an aeronautical company, the paper compares two different approaches for the reconstruction of a collaborative social network in the business realm. Traditional social network analysis and novel statistical inference models were both evaluated against data provided by the company, with the final scope of scouting key employees in the network, as well as exploiting the knowledge-transfer processes. As a main outcome of this paper, it was found how the network reconstruction using statistical models has an increased robustness, as well as sensitivity, allowing to discover hidden correlations among the users.

300 Cities - An Exploration in Characterizing US Cities

Abstract : The goal of the 300-Cities Project is to support IRS policy decisions by finding a small number of city clusters, where the cities within each cluster will respond similarly to IRS interventions. This report describes two types of analyses based on U.S. Census 2000 data. The first is an agent-class analysis. In this analysis city clustering operations are based on the correspondence of population profiles for pairs of cities. Extensive effort using this analysis framework in conjunction with the SAS statistical package demonstrates that although the framework is conceptually straightforward, it is computationally impractical and conceptually impoverished. The second analysis framework, the city-matching analysis, combines city summary and population heterogeneity metrics with information access constraints and taxpayer categories to create a city-matching index for each pair of cities. The city-matching analysis thus shifts the basis of analysis from a city's population profile to its information diffusion characteristics, and provides "hooks" to IRS classification schemes to make the findings more actionable. City clustering operations in this framework are based on city-matching indices, which were analyzed by traditional social network analysis techniques using the Organizational Risk Analyzer (ORA). Although the issue of how best to integrate the various components of the city-match index remain unresolved, exploratory results show promise by yielding actionable city clusters. The city clusters, however, only account for 95 of the 297 cities in the Census 2000 data. Together, the two analysis frameworks raise questions as to whether canonical city types exist.

Using Interest Graphs to Predict Rich-Media Diffusion in Content-Based Online Social Networks

Rich-media, pictures, and videos, are becoming an increasingly important aspect of online social networks. Unlike social networks, where users are connected primarily because of being friends, peers, or co-workers, content-based networks build connections between individuals founded on a shared interest in rich-media content. In this study, “interest-graphs” comprised of these content-based connections were examined. As shown, interest graph analysis provides important advantages over traditional social network analysis to identify valuable network members and predicting rich-media diffusion.

An Empirical Research on Fuzzy Alliance Network of Chinese Automobile Industry

The factors that influence enterprise innovation are complex and vague under the increasingly intense market competition and rapidly developed technology. Traditional social network analysis in inter-firm alliance network can not meet requirements of finding the relationship between affiliation and technological innovation precisely. To fill up this gap, in this paper we apply fuzzy theory to analysis of inter-firm alliance network by constructing alliance network using data of collaboration time span instead of binary data. Then we use fuzzy social network analysis to analyze the fuzzy centrality. By doing these analysis, we expand the research on inter-firm alliance network and give a new insight into enterprise innovation.

Towards a Methodology for Validation of Centrality Measures in Complex Networks

BackgroundLiving systems are associated with Social networks — networks made up of nodes, some of which may be more important in various aspects as compared to others. While different quantitative measures labeled as “centralities” have previously been used in the network analysis community to find out influential nodes in a network, it is debatable how valid the centrality measures actually are. In other words, the research question that remains unanswered is: how exactly do these measures perform in the real world? So, as an example, if a centrality of a particular node identifies it to be important, is the node actually important?PurposeThe goal of this paper is not just to perform a traditional social network analysis but rather to evaluate different centrality measures by conducting an empirical study analyzing exactly how do network centralities correlate with data from published multidisciplinary network data sets.MethodWe take standard published network data sets while using a random network to establish a baseline. These data sets included the Zachary's Karate Club network, dolphin social network and a neural network of nematode Caenorhabditis elegans. Each of the data sets was analyzed in terms of different centrality measures and compared with existing knowledge from associated published articles to review the role of each centrality measure in the determination of influential nodes.ResultsOur empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes.

Semantic and Time-Dependent Expertise Profiling Models in Community-Driven Knowledge Curation Platforms

Online collaboration and web-based knowledge sharing have gained momentum as major components of the Web 2.0 movement. Consequently, knowledge embedded in such platforms is no longer static and continuously evolves through experts’ micro-contributions. Traditional Information Retrieval and Social Network Analysis techniques take a document-centric approach to expertise modeling by creating a macro-perspective of knowledge embedded in large corpus of static documents. However, as knowledge in collaboration platforms changes dynamically, the traditional macro-perspective is insufficient for tracking the evolution of knowledge and expertise. Hence, Expertise Profiling is presented with major challenges in the context of dynamic and evolving knowledge. In our previous study, we proposed a comprehensive, domain-independent model for expertise profiling in the context of evolving knowledge. In this paper, we incorporate Language Modeling into our methodology to enhance the accuracy of resulting profiles. Evaluation results indicate a significant improvement in the accuracy of profiles generated by this approach. In addition, we present our profile visualization tool, Profile Explorer, which serves as a paradigm for exploring and analyzing time-dependent expertise profiles in knowledge-bases where content evolves overtime. Profile Explorer facilitates comparative analysis of evolving expertise, independent of the domain and the methodology used in creating profiles.

사회관계망에서 중요 노드 식별을 위한 지역정보 기반 매개 중심도

In traditional social network analysis, the betweenness centrality measure has been heavily used to identify the relative importance of nodes in terms of message delivery. Since the time complexity to calculate the betweenness centrality is very high, however, it is difficult to get it of each node in large-scale social network where there are so many nodes and edges. In this paper, we define a new type of network, called the expanded ego network, which is built only with each node’s local information, i.e., neighbor information of the node’s neighbor nodes, and also define a new measure, called the expended ego betweenness centrality. Through the intensive experiment with Barabasi-Albert network model to generate the scale-free networks which most social networks have as their embedded feature, we also show that the nodes’ importance rank based on the expanded ego betweenness centrality has high similarity with that based on the traditional betweenness centrality. Keywords:Social Network Analysis, Betweenness Centrality, Local Information, Expanded Ego Network

Social Network Analysis in Enterprise

Social network analysis (SNA) has been a research focus in multiple disciplines for decades, including sociology, healthcare, business management, etc. Traditional SNA researches concern more human and social science aspects—trying to undermine the real relationship of people and the impacts of these relationships. While online social networks have become popular in recent years, social media analysis, especially from the viewpoint of computer scientists, is usually limited to the aspects of people's behavior on specific websites and thus are considered not necessarily related to the day-to-day people's behavior and relationships. We conduct research to bridge the gap between social scientists and computer scientists by exploring the multifacet existing social networks in organizations that provide better insights on how people interact with each other in their professional life. We describe a comprehensive study on the challenges and solutions of mining and analyzing existing social networks in enterprise. Several aspects are considered, including system issues; privacy laws; the economic value of social networks; people's behavior modeling including channel, culture, and social inference; social network visualization in large-scale organization; and graph query and mining. The study is based on an SNA tool (SmallBlue) that was designed to overcome practical challenges and is based on the data collected in a global organization of more than 400 000 employees in more than 100 countries.

Combined methods, thick descriptions: Languages of collaboration on Github

AbstractLike many professional work activities in this age of ubiquitous computing and high‐speed internet connections, computer programming and software development are increasingly mediated by systems with ‘social media’ features like profiles, avatars, ‘liking’, and commenting capabilities. When working on shared tasks, programmers have effectively leveraged these capabilities to overcome differences in time and location while simultaneously using collaborative web applications, such as version control repositories like SCM or ‘git’ systems to work together more efficiently. Here we present preliminary findings from a project investigating patterns of collaboration on the social coding platform Github. We've used a research method that combines the use of statistical approaches from social network analysis (SNA) and traditional qualitative case study construction. Our results show that this method is useful in qualitatively explaining the topology of a collaborative network, especially the formation of cliques that have been identified using traditional SNA metrics.

Analysis of evolving team interactions in dynamic targeting

Command and Control (C2) enables a commander to recognise what objectives to achieve and is the means to ensure that appropriate actions are taken. Important insights can be gained by studying the complex patterns of interactions in military headquarters. Since traditional social network analysis (SNA) quantifies social interactions in terms of network theory without associated contextual information, we have developed a tool, called SNA of C2 (SNAC2), to exploit captured information of an evolving social network. Features include contextual marking–up of data, tagging with meta–data, the ability to categorise events into phases for in–depth analysis and enhanced metrics allowing quantitative temporal analysis of the social network. SNAC2 is applied to analysing dynamic targeting, a process used in the air and space operations centre (AOC) to engage time–sensitive targets (TSTs). The rich data captured could be used to expose process chokepoints, inefficient work practices and inappropriate workspace design.