Applied Network Analysis: A Methodological Introduction

Networks, where nodes/vertices denote entities and links/edges denote associations, provide a unified representation for a variety of complex systems, from social relationships to molecular interactions. In general, network analysis has proved useful in applications such as prediction of protein function, guiding the design of wet-lab experiments, and discovery of biomarkers of disease. Driven by the availability of large-scale data sets and appropriate informatics’ tools, the research community has begun to apply network analysis of these data to define underlying causes of pediatric diseases. This will almost certainly lead to more effective strategies for prevention and treatment. In this chapter, we will introduce classic and the state-of-the-art network analysis methodologies, approaches and their applications. We then provide three examples of recent research, where network analysis is being applied in pediatrics. These include identification of targets for development of new drugs to treat Pseudomonas infections, protein interactions in high-density lipoprotein particles that underlie development of cardiovascular disease, and analysis of networks in brain that may underlie conditions such as Attention Deficit Hyperactivity Disorder.KeywordsGene OntologyBiological NetworkBrain NetworkMaximal CliqueShort Path LengthThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Stress management during examinations is an issue perpetually concerning students at all levels of education. This research endeavor focuses not merely on the plethora of factors associated with stress during examinations but rather focuses primarily on specific emotions developed during written and oral exams, introducing an original method, based on electroencephalograph (EEG) data. In an attempt to address the role of emotions in stress management, we organized an experiment on a sample of 30 postgraduate students, administering the Trier research protocol. The validated Emotiv Epoc+ device was used, while acquired the data were processed using a combined methodology of network and time series analysis applied in three (3) steps. Initially a descriptive analysis was performed based on the extracted frequencies, while in the second phase a network analysis was conducted to understand the extend of coordination between measured emotions (stress, engagement, interest, relaxation, excitement, and focus), using the tools of centrality. Finally, time series analysis was employed to indicate the level of auto- and cross-correlation between stress and other emotions. Our findings show that (eu)stress plays an imperative role in examination success, as long as it normally fluctuates within middle-range values (on a scale 0–100), and is being restrained by other feelings, such as interest, excitement, and focus

Do emerging industrial nations have little hope in the face of the new technological revolution 4.0, given the powerful self-reinforcing mechanisms characteristic of the innovation systems in central regions? Or can socio-technical trajectories, initially emerging as niches but later evolving into sectors or even networks, pave the way for technological leapfrogging in these emerging countries? If so, can we envision specific techno-industrial policies to facilitate these leaps, and more importantly, can we develop specific tools and methods to diagnose and promote such policies?This paper aims to contribute to the improvement and application of a Structural Network Analysis (SNA) methodology to identify, within a large set of longitudinal patent data, in a subdomain of industry 4.0 (namely, green technologies), the most promising technological domains for a given country under study (South Korea).The interest of this perspective on technological economic policies is to target a technological domain that is both critical and stimulating enough to generate a network effect, yet sufficiently niche to not require capital beyond the capacity of an emerging nation.In the current state of our intermediate research results, the findings are only partially satisfactory. The results regarding the most promising technologies appear significant only half of the time (whereas in the reference study, they tend towards 100%). Nevertheless, many avenues for improvement in research remain open.JEL classification: 030.

Network analysis offers a novel framework for understanding the dynamic interconnections among symptoms, moving beyond traditional approaches to identify central symptoms that drive illness courses and treatment response. Recent studies have used this method to examine how symptoms group together and influence one another in unipolar depression; however, its application to bipolar disorder (BD) remains limited. This review systematically examines literature that applied network analysis methodology to BD to investigate central symptoms and their interrelationships within this condition. A systematic search of PubMed, Web of Science, PsycINFO, Embase, and Scopus databases was conducted from inception to January 2026 to identify studies that applied network analysis to examine mood symptom centrality in individuals with BD. Eleven studies met the inclusion criteria, encompassing diverse BD populations including young adults, and mixed-age samples across different illness phases. Across depressive symptom networks, depressed mood, low energy, and negative self-concept emerged as central nodes, while high energy, pressured speech, and elevated self-esteem were most central in networks of manic symptoms. Methodological and reporting heterogeneity, including variations in network estimation techniques, sample characteristics, and symptom assessment instruments, limited the comparability of findings. These findings advance understanding of central symptoms and network structures in BD, revealing consistent patterns across depressive and manic symptom networks. Identifying key symptom patterns and their interconnections may inform future symptom-targeted research and enhance understanding of symptom dynamics in BD.

Based on network analysis methodology, a network model was constructed to elucidate the relationship between academic stress and depressive symptoms among middle school students. By identifying core nodes (high-strength symptoms) and bridge nodes (cross-group associated symptoms) within the two symptom clusters, the study reveals the interactive pathways between these constructs. This approach provides a foundation for precise mental health interventions tailored to middle school students. A total of 1,576 middle school students in central Anhui Province were selected by the cluster sampling method, and questionnaire surveys were conducted using the Academic Stress Scale for Middle School Students and the Center for Epidemiologic Studies Depression Scale (CES-D). Data analysis was conducted using SPSS 26.0 and R software (Version 4.4.1). The partial correlation network was estimated using the EBICglasso algorithm (γ = 0.5) in R software (Version 4.4.1). Node strength centrality and bridge strength centrality were calculated, and the stability of the network was assessed using the correlation stability (CS) coefficient. The network structure of academic stress and depressive symptoms among middle school students comprises 308 valid edges, forming two tightly connected clusters. In the Academic Stress Scale, SS16 (strength = 1.169) and SS12 (strength = 1.136) were identified as core nodes; SS20 (bridge strength = 0.248) and SS14 (bridge strength = 0.185) exhibited higher bridge strengths, with SS20 (bridge strength = 0.248) serving as the critical bridge node.In the Depression Scale, CES-D6 (strength = 1.211) and CES-D3(strength = 1.189) exhibited higher strengths, while CES-D16(bridge strength = 0.189) and CES-D20(bridge strength = 0.157) showed higher bridge strengths. The cross-symptom clusters edge between SS20 (“When I am upset, I feel I have no close friends to talk to.“) and CES-D14(“I felt lonely.“) had the highest edge weight (0.137). The network stability test revealed that both strength centrality (CS = 0.75) and bridge strength centrality (CS = 0.67) exceeded the recommended threshold (> 0.5). Academic stress and depressive symptoms form a dynamic interconnected network through social isolation (SS20) and low life satisfaction (CES-D16), suggesting the need to design targeted intervention strategies focusing on bridge nodes to mitigate their negative impact on middle school students.

Data and network embedding techniques are essential for representing complex data structures in a lower-dimensional space, aiding in tasks like data inference and network reconstruction by assigning nodes to concise representations while preserving the network's structure. The integration of Particle Swarm Optimization (PSO) with matrix factorization methods optimizes mapping functions and parameters during the embedding process, enhancing representation learning efficiency. Combining PSO with techniques like Deep Walk highlights its adaptability as a robust optimization tool for extracting meaningful representations from intricate data and network architectures. This collaboration significantly advances network inference and reconstruction methodologies by streamlining the representation of complex data structures. Leveraging PSO's optimization capabilities enables researchers to extract high-quality information from data networks, improving the accuracy of data inference outcomes. The amalgamation of PSO with data and network embedding methodologies not only enhances the quality of extracted information but also drives innovations in network analysis and related fields. This integration streamlines representation learning and advances network analysis methodologies, enabling more precise data inference and reconstruction. The adaptability and efficiency of PSO in extracting meaningful representations from complex data structures underscore its significance in advancing network inference and reconstruction techniques, contributing to the evolution of network analysis methodologies.

Research background: Recent financial crisis of 2007?2008 has influenced global banking system and led to reduction of cross-border bank lending in the EU and worldwide. Global banking network has been analysed extensively in prior or post-crisis periods, but the literature on regionalization is scarce, especially with regard to the banking sector in the EU. Moreover, in previous empirical research evaluation of banking sector regionalization using network analysis methodology has not been yet applied.
 Purpose of the article: The aim of the article is to map the EU banking network and to assess its regionalization during post-crisis period.
 Methods: the paper employs comparative literature analysis and synthesis; BIS bilateral interbank cross-border claim yearly flows matrix data and network analysis method (including network mapping, structural and comparative analysis and the data of intraregional and interregional banking network matrices) to assess the changes in regionalization of the EU banking system.
 Findings & Value added: The results of the research show that during post-crisis period both, EU 12 and EU 28, banking networks became more clustered and more decentralized; also the level of regionalization within the EU banking network increased. Such results prove that the EU banking network has undergone structural changes with respect to bilateral interbank cross-border claims. This research adds to the knowledge of regionalization processes within the EU banking network during the post-crisis period and intends to be beneficial for market participants, EU level governmental bodies and financial policy makers.

Space institutions place humans in analog environments that closely match the target environment, such as the Moon or Mars, to study space habitation. Seven Mars analog missions consisting of habitation units with published designs were selected for analysis using graph data science methodologies to provide insight into how their designers solved the challenge of living in an isolated, constrained, and distant environment. Social Network Analysis (SNA) and Label Propagation Approach (LPA) are utilized to examine the relationships between adjacent functional areas of habitations. The paper presents the final documentation that will be used as a design framework to generate layouts for a habitation project.

Vehicular Social Networks: A survey

Network research within and beyond Management and Organization Studies (MOS) is booming in recent decades. Numerous scholarly debates have been held on the development of network research, both as a methodology and as networks in and of themselves as organizational forms. This chapter adds to a particular stream within MOS, namely Business administration scholarship that applies Social Network Analysis (SNA) to research inter-organizational networks. I first delineate the shortcomings of this stream in doing network research, to then combine current advances of SNA with a theoretical grounding of inter-organizational networks as material-discursive entities in a critical political economy perspective. I argue for the politico-economic embeddedness of inter-organizational networks, considering that inter-organizational networks (1) are historically contingent, (2) always involve multiple agents including the state, and (3) are arenas of power asymmetry. Four essential dimensions of inter-organizational networks in business environments are prevalent in shifting from a more mainstream to a dialectical understanding of the same in MOS: State-industry relations, labor-capital relations, technology, and cooperation and competition. This dialectical approach towards inter-organizational networks necessitates a critical methodology, which foregrounds the convergence of multiple sources of qualitative and quantitative data stemming from diverse research methods. Therefore, SNA is advanced through re-introducing the long-neglected methodology of Dialectical Network Analysis (DNA) to Management and Organization Studies.

We propose a semantic-based methodology for Social Network Analysis (SNA). This methodology addresses computations needed for SNA in a declarative way -in contrast to traditional SNA where computations are procedural. Our ingredients are semantic technologies: We define an ontology to represent graphs, their components (nodes, edges or paths), and the structural relationships between these components. We exploit reasoning capabilities of ontologies to infer structural relations between graph components. We also use ontological queries to perform computations needed in SNA. To demonstrate how does this approach work, we present three showcases of typical network analysis: basic metrics, triadic census, and betweenness centrality. The proposed approaches offer several computational opportunities for analyzing networks with respect to calculation of path-dependent centrality metrics, e.g. in distributed setups.

The origins and methodology of network analysis

Application of Network Analysis in tourism research is relatively new, especially in the study of tourism distribution channels. Network Analysis is employed to investigate the structure and pattern of relationships between actors in anetwork. This paper applies Network Analysis with ORA software to analyze tourism distribution channels. The results of the research show the pattern of the network between tour operators and travel agencies and between tour operators. Network Analysis also reveals the cooperation and cohesion of the network as well as the network dynamics between the case study tour operator and its travel agencies over a period of time.

Migration’s influence in shaping population dynamics in times of impending climate and population crises exposes its crucial role in upholding societal cohesion. As migration impacts virtually all aspects of life, it continues to require attention across scientific disciplines. This study aims to bridge the gap between theoretical understanding and practical application by integrating network analysis and regression methodologies within Migration Studies. In the study we employ network analysis to elucidate migration patterns at various geographical scales-city, country, and global. Additionally, regression analysis is discussed on an exploratory level, where we focus on the underlying factors driving migration, and identifying the key independent variables to enhance predictive accuracy. The study exposes distinct migration network structure and its features, and the consequences these have on conventional regression analysis applications. We conclude on the importance of methodological coherence and disciplinary integration, and highlight the avenues for enhancing the predictive power of migration models.

The investigation of gene sets identified in experiments and analyses is a common task for biologists. This includes identification of relevant pathway networks, neighborhoods of protein interactions, and associations with drugs and diseases. However, services that provide these analyses face the following challenges: (1) Curated pathway content tends to lack biological context and lag behind recent findings, as it focuses on consensus biology and is expensive to maintain; (2) User interfaces for network analyses need to be transparent and intuitive, without requiring complex parameter choices; and (3) Users have diverse, changing network analysis needs, requiring an evolving portfolio of methods and data. To address these challenges, we present NDEx Integrated Query (IQuery), a novel and flexible resource for gene set analysis providing enrichment, protein-protein interaction, and protein association analyses based on pathways and networks from NDEx, the Network Data Exchange. IQuery provides researchers with a simple “one click” query interface which runs multiple simultaneous queries without the need to specify the analyses to perform, query parameters, or network data sources. The results are aggregated and presented in a rich, intuitive interface for the user to browse, preview, save, or analyze. For each network, the user can view descriptions, citations, author information and other metadata, and inspect the data associated with each node and edge. The IQuery database is NDEx, a public data commons which is constantly growing via the addition of new public network resources and networks submitted by the authors of publications. The incorporation of published networks in IQuery enables timely coverage of new research as well as clear biological context for each pathway, and fosters cross-discipline insights and collaboration between teams. IQuery is also integrated with Cytoscape, the widely used network visualization and analysis application, enabling query result networks to be seamlessly downloaded to Cytoscape for editing, merging with other networks, annotation with user datasets, or use with any of hundreds of community developed apps. IQuery is built as a modular, web service-oriented platform. The primary IQuery service distributes each query to all component services using a standard API. This enables easy addition of new analyses, including those written by collaborators. This allows IQuery to grow in two ways: Through the growth of the NDEx database, and through the addition of modules providing new gene set analysis tools. In sum, IQuery is a powerful and versatile new network analysis service with collaboration and extensibility at its core, which will allow users to find, access, and analyze networks relevant to their research, and provide opportunities for cross-discipline communication and insight. Citation Format: Dexter R. Pratt, Keiichiro Ono, Sophie Liu, Jing Chen, Christopher Churas. IQuery: A tool aggregating multiple gene set analysis methods, based on networks and pathways in a community-driven public data repository [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3206.