Two-mode Networks Research Articles

Mapping epistemic pluralism: A network analysis of discursive practices in communities promoting refused knowledge about healthcare and wellbeing

This article presents an analysis of discourses performed in communities that share and disseminate knowledge refused by institutional science. The study focuses on an online community concerned with alkaline water, food, and lifestyle, aiming at understanding how promoters of refused knowledge in this community enrol other forms of knowledge, including science. Theoretically, this work is framed in Science & Technology Studies, and in the tradition of actor-network theory, situating itself in a recent turn that takes epistemic instability and pluralism into due consideration, thus overcoming opaque views of the opposition between science and non-science. Empirically, this fine-grained analytic purpose is addressed by a mixed-method strategy in which discursive practices are observed through a web-ethnography conducted between January 2020 and December 2021 on the relevant online spaces and then analysed qualitatively and quantitively by means of formal techniques. Relying on the tools of social network analysis, the discursive space of the community under study is formalised as a two-mode network of knowledge claims and heterogeneous actors enrolled in discourse to sustain those claims. Then, community detection is performed to map the different assemblages of claims and actors and the relevant repertoires characterising those assemblages. Finally, multiple correspondence analysis applied to two-mode networks is used to highlight the dimensions of concern and meaning expressed in the knowledge organisation of this community.

Resistance distance and sharp bounds of two-mode electrical networks

Electrical networks are ubiquitous in our daily lives, ranging from small integrated circuits to large-scale power systems. These networks can be easily represented as graphs, where edges represent connections and vertices represent electric nodes. The concept of resistance distance originates from electrical networks, with this term used because of its physical interpretation, where every edge in a graph G is assumed to have a unit resistor. The applications of resistance distance extend to various fields such as electrical engineering, physics, and computer science. It is particularly useful in investigating the flow of electrical current in a network and determining the shortest path between two vertices. In this work, we have investigated seven different resistance distance-based indices of bipartite networks and derived general formulae for them; the sharp bounds with respect to these resistance distance indices are also identified. Additionally, we introduced a novel resistance distance topological index, the Multiplicative Eccentric Resistance Harary Index, and derived general formula for it. The sharp bounds with respect to this newly introduced index are also identified for bipartite networks.

Examining organizational collaboration and resource flows of disaster response system based on a time-dynamic perspective

Increasing the efficiency of organizational collaboration and resource flows is key to improving the emergency response performance. Given that previous studies did not consider the interactions and dynamic characteristics of emergency response organizations and resources, this study divided the emergency response into four stages and analyzed the relationship between organizational collaboration and resource flows by combining the network attributes of emergency response goals at each stage. The 2022 Luding earthquake was selected as a case study, and a multisource mixed data method was used to identify 381 organizations and 2496 organizational relationships. Using the social network analysis, the dynamic evolution characteristics of the emergency response networks (ERNs) and organization-resource two-mode networks were analyzed. The results showed that the ERNs generally exhibited the following trends: a more compact structure, balanced distribution, evolution from decentralization to centralization, and homogenous organizational collaboration. A correlation existed between changes in the organizational status and ERN attributes, with government organizations always at the core and non-profit organizations gradually rising. The evolution of the resources was closely related to the network attributes and organizational relationships. We identified the strengths of China's current emergency management system, such as enhanced performance, improved information infrastructure, and emphasis on the engagement of the military and social organizations. We recommend the following for future practice: enhancing the information technology system, establishing a mechanism for the participation of social forces, and strengthening the management of emergency stockpiles. These will help improve the disaster emergency management performance and reduce casualties and losses.

Social network analysis for medical narcotics in South Korea: focusing on patients and healthcare organizations

BackgroundMedical narcotics must be administered under medical supervision because of their potential for misuse and abuse, leading to more dangerous and addictive substances. The control of medical narcotics requires close monitoring to ensure that they remain safe and effective. This study proposes a methodology that can effectively identify the overprescription of medical narcotics in hospitals and patients.MethodsSocial network analysis (SNA) was applied to prescription networks for medical narcotics. Prescription data were obtained from the Narcotics Information Management System in South Korea, which contains all data on narcotic usage nationwide. Two-mode networks comprising hospitals and patients were constructed based on prescription data from 2019 to 2021 for the three most significant narcotics: appetite suppressants, zolpidem, and propofol. Two-mode networks were then converted into one-mode networks for hospitals. Network structures and characteristics were analyzed to identify hospitals suspected of overprescribing.ResultsThe SNA identified hospitals that overprescribed medical narcotics. Patients suspected of experiencing narcotic addiction seek treatment in such hospitals. The structure of the network was different for the three narcotics. While appetite suppressants and propofol networks had a more centralized structure, zolpidem networks showed a less centralized but more fragmented structure. During the analysis, two types of hospitals caught our attention: one with a high degree, meaning that potential abusers have frequently visited the hospital, and the other with a high weighted degree, meaning that the hospital may overprescribe. For appetite suppressants, these two types of hospitals matched 84.6%, compared with 30.0% for propofol. In all three narcotics, clinics accounted for the largest share of the network. Patients using appetite suppressants were most likely to visit multiple locations, whereas those using zolpidem and propofol tended to form communities around their neighborhoods.ConclusionsThe significance of this study lies in its analysis of nationwide narcotic use reports and the differences observed across different types of narcotics. The social network structure between hospitals and patients varies depending on the composition of the medical narcotics. Therefore, these characteristics should be considered when controlling medication with narcotics. The results of this study provide guidelines for controlling narcotic use in other countries.

What makes co-production work in sustainable neighborhood rehabilitation in China? A stakeholder structure perspective

Globally, co-production serves as a crucial approach to promote sustainable neighborhood rehabilitation. However, in China, where the governance system is characterized by strong state discourse power and a lack of public participation tradition, neighborhood rehabilitation encounters significant challenges, leading to project postponements or even failures due to unsuccessful co-production activities. To reveal the hidden problems, this paper aims to gain an in-depth understanding of the stakeholders' co-production roles and their participation structure in the co-produced sustainable neighborhood rehabilitation process from multiple dimensions. Using the case in Chongqing, the data was collected through questionnaire surveys and in-depth interviews. Social network analysis was utilized as the research method, and one-mode and two-mode networks were established to depict six relationship types with using diverse network indicators. This study revealed the high complexity of stakeholder structure, the awkward role of grass-rooted government, the dilemma of public voices and perceptions, uneven involvement in the co-production process, and the dynamic path of co-production formation. Drawing upon co-production theory, this paper elucidates the intricate mechanism of co-produced neighborhood rehabilitation in China. It offers valuable implications for policymakers and urban planners for project promotion, and contributes empirical insights to research in similar governance systems across the global context.

Generalized latent space model for one-mode networks with awareness of two-mode networks

Latent space models have been widely studied for one-mode networks, in which the same type of nodes connect with each other. In many applications, one-mode networks are often observed along with two-mode networks, which reflect connections between different types of nodes and provide important information for understanding the one-mode network structure. However, the classical one-mode latent space models have several limitations in incorporating two-mode networks. To address this gap, a generalized latent space model is proposed to capture common structures and heterogeneous connecting patterns across one-mode and two-mode networks. Specifically, each node is embedded with a latent vector and network-specific degree parameters that determine the connection probabilities between nodes. A projected gradient descent algorithm is developed to estimate the latent vectors and degree parameters. Moreover, the theoretical properties of the estimators are established and it has been proven that the estimation accuracy of the shared latent vectors can be improved through incorporating two-mode networks. Finally, simulation studies and applications on two real-world datasets demonstrate the usefulness of the proposed model.

Research on the evolution of China's photovoltaic technology innovation network from the perspective of patents

Photovoltaic (PV) technology, as a low-carbon energy technology, is crucial to mitigating climate change and achieving sustainable development. China has the largest total number of PV technology patents in the world, but the lack of core technologies has restricted the further innovative development of China's PV industry. Therefore, it is necessary to clarify China's current PV technology accumulation to better catch up with key technology areas. To clearly describe the structural characteristics of China's PV technology innovation network, this study uses China's patent PV technology data over the past 20 years from the Incopat global patent database and analyses the structural characteristics of the network from the perspectives of one-mode and two-mode networks, using method of social network analysis (SNA). The results show that 1) the leading PV enterprises have basically formed relatively stable internal collaborations and that the scale of innovation network development has expanded rapidly, with very strong stamina; 2) with the development of China's PV industry, many innovative PV techniques have been developed by leading enterprises in the field of innovation and research and development (R&D) of PV technology, and among patent applicants with strong collaboration, kinship collaboration with investment relationships is dominant; 3) provinces participating in PV technology innovation are increasing significantly, the network is more influenced by leading nodes, and the eastern coastal provinces are pioneers in the innovation and R&D of PV technology; and 4) PV technological innovation collaboration between patent applicants and cities has changed from local collaboration to cross-regional collaboration, high-value areas are basically concentrated in the eastern coastal region of China, with scattered spatial characteristics, and cross-regional collaboration presents a “triangular” spatial structure, with the Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin-Hebei as cores. The conclusions can provide patent information support for scientific research on energy conservation and emission reduction to achieve low-carbon goals, and can also provide reference for policy formulation of renewable energy development and green development strategies.

Co-evolution of a socio-cognitive scientific network: A case study of citation dynamics among astronomers

This paper aims to understand how a group of academics cite each others’ work through time, considering the simultaneous co-evolution of three networks representing their scientific collaboration, the journals in which they publish and institutional membership. It argues that both social and cognitive processes contribute to these dynamics. Two types of network mechanisms are considered specifically: closures by affiliation and closures by association. To assess whether these mechanisms generate the macro features of the network under study, we propose new features for three-mode multilevel networks such as the mixed geodesic distances, mixed degree distributions, and the mixed quadrilateral census. We investigate whether a micro-level model that considers the above-mentioned network mechanisms is able to correctly reproduce these features. We apply stochastic actor-oriented models (SAOMs) for one-mode and two-mode networks to link the micro-macro processes using a dataset of a scientific community of astronomers from 2013 to 2015. The results suggest that social relationships grounded on scientific collaboration and proximity based on institutional affiliation are more accurately suited to understanding the co-evolution of the network of citations than an alternative approach that merely considers cognitive-based networks measured as the similarity in publishing in the same journals.

FMC2 model based perception grading for dark insurgent network analysis.

The burgeoning role of social network analysis (SNA) in various fields raises complex challenges, particularly in the analysis of dark and dim networks involved in illicit activities. Existing models like the stochastic block model (SBM), exponential graph model (EGM), and latent space model (LSM) are limited in scope, often only suitable for one-mode networks. This article introduces a novel fuzzy multiple criteria multiple constraint model (FMC2) tailored for community detection in two-mode networks, which are particularly common in dark networks. The proposed method quantitatively determines the relationships between nodes based on a probabilistic measure and uses distance metrics to identify communities within the network. Moreover, the model establishes fuzzy boundaries to differentiate between the most and least influential nodes. We validate the efficacy of FMC2 using the Noordin Terrorist dataset and conduct extensive simulations to evaluate performance metrics. The results demonstrate that FMC2 not only effectively identifies communities but also ranks influential nodes within them, contributing to a nuanced understanding of complex networks. The method promises broad applicability and adaptability, particularly in intelligence and security domains where identifying influential actors within covert networks is critical.

The duality of networks and groups: Models to generate two-mode networks from one-mode networks

AbstractShared memberships, social statuses, beliefs, and places can facilitate the formation of social ties. Two-mode projections provide a method for transforming two-mode data on individuals’ memberships in such groups into a one-mode network of their possible social ties. In this paper, I explore the opposite process: how social ties can facilitate the formation of groups, and how a two-mode network can be generated from a one-mode network. Drawing on theories of team formation, club joining, and organization recruitment, I propose three models that describe how such groups might emerge from the relationships in a social network. I show that these models can be used to generate two-mode networks that have characteristics commonly observed in empirical two-mode social networks and that they encode features of the one-mode networks from which they were generated. I conclude by discussing these models’ limitations and future directions for theory and methods concerning group formation.

How typical is your project? The need for a no-model approach for information management in AEC

This paper discusses the merit of using a no-model approach (no common product models or ontologies, etc.) for managing information in the AEC. It proposes an option for such an approach through the generation and analysis of semantic and social networks of communication between project stakeholders. The proposed approach advocates for a bottom-up discovery of knowledge constructs from stakeholder communication. Knowledge constructs are mini two-mode networks containing, on the one hand, clusters of concepts that appear frequently in the semantic networks of stakeholder communication; and, on the other hand, the social networks of stakeholders discussing these concepts. Using common models (such as IFC) has several limitations, including inflexibility to recognize and accommodate project contexts (which vary constantly), inability to timely capture the emergence of knowledge, and the scope creep problem (the ever-existing need to add more concepts to the common model from within and outside ACE domain). The no-model approach presented here is meant to complement and not replace the established model-based approach. This approach is built on the belief in the ontological agency of project stakeholders: knowledge is a social phenomenon that emerges through interactions between people. It advocates a shift from a top-down format where experts or standards clearinghouses tell (force) practitioners what should be true about their project. In every project, stakeholders customize (the structure of) established knowledge and adopt elements from emerging knowledge to address project-specific needs. They use the more superior intelligence (the human one) to innovate a ‘model of what they know’ to guide the management of the project in a manner specific to its context. By studying projects’ communication, we tell (inform) project stakeholders what knowledge constructs can be found in their communication. Unlike generic/static models, the resulting knowledge constructs are by default sensitive to project conditions. We should re-design our information management systems to be able to recognize and adaptively use the constructs established by project teams to facilitate their sharing of data (along with the established scheme, such as IFC). Relatedly used constructs can be nominated as AEC-wide prototype constructs, representing what we know about a typical project. At the initiation of a new project, these can be the starting scheme used by information and communication systems. As the project evolves and the project's own constructs are generated, the project-specific constructs should guide the flow of information. Contrasting project constructs against prototypes should inform the stakeholders of not only what is factual about their view/model of knowledge, but also how unique are they (from generic/base knowledge). This approach to no-model thinking is advantageous for several reasons. First, addressing the model rigidity problem. Because of the increasing complexity of projects, no single/standardized model can capture all contexts. Second, the increasing need for handling project unstructured data. The proposed approach helps formalize knowledge constructs from such data using network science. Third, recognizing and tracking the evolutionary nature of knowledge. Fourth, supporting innovation: instead of forcing knowers (people) to comply with a static model of reality, the new approach encourages them to imagine new possible futures/ worlds—after all, the true essence of digital twinning is to virtualize futures not just to digitize the present.

Multimodal mechanisms of political discourse dynamics and the case of Germany’s nuclear energy phase-out

AbstractThe 2011 policy pivot of the German government, from extending nuclear power plants terms to securing their shutdown for 2022, cannot be explained without looking at how the German political discourse network shifted in the months following Fukushima. This paper seeks to model and identify mechanisms that help explain how the two-mode network of political actors’ support for claims developed. We identify possible mechanisms to explain discourse dynamics from literature on political discourse and discourse networks, and extend homophily mechanisms to two-mode networks as “tertius” effects. We then introduce and employ a multimodal extension of dynamic network actor models to answer two questions key to how the discourse has evolved: which actors support claims more frequently and which claims they support. Our results indicate that mechanisms vary according to the discursive phase, but that powerful actors participate in the discourse more often, and actors tend to support claims that have already found support by cross-party coalitions. These are the two most prominent mechanisms that help to explain the dramatic nuclear policy change in Germany after Fukushima.

Network Approach to the Digital Innovation Hub as an Ecosystem Supporting the Digital Transformation of Enterprises in a Region

The study aims to examine the two-mode network of digital innovation hubs (DIH) operating in Poland, understood as ecosystems and competence centers, to support the digital transformation of enterprises in a region. Digital Innovation Hub is also one of the S3 Smart Specialization Platform mapping tools and identifying areas of economic specialization of regions, aimed at facilitating interregional cooperation and creating partnerships between different actors across Europe. While the functions and goals of DIH are more and more often presented in national and regional development documents, little is known about the network structure of DIHs operating in a given country (region) and in the European Union. The study used structured and secondary Smart Specialization Platform data, which allows the creation of two-mode relationship networks and shows how interrelated the studied DIHs and digital technologies they use are. Based on the social network analysis and network metrics (centrality, density, and network projection) integrated into programs such as UCINET and ORA-PRO, the visualization and measurement of the network structure of two-mode networks (actor x technology ATij) as well as its projection into actor x actor (AAij) and technology x technology (TTij) networks were created. The results show 15 DIHs or actors (A) and 29 technologies (T), each of which a given DIH offers to show primarily influential DIHs and digital technologies as well as the areas of cooperation. DIHs are a promising and still little explored area of interest for researchers worldwide. The article is the first attempt to investigate the network structure of DIHs operating in Poland and their relations to technologies.

Digital Innovation Hubs: Two-mode and Network-Based View on Technology and Services Provided

The Digital Innovation Hub (DIH) is a relatively young research area emerging on the basis of well-known ideas of technology parks or clusters. DIH is mainly focused on the digital transformation of enterprises and the development of a given region in the context of Industry 4.0 based on digital technology. The European Union is the initiator of the "Digital Europe" program for 2021-2027, aiming to promote the transfer of digital specialist knowledge to economic entities. The authors chose the DIHs operating in Poland as a starting point for network analysis in the context of the technology and services provided by Polish DIHs. This research aims to measure network cohesion, the centrality of individual DIH, and its specialty (exclusivity). It is based on secondary data collected using the Smart Specialization Platform based on which the two two-mode networks have been developed: actor x technology (ATij) and actor x services provided (ASPij). Measurements and visualization were made using two network programs (UCINET and ORA PRO). The following social network analysis techniques were used: 1) Two-mode cohesion: density, diameter, and fragmentation; 2) Two-mode centrality: degree and betweenness, and 3) Two-mode specialty (exclusivity). The results show two-mode networks consisting of the following nodes: 15 actors (A) or DIHs; 29 technologies (T), and 16 services provided (SP) forming a meta-network. They provide an insight into the network structure, most influential actors (DIHs), technologies, and services, as well as the specialty of each DIH in the European country, thus giving rise to further comparative research, using secondary data for the other 26 countries of the European Union.

Minimum Zagreb Eccentricity Indices of Two-Mode Network with Applications in Boiling Point and Benzenoid Hydrocarbons

A two-mode network is a type of network in which nodes can be divided into two sets in such a way that links can be established between different types of nodes. The relationship between two separate sets of entities can be modeled as a bipartite network. In computer networks data is transmitted in form of packets between source to destination. Such packet-switched networks rely on routing protocols to select the best path. Configurations of these protocols depends on the network acquirements; that is why one routing protocol might be efficient for one network and may be inefficient for a other. Because some protocols deal with hop-count (number of nodes in the path) while others deal with distance vector. This paper investigates the minimum transmission in two-mode networks. Based on some parameters, we obtained the minimum transmission between the class of all connected n-nodes in bipartite networks. These parameters are helpful to modify or change the path of a given network. Furthermore, by using least squares fit, we discussed some numerical results of the regression model of the boiling point in benzenoid hydrocarbons. The results show that the correlation of the boiling point in benzenoid hydrocarbons of the first Zagreb eccentricity index gives better result as compare to the correlation of second Zagreb eccentricity index. In case of a connected network, the first Zagreb eccentricity index ξ1(ℵ) is defined as the sum of the square of eccentricities of the nodes, and the second Zagreb eccentricity index ξ2(ℵ) is defined as the sum of the product of eccentricities of the adjacent nodes. This article deals with the minimum transmission with respect to ξi(ℵ), for i=1,2 among all n-node extremal bipartite networks with given matching number, diameter, node connectivity and link connectivity.

Knowledge Transfer in a Two-Mode Network Between Higher Education Teachers and Their Innovative Teaching Projects

Knowledge transfer (KT) and innovation diffusion are closely related to each other because it is knowledge regarding an innovation that gets adopted. Little research in learning analytics provides insight into KT processes in two-mode networks, especially in the context of educational innovations. It is unclear how such networks are structured and whether funding can create a network structure efficient for KT. We used a case-study approach to analyze a two-mode network of 208 university members (based on archival data) who worked together on 91 innovative teaching projects. Our results show that the two-mode network displays a decentralized structure and more clustering than can be assumed by chance, promoting KT and learning. To gain a deeper understanding of the kind of knowledge that is transferred in the network, we analyzed the effects of different educational innovation elements (e.g., game-based learning) as attributes of higher education teachers. Overall, our results suggest that funding and the creation of project structures in the context of educational innovation is a sustainable way to create KT, and therefore organizational change. Furthermore, the results imply that university practitioners need to implement networking interventions to create more connections between subgroups in teacher-related networks.

Generators or diffusers? Examining differences in the dynamic coupling of context and social ties across multiple types of foci

Feld’s focus theory is one of the most influential approaches in social network analysis. However, with very few exceptions, empirical assessments of the theory have only focused on a narrow range of its predictions premised on comparative statics. This paper restates classical focus theory in terms of dependence between multiple network layers as they co-evolve, yielding dynamic focus theory. Social foci serve as an ecology within which person-to-person relations form and as a mechanism via which tastes and behaviors diffuse within a population through interpersonal ties. We argue this is a type of “focus effect” distinct from the standard tie-generation process typically considered. In addition, we go beyond previous single-foci studies in considering multiple foci types, allowing us to ascertain which foci—e.g., clubs, activities, musical genres, or academic course areas—act more like tie generators or which ones as taste diffusers. Using rich data from the NetSense and NetHealth studies, we use recently developed Stochastic Actor-Oriented Models (SAOMs) for two-mode networks to estimate simultaneous foci effects across different foci types. We find that musical genres function primarily as taste-diffusers, club types exclusively as tie generators, and course areas and activities as both tie generators and taste diffusers. We conclude by discussing key implications of dynamic focus theory, including how foci properties can account for variability in foci effects and the role different types of social foci play in social influence and the diffusion of tastes and behaviors.

How do youth choose activities? Assessing the relative importance of the micro-selection mechanisms behind adolescent extracurricular activity participation

We investigate the network micro-selection mechanisms responsible for patterns of high school student extracurricular activity (ECA) participation, with a particular focus on those that can lead to ethnoracial segregation. We identify six types of mechanisms by which students select into activities (e.g., peer influence, homophily), which we test using a unique longitudinal dataset that combines student surveys with yearbook data on ECA involvement. These contexts represent two ethnoracially diverse U.S. high schools involving 2403 students and over 200 different activities spanning two school years. Using a stochastic actor-oriented model for two-mode networks, we find support for the hypothesized activity selection mechanisms. Follow-up analyses convey the relative importance of different mechanisms and inform our discussion of how ECA participation patterns develop and possible sources of segregation. Whereas selection is driven by mechanisms that include influence from friends and co-participants and similarity to fellow participants, no single overarching mechanism appears strong enough to fully account for ECA segregation

Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections

Projections of bipartite or two-mode networks capture co-occurrences, and are used in diverse fields (e.g., ecology, economics, bibliometrics, politics) to represent unipartite networks. A key challenge in analyzing such networks is determining whether an observed number of co-occurrences between two nodes is significant, and therefore whether an edge exists between them. One approach, the fixed degree sequence model (FDSM), evaluates the significance of an edge’s weight by comparison to a null model in which the degree sequences of the original bipartite network are fixed. Although the FDSM is an intuitive null model, it is computationally expensive because it requires Monte Carlo simulation to estimate each edge’s p value, and therefore is impractical for large projections. In this paper, we explore four potential alternatives to FDSM: fixed fill model, fixed row model, fixed column model, and stochastic degree sequence model (SDSM). We compare these models to FDSM in terms of accuracy, speed, statistical power, similarity, and ability to recover known communities. We find that the computationally-fast SDSM offers a statistically conservative but close approximation of the computationally-impractical FDSM under a wide range of conditions, and that it correctly recovers a known community structure even when the signal is weak. Therefore, although each backbone model may have particular applications, we recommend SDSM for extracting the backbone of bipartite projections when FDSM is impractical.

Analysis of the Southern women network using fractional approach

The Davis’ Southern women network is one of the most analyzed two-mode networks in the SNA literature. Freeman published in 2003 a meta-analysis in which he reviewed 21 clusterings of the set of women obtained by different approaches. Some solutions are based on the projection to a one-mode network. In the projection, the nodes from the other mode with a large degree are over-represented. In this paper, another approach is presented based on fractional weighting used in bibliometrics that provides a fairer weighting of nodes on either mode. The obtained projection networks are difficult to read. To get insight into their structure and to identify groups of nodes the Pathfinder method and hierarchical clustering are used. Three regular two-mode blockmodels of the Southern women network are obtained congruent with Davis’ second matrix arrangement.