Network Analytical Framework Research Articles

Multiple paths to rumination within a network analytical framework

Theories of rumination have proposed different psychological factors to place one at risk for repetitive negative thinking. A comprehensive empirical test that captures the most relevant contributors to rumination is lacking. Building on influential self-regulatory and metacognitive frameworks, we modeled how key constructs in this context relate to ruminative thinking. 498 participants completed online questionnaires including indicators of rumination, metacognition, promotion goal orientation, effortful control, and depression. We estimated regularized partial correlation networks to investigate unique associations between the different constructs and followed these analyses up with directed acyclic graphs to identify potential pathways towards rumination. Results demonstrated that: (1) both self-regulatory and metacognitive factors were directly linked to rumination, amongst these were (2) positive beliefs, negative beliefs about uncontrollability and harm, cognitive self-consciousness, depression, effortful control, perfectionism, and (lack of) cognitive confidence, and (3) we identified multiple directed pathways, suggesting three direct contributors to rumination while controlling for the influence of all other variables: diminished effortful control, positive beliefs, and cognitive self-consciousness. This study is the first to comprehensively assess metacognitive and self-regulatory frameworks of rumination in a data-driven manner. Our findings suggest that there are multiple pathways towards rumination, which should be incorporated in clinical case conceptualization of rumination and related disorders.

Interaction network rewiring and species' contributions to community-scale flexibility.

The architecture of species interaction networks is a key factor determining the stability of ecological communities. However, the fact that ecological network architecture can change through time is often overlooked in discussions on community-level processes, despite its theoretical importance. By compiling a time-series community dataset involving 50 spider species and 974 Hexapoda prey species/strains, we quantified the extent to which the architecture of predator-prey interaction networks could shift across time points. We then developed a framework for finding species that could increase the flexibility of the interaction network architecture. Those "network coordinator" species are expected to promote the persistence of species-rich ecological communities by buffering perturbations in communities. Although spiders are often considered as generalist predators, their contributions to network flexibility vary greatly among species. We also found that detritivorous prey species can be cores of interaction rewiring, dynamically interlinking below-ground and above-ground community dynamics. We further found that the predator-prey interactions between those network coordinators differed from those highlighted in the standard network-analytical framework assuming static topology. Analyses of network coordinators will add a new dimension to our understanding of species coexistence mechanisms and provide platforms for systematically prioritizing species in terms of their potential contributions in ecosystem conservation and restoration.

A multiplex network based analytical framework for safety management standardization in construction engineering

Effective safety management significantly influences the success of construction projects. Comprehensive standards are developed to ensure a fundamental safety level. However, improving the standard system requires an effective evaluation method to assess its coverage of construction risks. This challenge arises due to the intricate relationships between risks and standards. Addressing this issue, this paper introduces a dual-layer network analytical framework for evaluating standard systems in construction safety management. By utilizing various property metrics and four dedicated node metrics, the framework allows quantifying the extent to which a standard system addresses project-specific risk factors. The efficacy of the analytical framework is validated through a case study, revealing that the management mechanism of a standard system primarily manifests in the overall management of specific risks and associated risks with triggering relationships. It is observed that key standards often encompass a wider array of risks. Based on the findings, this paper offers suggestions for revising construction standards.

New trends in network modeling of psychopathology.

New trends in network modeling of psychopathology.

A multi-scale comparison of tourism attraction networks across China

Tourist destination is a complex system of multiple interacting stakeholders. Although studies have applied network analytics to explore the structure of this system, majority are oriented towards the description of limited destinations, this not providing generalizable insights of the effect of different attraction patterns. This study develops a multi-scale multi-case network analytical framework that cross-compares the network properties of 340 destinations across China progressively from macro, meso to micro scale to dissect the effect mechanism. The study reveals that 1) destinations with larger markets exhibit significant Matthew effects, which tourists are more centralized on core attractions as the network expands; 2) the core-periphery structure is a beneficial pattern that strengthens interactions inside the core, and stimulates weak attractions from core to periphery; 3) motifs cooperation and affiliation promote coordination and sustainability of the system, while competition and structure hole, which maintains local preferential attractions, decrease the overall integrity and efficiency.

Networking down: Networks, innovation, and relational labor in digital book publishing

While management and network scholars have long asked why some groups adapt to innovation more readily than others, such questions have been surprisingly absent in studies of new media production. We offer a network analytical framework which relates producers’ informal networks to their capacity to adopt digital innovations. Through a network ethnography of 4264 romance writers, we find that established authors who reversed traditional advice patterns, by seeking advice from inexperienced newcomers rather than experienced peers, were more likely to adopt digital self-publishing. By linking the concept of relational labor to network structures, we demonstrate the value of “networking down” in a digitally disrupted cultural industry—a surprising finding in a business where networking up, to powerful actors, has seemed critical for success. We argue that strategic relational labor by established content creators facilitates adaptation to digital conditions and provides some measure of protection against precarity in a changing landscape.

Applying network analysis to investigate substance use symptoms associated with drug overdose

BackgroundDrug overdose deaths have been increasing over the last several decades. While single substance classes, such as opioids, have been implicated in this rise, less is known about the contributions of polysubstance use (PSU) and other combinations of specific substances and symptoms that may be a risk factor for drug overdose. MethodsSymptoms of alcohol, cannabis, and other drug use disorders, as well as co-substance use indicators, were assessed and then examined via network analysis in a sample of young adults (N = 1540). Features of the estimated symptom network were investigated, including topology and node centrality, as well as bridge centrality, which further examines node centrality while accounting for the nodes belonging to discrete communities. ResultsIndividual symptoms were more strongly associated with other symptoms within the same substance class than across substance classes. Tolerance and withdrawal symptoms were the most central items in the network. However, when accounting for symptoms belonging to discrete substance classes, drug overdose emerged as a strong bridge symptom, among others. ConclusionsAs a strong bridge symptom, drug overdose had many connections with a variety of substances and symptoms, which might suggest that risk for drug overdose may be a function of overall substance use severity. Altogether, examining alcohol and substance use symptoms using a network analytic framework provided novel insights into the role PSU might play in conferring risk for drug overdose.

A New Perspective on “Drinking” Self: A Network Approach to Characterizing Drinking-Related Changes in Personality

Background Previous research suggests that self-knowledge, particularly perceptions of oneself as a “drinker,” may influence the development and progression of alcohol use and related problems, such as binge drinking. While existing studies have provided empirical evidence for the importance of assessing drinking self-perception within the five-factor model of personality framework, further examination with novel analytic methods, such as network analyses, could improve understanding of the drinker self-concept. Methods: Five factor traits of openness to experience, conscientiousness, extraversion, agreeableness, and emotional stability were assessed among a large sample of young adults (n = 423) across “general” and “drinking” contexts. Features of the personality networks were investigated, including topology, network centrality, stability of the network across “general” and “drinking” assessments, as well as differences in the network across the two assessments. Results: Individual personality items were more related to other items within the same trait than to other traits. There was no most central item in the networks, but item strength was uncorrelated to mean-level of the item. The network structure was stable across both assessments, although the overall strength of item relationships significantly increased in the drinking personality network. Conclusions: Examining drinking personality using a network analytic framework provided novel insights into drinker self-concept and the role drinking contexts might play in self-perception of personality in those contexts. Supplemental data for this article is available online at https://doi.org/10.1080/10826084.2022.2034874 .

Analysis of urban agglomeration structure through spatial network and mobile phone data

AbstractUrban agglomeration is an important strategy used to promote economic development and urbanization in China. Understanding the structure of urban agglomeration is therefore essential for policy‐makers and planners. In this study, the Beijing–Tianjin–Hebei urban agglomeration (BTHUG) is explored through a proposed spatial network analytical framework and a large mobile phone data set (over 20 million users). We first construct a weight‐directed spatial interaction network based on an origin–destination matrix derived from the data set. Several network metrics (i.e., degree, strength, the rich‐club coefficient, and the assortativity coefficient) and three selected community detection algorithms (i.e., Infomap, Louvain, and Regionalization) are applied and compared to reveal the structure of the BTHUG. A four‐level hierarchical structure is defined and observed: one global center, two local centers, major cities that have low mobility flow but strong linkages with the three centers, and peripheral cities that have low mobility flow and weak linkages with the three centers. In particular, the results imply that the spatial structure of the BTHUG is over‐dependent on the global center (i.e., Beijing and northern Langfang). Further, ignoring spatial interaction patterns in top‐down administrative planning for urban agglomeration may lead to ineffective integrated development. The implications for BTHUG planning are discussed.

Measuring Law Over Time: A Network Analytical Framework with an Application to Statutes and Regulations in the United States and Germany

How do complex social systems evolve in the modern world? This question lies at the heart of social physics, and network analysis has proven critical in providing answers to it. In recent years, network analysis has also been used to gain a quantitative understanding of law as a complex adaptive system, but most research has focused on legal documents of a single type, and there exists no unified framework for quantitative legal document analysis using network analytical tools. Against this background, we present a comprehensive framework for analyzing legal documents as multi-dimensional, dynamic document networks. We demonstrate the utility of this framework by applying it to an original dataset of statutes and regulations from two different countries, the United States and Germany, spanning more than twenty years (1998–2019). Our framework provides tools for assessing the size and connectivity of the legal system as viewed through the lens of specific document collections as well as for tracking the evolution of individual legal documents over time. Implementing the framework for our dataset, we find that at the federal level, the United States legal system is increasingly dominated by regulations, whereas the German legal system remains governed by statutes. This holds regardless of whether we measure the systems at the macro, the meso, or the micro level.

A network analytical framework to analyze infrastructure damage based on earthquake cascades: A study of earthquake cases in Japan

Earthquakes can result in disasters affecting infrastructure and assets that are exposed and vulnerable and thus prone to damage. Considering earthquake cascades, we can reveal the immediate and intermediate effects of earthquakes causing infrastructure damage. In this study, we propose a network analytical modeling approach to analyze earthquake cascades. The analytical framework integrates incident causation modeling and probabilistic network analysis. The linear effects from earthquakes are delineated in incident chains to represent the consequences of earthquakes to infrastructure; the incident chains are then remodeled topologically by the network theory to reveal the nonlinear cascading effects of earthquakes to infrastructure. The network of earthquakes’ effects and intermediate effects is then structurally analyzed by probabilistic network analysis that uncovers the unifying principles and statistical topology of earthquake cascades to infrastructure. The results show that earthquakes can directly cause significant damage to infrastructure and indirectly cascade the effects to infrastructure damage via intermediate effects; each effect or intermediate effects in earthquake cascades have their own role in leading to infrastructure damage. The critical infrastructure damage due to earthquakes and critical intermediate effects in propagating earthquake cascades are revealed in this study by the network analysis of network density, degree centrality, closeness centrality, betweenness centrality, and cascade quantification. To reduce infrastructure damage due to earthquake cascades, we suggest tackling the directed interdependent intermediate effects to minimize the risks associated with earthquakes to infrastructure damage. The network analytical framework in this study can be used to understand the propagation damage of earthquakes on infrastructure; thus, our work can provide valuable insight into earthquake preparedness and emergency management.

Measuring Law Over Time: A Network Analytical Framework with an Application to Statutes and Regulations in the United States and Germany

How do complex social systems evolve in the modern world? This question lies at the heart of social physics, and network analysis has proven critical in providing answers to it. In recent years, network analysis has also been used to gain a quantitative understanding of law as a complex adaptive system, but most research has focused on legal documents of a single type, and there exists no unified framework for quantitative legal document analysis using network analytical tools. Against this background, we present a comprehensive framework for analyzing legal documents as multi-dimensional, dynamic document networks. We demonstrate the utility of this framework by applying it to an original dataset of statutes and regulations from two different countries, the United States and Germany, spanning more than twenty years (1998-2019). Our framework provides tools for assessing the size and connectivity of the legal system as viewed through the lens of specific document collections as well as for tracking the evolution of individual legal documents over time. Implementing the framework for our dataset, we find that at the federal level, the United States legal system is increasingly dominated by regulations, whereas the German legal system remains governed by statutes. This holds regardless of whether we measure the systems at the macro, the meso, or the micro level.

Comprehensive and Superficial Data Users: A Convergent Mixed Methods Study of Teachers’ Practice of Interim Assessment Data Use

Background/Context Teachers’ practice of interim assessment data use is subject to an array of individual, social, and organizational contextual factors. While contemporary research situates interim assessments within teachers’ broader practice of data use, teachers do not make sense of interim assessment data in isolation. This interconnectedness of data sources serves as an important context for understanding how teachers make sense of interim assessment data. Purpose/Objective/Research Question/Focus of Study Teachers’ ability to connect data sources to inform a common instructional practice has important implications for their instructional decision making. The present study maps the interconnectedness of interim assessment with other data sources as one of two dimensions of teachers’ practice of data use. Furthermore, this article explores how those dimensions are related to similarities, differences, and patterns in how teachers make sense of interim assessment data. Population/Participants/Subjects Data come from 88 grade 3-5 mathematics or English language arts teachers across 10 elementary schools in two states. All participants completed a survey about their data use practices. A subsample of 23 teachers completed an extended survey and participated in an interview data about how they use interim assessment data. Research Design: We conducted a convergent mixed methods study of teachers’ practice of interim assessment data use. Data Collection and Analysis Quantitatively, we analyzed the interconnectedness of data sources across teachers within an epistemic network analytic framework. Qualitatively, we coded interview transcripts to gather evidence of how teachers use interim assessment data to make instruction decisions. We integrated our quantitative and qualitative findings and recoded interview transcripts to develop a nuanced perspective on the similarities, differences, and patterns in how teachers make sense of interim assessment data. Findings/Results Findings reveal teachers rarely interpret interim assessment data in isolation and meaningfully vary in the extent to which they perceive it informs their broader instructional practice. Taken together, these two dimensions of teachers’ practice of interim assessment data use are indicators of significant patterns in the authenticity of teachers’ data use with implications for their instructional decision making. Conclusions/Recommendations The present study maps the interconnectedness of data sources within teachers’ instructional practice and how it serves as one of two dimensions that explain similarities, differences, and patterns in how they make sense of interim assessment data. Our findings have implications for how instructional support coaches, administrators, interim assessment developers, and researchers can support teachers’ use of interim assessment data for instructional purposes.

The formation mechanism of the player transfer network among football clubs

ABSTRACT Clubs compete for talented players to succeed in the global football industry. Drawing on a network analytical framework, this research explores how endogenous and exogenous factors influence the partner selection process in the player transfer market. The boundary of the transfer network consists of 98 football clubs from the top five European leagues. Using exponential random graph models (ERGMs), the current study confirms that clubs have a significantly high propensity of exchanging players. Player transactions often occur through closed triads, such that clubs tended to make a deal with those who shared the same third partners. Additionally, league type and match performance produce stable and significant effects on the formation of transfer ties. Practical implications are also discussed.

Multilevel determinants of collaboration between organised criminal groups

Collaboration between members of different criminal groups is an important feature of crime that is considered organised, as it allows criminals to access resources and skills in order to exploit illicit economic opportunities. Collaboration across criminal groups is also difficult and risky due to the lack of institutions supporting peaceful cooperation in illicit markets. Thus cross-group collaboration has been thought to take place mostly among small and transient groups. This paper determines whether and under what conditions members of different, larger organised crime groups collaborate with one another. To do so we use intelligence data from the Canadian province of Alberta, centering on criminals and criminal groups engaged in multiple crime types in multiple geographic locations. We apply a multilevel network analytical framework and exponential random graph models using Bayesian techniques to uncover the determinants of cross-group criminal collaboration. We find cross-group collaboration depends not only on co-location, but also on the types of groups to which the criminals are affiliated, and on illicit market overlap between groups. When groups are operating in the same geographically-situated illicit markets their members tend not to collaborate with one another, providing evidence for the difficulty or undesirability of cross-group collaboration in illicit markets. Conversely, members of Outlaw Motorcycle Gangs are more likely to collaborate across groups when markets overlap, suggesting the superior capacity and motivation of biker gangs to coordinate criminal activity. Our paper contributes to the understanding of criminal networks as complex, emergent, and spatially embedded market phenomena.

Social Sensing of the Imbalance of Urban and Regional Development in China Through the Population Migration Network around Spring Festival

Regional development differences are a universal problem in the economic development process of countries around the world. In recent decades, China has experienced rapid urban development since the implementation of the reform and opening-up policy. However, development differs across regions, triggering the migration of laborers from underdeveloped areas to developed areas. The interaction between regional development differences and Spring Festival has formed the world’s largest cyclical migration phenomenon, Spring Festival travel. Studying the migration pattern from public spatiotemporal behavior can contribute to understanding the differences in regional development. This paper proposes a geospatial network analytical framework to quantitatively characterize the imbalance of urban/regional development based on Spring Festival travel from the perspectives of complex network science and geospatial science. Firstly, the urban development difference is explored based on the intercity population flow difference ratio, PageRank algorithm, and attractiveness index. Secondly, the community detection method and rich-club coefficient are applied to further observe the spatial interactions between cities. Finally, the regional importance index and attractiveness index are used to reveal the regional development imbalance. The methods and findings can be used for urban planning, poverty alleviation, and population studies.

Network topological approach to modeling accident causations and characteristics: Analysis of railway incidents in Japan

Railway networks are a critical infrastructure in today's society, and many efforts have been made to promote their safety. Even when safety measures are introduced, railway incidents still occur. Incident investigation can acquire facts about the causation factors and effects of incidents. In this study, we propose a network analytical framework to identify the factors and effects of railway incidents. This framework captures the complexity of interdependencies and delineates the causation of railway incidents in incident chains; the incident chains are then remodeled to study the topological characteristics of factors and effects in a network model. Reported railway incident cases in Japan are selected for analysis from three perspectives: local view analysis, global view analysis, and contextual view analysis. Statistics and network diagrams are developed from each perspective to analyze the relational railway incident data. The results show that most railway incidents are caused by more than one factor, and one factor tends to cascade into others. Each factor has its own role in the occurrence of an effect, but interdependencies can still be found among most factors. The significant factors in railway incidents are revealed in this study, and it is suggested that tackling the occurrence of the significant factors and their interdependent factors is effective for controlling and reducing the occurrence of railway incidents. The network analytical framework can be used to forward estimate the effect specifically caused by a factor and its propagation exposure, as well as to backward determine the possible causes of an effect from its interdependent intermediates and factors.

Family Firms in the Ownership Network: Clustering, Bridging, and Embeddedness

In this article, we investigate family firms' position in the intercorporate ownership network. Rooting our predictions in the Behavioral Agency Model and a Network analytical framework, we predict and find that family involvement decreases the likelihood of business group affiliation and of cross-group ties leading to a lower embeddedness within the overall network. We predict and find the opposite effect for community involvement. We use the complete longitudinal dataset of publicly listed firms' corporate ownership ties in India (2001, 2005, and 2009). Theoretical and substantive contributions are to research on family businesses and to research on interorganizational networks.

Detecting and visualizing cohesive activity-travel patterns: A network analysis approach

This article presents a network analytical framework to detect individual-based activity-travel patterns (ATPs) in space and time. Compared to many existing classification methods (e.g., hot-spot detection, sequential alignment method), the network method substantiates the social meanings underlying the interconnectedness and similarities of people's activity trajectories and better integrates spatial interaction (colocation or distance-decay) and temporal connections (concurrence or sequence) of daily lives in the measure of similarity. This approach enables us to detect variant community structures, with individuals in the same community interacting relatively more than individuals belonging to different communities, by decomposing the complex trajectories into different meaningful events (e.g., activities, trips, tours, and subsequences). We also demonstrate the practicality and scientific merit of the network analysis approach in a case study of household travel behavior in Charlotte, North Carolina. Results derived from disaggregated survey data establish the effectiveness and flexibility of the network methods to detect cohesive communities of individuals and ATPs by different narratives of everyday-life events. This study also suggests that the network analysis approach has great potential to classify large datasets of other space-time trajectories and to discover policy-sensitive activity, trip, and tour patterns that help us develop policy and planning alternatives for sustainable communities and mobility.

Networks and Innovation in the Production of Communication: Explaining Innovations in U.S. Electoral Campaigning From 2004 to 2012

We outline a network analytic framework for analyzing the production of communication. In our framework, individuals—in part the products of the history of their social and professional ties—merge from various fields and a medley of prior production experiences within organizations to produce communicative innovations. Organizations with individuals who have diverse backgrounds and significant overlap in work experiences will be more innovative. We demonstrate this through a network analysis of the professional biographies of 629 staffers on U.S. presidential campaigns from 2004 to 2012. Democratic staffers came from more diverse organizations and shared significant overlap in prior experiences than their Republican counterparts. Through interview data, we argue that this in part explains Democratic innovativeness in technology during this period.