Affiliation Network Analysis Research Articles

Entre conselhos e conferências: as arenas deliberativas sobre direitos humanos em Minas Gerais

ABSTRACT Introduction: The academic literature on deliberative systems suggests exploring the connections between deliberative forums and other components of the political system and society. This article investigates the participation of governmental actors and civil society in councils and conferences addressing public policies on human rights in the state of Minas Gerais, Brazil. The objectives are twofold: to propose a theoretical framework for examining deliberative subsystems and to identify the connectors in a multilevel network consisting of human rights councils and conferences. Materials and methods: We adopted a methodology centered on subsystem theory, prioritizing the dynamics of deliberation arenas and the synergy between councils at local and state levels. We reinterpreted the concept of subsystem to develop an empirical model that incorporates its thematic dimension. To examine the interconnections between councils and conferences, we employed an analytical approach known as two mode network analysis, also referred to as affiliation network analysis, situated in the domain of Social Network Analysis. Results: Our findings suggest a high level of integration within the subsystem. In contrast to prior studies, we found that civil society actors, alongside governmental agents, significantly contribute to interconnecting various arenas within the subsystem. This finding underlines the functional specialization of liaison agents within the context of the deliberation system. Discussion: Our findings offer a twofold contribution to the existing literature. Firstly, we present an innovative theoretical model for analyzing deliberative systems. Secondly, we recommend the use of multilevel network analysis as a robust tool for identifying connections between multiple arenas.

Modeling and analysis of affiliation networks with preferential attachment and subsumption.

Preferential attachment describes a variety of graph-based models in which a network grows incrementally via the sequential addition of new nodes and edges, and where existing nodes acquire new neighbors at a rate proportional to their degree. Some networks, however, are better described as groups of nodes rather than a set of pairwise connections. These groups are called affiliations, and the corresponding networks affiliation networks. When viewed as graphs, affiliation networks do not necessarily exhibit the power law distribution of node degrees that is typically associated with preferential attachment. We propose a preferential attachment mechanism for affiliation networks that highlights the power law characteristic of these networks when presented as hypergraphs and simplicial complexes. The two representations capture affiliations in similar ways, but the latter offers an intrinsic feature of the model called subsumption, where an affiliation cannot be a subset of another. Our model of preferential attachment has interesting features, both algorithmic and analytic, including implicit preferential attachment (node sampling does not require knowledge of node degrees), a locality property where the neighbors of a newly added node are also neighbors, the emergence of a power law distribution of degrees (defined in hypergraphs and simplicial complexes rather than at a graph level), implicit deletion of affiliations (through subsumption in the case of simplicial complexes), and to some extent a control over the affiliation size distribution. By varying the parameters of the model, the generated affiliation networks can resemble different types of real-world examples, so the framework also serves as a synthetic generation algorithm for simulation and experimental studies.

Evaluating Advancements in Forensic Medicine and Toxicology Through a Scientometric Mapping

The study focused on using scientometric methods to analyze forensic medicine and toxicology literature. The selected period for the study was from 2013 to 2022, allowing for a recent and comprehensive field analysis. The study used bibliographic data from a PubMed database, a widely used biomedical literature database. The study aimed to provide a comprehensive picture of the production and dissemination of the literature in the field. To achieve this, the study analyzed various bibliometric indicators, including theme analysis, keyword evolvement, and collaboration map. The analysis of trending topics and future thematic directions in the field was also conducted to provide insight that could inform and stimulate the research interests of young researchers. This scholarly article explains the construction of author networks, the assessment of author productivity yearly, and the analysis of affiliation networks and organizations. The number of articles published by an institution is an essential indicator of research productivity and expertise in the particular field. The study's findings provided valuable insights into the trends and patterns in forensic medicine and toxicology.

Distributed Approaches to Butterfly Analysis on Large Dynamic Bipartite Graphs

Tip decomposition has a pivotal role in mining cohesive subgraphs in bipartite graphs by computing the tip number of each vertex in accordance with the non-trivial motif butterfly ((2,2)-biclique). It has been a popular research topic with applications in document clustering, spam group detection, and analysis of affiliation networks. In such applications, the graphs are not only large, but they evolve quickly with new edges being continuously added/deleted. While existing centralized techniques could solve the tip decomposition problem for static bipartite graphs, they are not efficient for maintaining the tip numbers of vertices on large-scale graphs. In this paper, we study butterfly analysis problems on bipartite graphs in a distributed environment with the vertex-centric model. We first extend a centralized butterfly counting algorithm to a distributed version, called DBCA. An ingenious message aggregation strategy is designed to reduce massive redundant messaging and avoid the memory overflow problem while processing large-scale graphs. Based on the results of DBCA, we develop a distributed tip decomposition algorithm (DTDA) to get the tip number of each vertex in parallel. Finally, to maintain the tip numbers of vertices efficiently while graphs evolve over time, we explore a distributed tip maintenance algorithm (DTMA) along with a novel task-split strategy. Specifically, for an updated edge (insertion/deletion), several sub-tasks will be generated in line with the topology structure of the original bipartite graph. To our best knowledge, this is the first study to process the butterfly analysis problems in a distributed environment. In addition, comprehensive experiments have been conducted on real-world bipartite graphs. The experiment results demonstrate that our proposed algorithms are efficient and scalable.

The social nature of New Education: an affiliation network analysis of the movement’s evolution, 1875–1935

ABSTRACT The New Education movement was a remarkable coalition of national reform movements that emerged at the turn of the twentieth century. As a heterogeneous movement that was united only in its opposition to the schooling system at the time, its structure and boundaries in the UK have remained a matter of academic debate. This article implements the previously proposed idea of treating New Education as a social movement and analysing the networks between reformers. A social network analysis of the central 58 reformers suggests that the movement was initially divided into two ideologically separate subgroups, but that from ca. 1905 onwards the reformers were strongly interconnected despite their different interests. Thus, by focusing on the social structure of the movement’s core rather than its educational thought, the article challenges the assumption that the New Education movement was fragmented and characterised by tensions. It also furthers the discussion on the feasibility of social network analysis for studying educational reforms.

Parent Engagement with a Self-Tailored Cancer Prevention Digital Behavior Change Intervention: Exploratory Application of Affiliation Network Analysis.

Evaluating digital behavior change intervention engagement is complex and requires multidimensional and novel approaches that are emerging. The relationship and interdependence between engagement with the technology and engagement with the psychosocial or behavior change process often presents conceptual and evaluative challenges. Large objective data sets detailing technology use are plentiful but meaningful interpretation can be challenging at granular levels. Affiliation network analysis which describes two-mode network data may provide a novel approach to evaluate engagement of digital behavior change interventions. The purpose of this paper is to use affiliation network analysis as an exploratory method to describe, assess and visualize content-specific patterns underlying psychosocial characteristics related to HPV vaccine safety concerns of parents using the HPVcancerFree intervention. Results indicate that affiliation network analysis shows promise in supplementing existing methods to assess engagement of digital interventions.

Distributed Tip Decomposition on Large Bipartite Graphs

Tip decomposition has a pivotal role in mining cohesive subgraphs in bipartite graphs. It is a popular research topic with wide applications in document clustering, spam group detection, and analysis of affiliation networks. With the explosive growth of the bipartite graph data scale in these scenarios, it is necessary to use distributed methods to realize its effective storage. For this reason, this paper studies the problem of the tip decomposition on a bipartite graph in the distributed environment for the first time. Firstly, a new relay-based communication mode is proposed to realize effective message transmission when the given bipartite graph is decomposed in a distributed environment. Secondly, the Distributed Butterfly Counting (DBC) algorithm and the Distributed Tip Decomposition (DTD) algorithm are designed. In particular, a controllable parallel vertex activation strategy is proposed to solve the problem of memory overflow when DBC decomposes large-scale bipartite graphs. Finally, the message pruning strategy based on vertex priority and message validity pruning strategy are introduced to further improve the efficiency of the algorithm by reducing redundant communication and computing overhead. The experiment is deployed on the high-performance distributed computing platform of the National Supercomputing Center. The effectiveness and efficiency of the proposed algorithms are verified by experiments on several real datasets.

Integrated molecular and affiliation network analysis: Core-periphery social clustering is associated with HIV transmission patterns

This study investigates the two-mode core-periphery structures of venue affiliation networks of younger Black men who have sex with men (YBMSM). The study examined the association between these structures and HIV phylogenetic clusters, defined as members who share highly similar HIV strains that are regarded as a proxy for sexual affiliation networks. Using data from 114 YBMSM who are living with HIV in two large U.S. cities, our findings indicate that HIV phylogenetic clustering patterns were found to be associated with social clustering patterns whose members share affiliation with core venues that overlap with those of YBMSM. Distinct HIV transmission patterns were found in each city, a finding that can help to inform tailored venue-based and network intervention strategies.

Identifying overlapping terrorist cells from the Noordin Top actor–event network

Actor–event data are common in sociological settings, whereby one registers the pattern of attendance of a group of social actors to a number of events. We focus on 79 members of the Noordin Top terrorist network, who were monitored attending 45 events. The attendance or nonattendance of the terrorist to events defines the social fabric, such as group coherence and social communities. The aim of the analysis of such data is to learn about the affiliation structure. Actor–event data is often transformed to actor–actor data in order to be further analysed by network models, such as stochastic block models. This transformation and such analyses lead to a natural loss of information, particularly when one is interested in identifying, possibly overlapping, subgroups or communities of actors on the basis of their attendances to events. In this paper we propose an actor–event model for overlapping communities of terrorists which simplifies interpretation of the network. We propose a mixture model with overlapping clusters for the analysis of the binary actor–event network data, called $\mathtt{manet}$, and develop a Bayesian procedure for inference. After a simulation study, we show how this analysis of the terrorist network has clear interpretative advantages over the more traditional approaches of affiliation network analysis.

Structural embeddedness and position: evidences from affiliation of patent with technological classifications

ABSTRACTThis paper contributes to the empirical identification of the structural positions in network through an exploration of the interactions between R&D activities (patents) and technological areas of patents (technological classifications). Affiliation network analysis was conducted to study the affiliation between patents and technological classifications. We identified some key patents and explored the differentiated positions of the core and periphery. The results of network analysis was further verified by statistical analysis. We found that the impacts of the centralities in position were significant. Technological implications and foresights are discussed in the conclusion.

Triadic analysis of affiliation networks

AbstractTriadic closure has been conceptualized and measured in a variety of ways, most famously the clustering coefficient. Existing extensions to affiliation networks, however, are sensitive to repeat group attendance, which does not reflect common interpersonal interpretations of triadic closure. This paper proposes a measure of triadic closure in affiliation networks designed to control for this factor, which manifests in bipartite models as biclique proliferation. To avoid arbitrariness, the paper introduces a triadic framework for affiliation networks, within which a range of measures can be defined; it then presents a set of basic axioms that suffice to narrow this range to the one measure. An instrumental assessment compares the proposed and two existing measures for reliability, validity, redundancy, and practicality. All three measures then take part in an investigation of three empirical social networks, which illustrates their differences.

Exploratory social network analysis of affiliation networks of Indian listed companies

Interlocking directorates play a crucial role in the corporate governance system. In this paper we analyse the structural characteristics of the network of the interlocking directorate of National Stock Exchange (NSE) listed Indian companies using the tools of social network analysis to examine the effects of the underlying network on the performance of companies and directors. A component analysis of the network shows that 78.5% of the companies fall under one giant component with the largest island containing 6 companies. The giant component was further analysed for clusters and centrality measures. The results show that the highly boarded directors who constitute just 2.25% of the director population are associated with 42% of the total listed companies which account for 65.5% of the total market capitalisation. The top central actors in both director as well as company networks have been identified. The calculated values of mean path length and global clustering coefficient provide evidence for the existence of small world structure in the Indian corporate field.

Casting a net to better understand fisheries management: An affiliation network analysis of the Great Lakes Fishery Commission

Natural resource governance, and fisheries management in particular, includes multiple governmental jurisdictions and stakeholders. For many fisheries, however, an understanding of the numbers of organizations involved in management and the relationships among those organizations remains lacking. Using an email survey, we investigated the affiliation network of formal and informal relationships of Great Lakes Fishery Commission (GLFC) policy and decision makers (i.e., organizations involved in the management of Laurentian Great Lakes fisheries). The 63 respondents identified 1516 relationships with 149 organizations which provide numerous opportunities for groups to share resources and information. Important organizational roles that can be identified through network analysis include the coordinating role of the GLFC׳s staff and connections through stakeholder representatives. Informal relationships are an important aspect of the affiliation network with more than one-third of all relationships identified as informal relationships and 16 organizations identified only through informal relationships. Identification of affiliation networks could be a useful tool for understanding the connection between diverse stakeholder organizations and natural resource managers.

Content-driven analysis of an online community for smoking cessation: integration of qualitative techniques, automated text analysis, and affiliation networks.

We identified content-specific patterns of network diffusion underlying smoking cessation in the context of online platforms, with the aim of generating targeted intervention strategies. QuitNet is an online social network for smoking cessation. We analyzed 16 492 de-identified peer-to-peer messages from 1423 members, posted between March 1 and April 30, 2007. Our mixed-methods approach comprised qualitative coding, automated text analysis, and affiliation network analysis to identify, visualize, and analyze content-specific communication patterns underlying smoking behavior. Themes we identified in QuitNet messages included relapse, QuitNet-specific traditions, and cravings. QuitNet members who were exposed to other abstinent members by exchanging content related to interpersonal themes (e.g., social support, traditions, progress) tended to abstain. Themes found in other types of content did not show significant correlation with abstinence. Modeling health-related affiliation networks through content-driven methods can enable the identification of specific content related to higher abstinence rates, which facilitates targeted health promotion.

Examining Networks of Influence: Using Semantic Similarity Clustering and Affiliation Network Analysis to Reveal Lobbying Dynamics

Scholars of lobbying have been limited in their ability to measure organizational lobbying agendas, positions and coalitions. They are either forced to rely on time-consuming interviews or overly-broad Lobbying Disclosure Act-mandated issue codes. We propose a new approach. We use a hierarchical agglomerative clustering (HAC) algorithm to group bills within LDA issue codes based on their similarity, calculated using latent semantic analysis of a corpus we constructed from summary text provided by the Congressional Research Service (CRS). This technique allows us to disaggregate within the existing categories and label individual bills with higher resolution than was previously possible. We then use the clustering groups to label bills within a weighted affiliation network based on the volume of lobbying by a given industry on a given bill. The topic labels of bills within the network provide more detailed insight into the specific policies or provisions different industries have supported, and might be likely to support in the future. As a test, we apply this approach to lobbying on immigration legislation during the 109th-112th Congresses. Prepared for the 6th annual meeting of the Political Networks Section of the American Political Science Association (PolNet), June 28th 2013.

Network Analysis Among HIV-infected Young Black Men Who Have Sex With Men Demonstrates High Connectedness Around Few Venues

Network analysis is useful for understanding sexual transmission of HIV and other sexually transmitted infections. We conducted egocentric and affiliation network analysis among HIV-infected young black men who have sex with men (MSM) in the Jackson, Mississippi, area to understand networks and connectedness of this population. We interviewed 22 black MSM aged 17 to 25 years diagnosed as having HIV in 2006 to 2008. Participants provided demographic and geographic information about each sex partner during the 12 months before diagnosis and identified venues where they met these partners. We created affiliation network diagrams to understand connectedness of this population and identify venues that linked participants. The median number of partners reported was 4 (range, 1-16); a total of 97 partners (88 of whom were male) were reported. All but 1 participant were connected through a network of venues where they had met partners during the 12 months before diagnosis. Three venues were named as places for meeting partners by 13 of 22 participants. Participants reported having partners from all regions of Mississippi and 5 other states. HIV-infected young black MSM in this analysis were linked by a small number of venues. These venues should be targeted for testing and prevention interventions. The pattern of meeting sex partners in a small number of venues suggests densely connected networks that propagate infection. This pattern, in combination with sexual partnerships with persons from outside Jackson, may contribute to spread of HIV and other sexually transmitted infections into or out the Jackson area.

The network effect on technological innovation – by the analysis of affiliation network

PurposeThe purpose of this paper is to analyze the effect of affiliation network on creating innovative ideas and forming a technological position.Design/methodology/approachThe authors adopt the concept of an affiliated network and use the technique of social network analysis to investigate patent data from USPTO.FindingsFirst, the technological classification which captures the core position in the network is the most important technological trend. On the contrary, the technological classification which belongs to the position of periphery is less important for technological development. Second, the technological community with the highest degree, closeness and betweenness among other communities means that this technological community occupies broader technological scopes and with important influence with others.Practical implicationsThis paper demonstrates that affiliation network analysis can be a useful tool in determining the relationship between technology (patent) and R&D activity (technological classification). In the technological affiliation network, patents are related to one another because they occupy the same technological classification and technological classifications are related to one another because they share with patents. This connection has a clear advantage over a strategy of having R&D activities acts as bridges between distinct clusters of technologies.Originality/valueThis paper has briefly studied how affiliation network of technological communities and technological classifications relate to the R&D and technological development in environment. The authors' perspectives and empirical method can provide suggestions for making R&D decisions.

Applying Social Network Analysis to Analyze a Web-Based Community

this paper deals with a very renowned website (that is Book-Crossing) from two angles: The first angle focuses on the direct relations between users and books. Many things can be inferred from this part of analysis such as who is more interested in book reading than others and why? Which books are most popular and which users are most active and why? The task requires the use of certain social network analysis measures (e.g. degree centrality). What does it mean when two users like the same book? Is it the same when other two users have one thousand books in common? Who is more likely to be a friend of whom and why? Are there specific people in the community who are more qualified to establish large circles of social relations? These questions (and of course others) were answered through the other part of the analysis, which will take us to probe the potential social relations between users in this community. Although these relationships do not exist explicitly, they can be inferred with the help of affiliation network analysis and techniques such as m-slice. Book-Crossing dataset, which covered four weeks of users' activities during 2004, has always been the focus of investigation for researchers interested in discovering patterns of users' preferences in order to offer the most possible accurate recommendations. However; the implicit social relationships among users that emerge (when putting users in groups based on similarity in book preferences) did not gain the same amount of attention. This could be due to the importance recommender systems attain these days (as compared to other research fields) as a result to the rapid spread of e-commerce websites that seek to market their products online. Certain social network analysis software, namely Pajek, was used to explore different structural aspects of this community such as brokerage roles, triadic constraints and levels of cohesion. Some overall statistics were also obtained such as network density, average geodesic distance and average degree.

Exponential random graph ( [formula omitted]) models for affiliation networks

Recent advances in Exponential Random Graph Models (ERGMs), or p ∗ models, include new specifications that give a much better chance of model convergence for large networks compared with the traditional Markov models. Simulation based MCMC maximum likelihood estimation techniques have been developed to replace the pseudolikelihood method. To date most work on ERGMs has focused on one-mode networks, with little done in the case of affiliation networks with two or more types of nodes. This paper proposes ERGMs for two-mode affiliation networks drawing on the recent advances for one-mode networks, including new two-mode specifications. We investigate features of the models by simulation, and compared the goodness of fit results obtained using the maximum likelihood and pseudolikelihood approaches. We introduce a new approach to goodness of fit for network models, using a heuristic based on Mahalanobis distance. The classic Southern Women data and Australian Interlocking Director data are used as examples to show that the ERGM with the newly specified statistics is a powerful tool for statistical analysis of affiliation networks.