Network Analysis Techniques Research Articles

Understanding trends, patterns, and dynamics in global company acquisitions: a network perspective

Studying acquisitions offers invaluable insights into startup trends, aiding informed investment decisions for businesses. However, the scarcity of studies in this domain prompts our focus on shedding light in this area. Employing Crunchbase data, our study delves into the global network of company acquisitions using diverse network analysis techniques. Our findings unveil an acquisition network characterized by a primarily sparse structure comprising localized dense connections. We reveal a prevalent tendency among organizations to acquire companies within their own country and industry, as well as those within the same age bracket. Furthermore, we show that the country, region, city, and category of the companies can affect the formation of acquisition relationships between them. Our temporal analysis indicates a growth in the number of weakly connected components of the network over time, accompanied by a trend toward a sparser network. Through centrality metrics computation in the cross-city acquisition network, we identify New York, London, and San Francisco as pivotal and central hubs in the global economic landscape. Finally, we show that the United States, United Kingdom, and Germany are predominant countries in international acquisitions. The insights from our research assist policymakers in crafting better regulations to foster global economic growth, and aid businesses in deciding which startups to acquire and which markets to target for expansion.

Gut Microbiota and Metabolic Alterations Associated with Heart Failure and Coronary Artery Disease.

This study investigates the role of gut microbiota in cardiovascular diseases, with an additional focus on pro-atherogenic metabolites. We use advanced network analysis and machine learning techniques to identify key microbial features linked to coronary artery disease (CAD) and heart failure with reduced ejection fraction (HFrEF). This cross-sectional study included 189 participants divided into three groups: coronary artery disease (n = 93), heart failure with reduced ejection fraction (n = 43), and controls (n = 53). Assessments included physical exams, echocardiography, dietary surveys, blood analysis, and fecal analysis. Gut microbiota composition was analyzed using next-generation sequencing (NGS) and quantitative polymerase chain reaction (qPCR). Statistical analysis methods for testing hypotheses and correlations, alpha and beta-diversity analyses, co-occurrence networks, and machine learning were conducted using Python libraries or R packages with multiple comparisons corrected using the Benjamini-Hochberg procedure. Significant gut microbiota alterations were observed, with higher Bacillota/Bacteroidota ratios in CAD and HFrEF groups compared to controls (p < 0.001). Significant differences were observed in α-diversity indices (Pielou, Chao1, Faith) between disease groups and controls (p < 0.001). β-diversity analyses also revealed distinct microbial profiles (p = 0.0015). Interestingly, trimethylamine N-oxide (TMAO) levels were lower in CAD and HFrEF groups compared to controls (p < 0.05), while indoxyl sulfate (IS) levels were comparable between the study groups. Co-occurrence network analysis and machine learning identified key microbial features linked to these conditions, highlighting complex interactions within the gut microbiota associated with cardiovascular disease.

Exploring Spatiotemporal Interaction Patterns of Dockless Shared Bicycle Networks: A Case Study of Shenzhen, China

Abstract. The explosive development of shared bicycles has changed the way people travel. In parallel with the urbanization process, shared bicycles have emerged as a significant mode of transportation, especially for the first and last mile transit. With the aim of obtaining a comprehensive understanding of the spatiotemporal patterns underlying shared bicycle usage, this study takes Shenzhen, China as a case study. Drawing on the principles and techniques of complex network analysis, the research adopts a spatial grid framework, treating each grid as a node, the flow of shared bicycles as an edge, and the flow frequency as the corresponding weight. Subsequently, the spatiotemporal interaction networks of different periods of working days and weekends are constructed respectively, based on which the topological characteristics are analysed. The results show that the spatiotemporal interaction networks exhibit high regional connectivity and node interdependence, suggesting the presence of a small-world phenomenon.

Hidden neighbours: extracting industry momentum from stock networks

AbstractThis paper introduces an innovative method for constructing industry momentum portfolios by leveraging two stock networks: one based on stock price correlations and the other on corporate text similarity. We find that these networks capture different aspects of company relationships, motivating us to combine them and form a portfolio that exploits less visible industry momentum. Our Hidden Neighbours portfolio, analysed from 2013 to 2022, delivered an annualised return of 18.16% with a Sharpe ratio of 0.85, outperforming the S&amp;P 500 and other traditional momentum strategies. Factor decomposition attributes returns primarily to the idiosyncratic factor $$\alpha$$ α . Our study employs interdisciplinary methods, merging network analysis and Natural Language Processing (NLP) techniques for portfolio construction. Utilising advanced text embedding models, we enhance portfolio construction by integrating textual insights from corporate disclosures into stock networks. The paper offers a comprehensive strategy across diverse data and the interdisciplinary approach, uniting financial theory, network science, and NLP, advances both theory and practice of portfolio management.

뉴스 기사 빅데이터 분석을 통한 북한이탈주민의 사회적 이슈와 언어 문제 탐색: 2019-2024

Objectives The purpose of this study is to explore social issues and language problems that appear in news articles related to North Korean defectors and to suggest their implications. Methods 561 news articles related to North Korean defectors covered by major domestic newspapers and media companies from 2019 to June 2024 were collected. For analysis tools, language network analysis and topic modeling techniques were used to analyze major keywords and topics and visualize them. In addition, based on the analysis results, social issues and language problems of North Korean defectors were explored, and Korean language education implications were presented. Results he research results are as follows. First, the amount of news coverage has been increasing significantly over the past three years, and political articles have shown an overwhelmingly high proportion. Second, the main keywords are ‘government, support, human rights, protection, and settlement’. The main topics were protection of human rights of North Korean defectors, increasing number of North Korean defectors among elites and the 2030 generation, and complaints of difficulties due to mental and economic discrimination and prejudice in the process of settling in Korea. The content of the government's integrated support policy accounted for a high proportion. Third, it was found that North Korean defectors were struggling with the language difference between North and South Korea, and Korean language education focusing on pronunciation, accent, and vocabulary in each area was necessary for them. Conclusions The main topic of the news article was the integrated support for the protection of human rights and stable settlement of North Korean defectors. However, although North Korean defectors complain of ‘difficulty’ due to economic, psychological, emotional, and linguistic differences, these related topics are hardly covered in the media. Therefore, this paper suggested implications for Korean language education research for North Korean defectors' language adaptation.

A dynamic resilience management framework for deep-tier supply networks

The unprecedented supply chain disruptions caused by COVID-19 has had severe operational and financial consequences to firms across industries and continents. While tactical reactionary strategies can help, firms are in need of proactive management approaches to design more resilient supply chain networks in the first place. Firms are looking for an effective framework to design and monitor supply networks, mitigate disruption consequences, and manage resilience under different scenarios. We propose a framework to manage the resilience of deep-tier automotive supply networks by integrating a simulation-based resilience assessment scheme for effectiveness with an efficient optimization-based framework to find optimal strategies for handling regular disruption events. The framework promotes network analysis techniques combined with discrete-event simulation informed by secondary data sources and global supply risk databases for improving resilience management. We validate the effectiveness of the proposed framework using a real-world global automotive original equipment manufacturer case study. Our results demonstrate that the proposed dynamic framework relying on deep-tier visibility can optimize resilience strategies through all key performance indicators. The results show an average of 35% and 40% reductions in back-ordered cost and shipment delays, respectively, with a marginal growth in holding cost when the proposed framework is implemented with deep-tier visibility.

A micro-network within the port for vessel anchorage selection decision support

Upon arrival at ports, selecting the appropriate anchorage is a critical concern shared by ship captains and port managers. This study aims to exploit the rich geographic and semantic information embedded in historical ship trajectory data to construct a micro-network of port elements and employ network analysis techniques to mine spatial service relationships among port elements, thereby establishing an Anchorage Selection Decision Support (ASDS) model. This process involves extracting vessels’ dwell location information within ports from trajectory data and using this information and its sequence to establish an Anchorages-Berths Micro-Network (ABMN) in port. By applying association rule mining techniques, this research reveals the spatial service relationships within the micro-network between anchorages and berths, and integrates relevant indicators to develop the ASDS model. The aim is to help pilots of approaching vessels in making more rational anchorage choices, thus optimizing the operational efficiency of vessels in ports. The effectiveness of this approach has been validated through experiments conducted in the research region at the Nanjing port of the Yangtze River. This research is significant for exploring the correlation between port anchorages and berths, as well as for selecting appropriate anchorages for vessels arriving at the port.

Connectivity Analysis of Non-Container Ports at PT Pelindo Multi Terminal Using SNA and GIS / Connectivity Analysis of Non-Container Ports at PT Pelindo Multi Terminal Using SNA and GIS

This study aims to analyze the connectivity of non-containerized ports, particularly in the sub-commodity of vegetable/animal oils and fats, within the operational area of PT Pelindo Multi Terminal. Using Social Network Analysis (SNA) and Geographic Information System (GIS) techniques, this study builds a network based on nodes (ports) and assesses inter-node connections with metrics such as degree centrality, betweenness centrality, and hub index. Gephi application was used for the calculation of network metrics, while QGIS visualized the shipping routes. The research revealed that high traffic ports such as Belawan, Dumai, and Tanjung Priok play a crucial role in the network. The findings support recommendations for infrastructure development and implementation of an integrated Terminal Operations System (TOS) to improve logistics efficiency.

Bioinformatics approaches for studying molecular sex differences in complex diseases.

Many complex diseases exhibit pronounced sex differences that can affect both the initial risk of developing the disease, as well as clinical disease symptoms, molecular manifestations, disease progression, and the risk of developing comorbidities. Despite this, computational studies of molecular data for complex diseases often treat sex as a confounding variable, aiming to filter out sex-specific effects rather than attempting to interpret them. A more systematic, in-depth exploration of sex-specific disease mechanisms could significantly improve our understanding of pathological and protective processes with sex-dependent profiles. This survey discusses dedicated bioinformatics approaches for the study of molecular sex differences in complex diseases. It highlights that, beyond classical statistical methods, approaches are needed that integrate prior knowledge of relevant hormone signaling interactions, gene regulatory networks, and sex linkage of genes to provide a mechanistic interpretation of sex-dependent alterations in disease. The review examines and compares the advantages, pitfalls and limitations of various conventional statistical and systems-level mechanistic analyses for this purpose, including tailored pathway and network analysis techniques. Overall, this survey highlights the potential of specialized bioinformatics techniques to systematically investigate molecular sex differences in complex diseases, to inform biomarker signature modeling, and to guide more personalized treatment approaches.

Network Issue Agenda Setting on Facebook: Exploring the Interplay Between Polarized Campaigns and Party Supporters

This study undertook an analysis of network agenda setting during the 2020 U.S. Presidential campaign, focusing on the interactions between the campaigns and their respective supporters within the context of a polarized social media environment. By employing social network analysis techniques to examine issue agendas, the study revealed a relatively weak correlation between the agendas of the campaigns and their affiliated supporters on Facebook. Conversely, it found a notable association between entities sharing the same ideological orientation—party supporters displayed a higher degree of engagement with their own party’s campaign, and vice versa. The implications of these findings, from a theoretical, methodological, and practical standpoint, have been thoroughly discussed.

Diplomatic webs: the influential figures shaping U.S. policy in Israel, Qatar, and Iraq

Abstract Purpose This study examines the Twitter/X networks of U.S. ambassadors in Israel, Iraq, and Qatar from 2017 to 2024, aiming to assess how digital diplomacy is conducted through these platforms. Utilizing Sprinklr, we gathered 586,736 mentions involving the ambassadors’ Twitter handles, with a focus on evaluating the influence and communication strategies within these networks. Design/methodology/approach We analyzed a random sample of 30,000 tweets from the collected data using network analysis techniques. This approach enabled the examination of centrality metrics within the ambassadors’ digital networks, providing insights into the influence patterns and communicative interactions via Gephi. Findings The analysis revealed a significant influence of state actors and established political elites who predominantly engage in unidirectional communication, despite the platforms’ capabilities for interactive and reciprocal dialogues. We identified elected officials and specific non-governmental organizations as key actors shaping the diplomatic narratives, highlighting the diverse yet controlled actor interplay in digital diplomacy. Practical implications This research underscores the need for strategic adjustments in digital diplomacy practices to enhance interaction and inclusivity. Our work provides policymakers with insights into leveraging digital platforms for more effective and dynamic diplomatic exchanges. Social implications The study illuminates the role of digital platforms as critical venues for shaping diplomatic narratives by both state and non-state actors. Notably, our findings highlight the use of hashtags in advancing human rights movements and in discussions surrounding the Israel–Palestine conflict, demonstrating hashtags’ impact on global and regional advocacy efforts. Originality/value This research offers a unique perspective on the integration of traditional diplomatic roles with contemporary digital strategies, particularly highlighting the constraints and potentials within Middle Eastern contexts. We suggest ways to enhance the inclusivity and effectiveness of diplomatic engagements through improved social media utilization, thereby contributing to the evolving field of international relations and public diplomacy.

The Urgency of Actors' Presence on Trending Hashtags on Twitter's New Media in the Frame of Social Network Analysis

The phenomenon of using social media as a new media to meet the needs of information in the community is the result of the rapid development of the internet. Now the needs of the community are easily met, because various things have been contained in it. This can be analyzed based on the phenomenon of trending topics through hashtags on social media twitter using Social Network Analysis (SNA). The purpose of writing this research article is that with so many soap operas in Indonesia that last more than one year on the Indonesian screen, it becomes an interesting thing, but it is not only in the real world, but has become a conversation in cyberspace, Twitter in particular. Using mixed method research methods with actor and network analysis techniques in the quantitative and technical domains of observation, record and descriptive analysis techniques, with Bruno Latour's network study approach. The subjects of the study were the network of actors and the flow of information on the hashtag #IkatanCintaEp1012. The use of Twitter social media as a new media can show networks and actors that make the spread of information and connectivity between fans in the virtual world increasingly stronger and faster in a hashtag network.

Research on provincial implied carbon emissions in China under the shared responsibility driven by new quality productivity: A new approach

The allocation of responsibility for carbon emissions among involved parties has garnered significant attention. This study proposes a comprehensive sharing coefficient methodology known as CVAT (direct carbon emissions, value added, and technology level) to enhance the accuracy and equity of responsibility sharing driven by the new productivity standard. This study utilizes the environmental expansion multi-region input-output (EEMRIO) model to assess China's implicit provincial carbon emissions over the previous two decades. The study investigates the characteristics of the implied carbon transfer network using social network analysis (SNA) techniques. Key findings include: (1) The CVAT method's determination of responsibility sharing coefficients aligns with fairness and efficiency principles, resulting in a more equitable distribution of implied carbon emission responsibilities conducive to achieving fair carbon emission reduction. (2) Over the research period, national implied carbon emissions exhibited an upward trajectory, increasing from 3.305 billion tons in 2002 to 9.726 billion tons in 2017, with Shandong, Hebei, and Jiangsu provinces ranking among the top emitters in China. (3) Eastern coastal and western regions function as net carbon transfer out regions, while the central, northeast, and southwest regions act as net carbon transfer in regions. (4) The implied carbon transfer network demonstrated relative stability throughout the study period, with Shandong, Hebei, Jiangsu, and Inner Mongolia emerging as major carbon transfer provinces. The study's outcomes hold significant implications for the impartial and accurate assessment of carbon emission responsibilities and the advancement of global carbon emission reduction efforts.

The longitudinal relationships between problematic mobile phone use symptoms and negative emotions: a cross-lagged panel network analysis

BackgroundThe association between problematic mobile phone use (PMPU) and negative emotions in university students is not well understood in terms of causality and directionality. This study aims to clarify whether negative emotions trigger PMPU or whether the PMPU itself leads to increased negative emotions over time. MethodsA two-wave longitudinal study was conducted involving 5568 Chinese freshmen who were surveyed at baseline and followed up after one academic year. PMPU, social media use, online game use, fear of missing out, loneliness, social anxiety, and academic burnout were measured. Cross-sectional and longitudinal connections between these variables were examined using network analysis techniques. ResultsThe variable with the strongest influence in both contemporaneous networks was “Productivity loss” of MAPI. Moreover, “Academic burnout” at baseline significantly predicted higher levels of problematic smartphone use and negative emotions at follow-up, suggesting that it may serve as a catalyst for addictive tendencies. Furthermore, we observed bidirectional relationships between “Escapism” and “Social anxiety”, as well as between “Social anxiety” and “Inability to control craving”, suggesting a potential self-perpetuating cycle. ConclusionThese findings highlight the role of academic burnout in initiating cycles of PMPU and negative emotions. In order to effectively tackle PMPU, it is crucial to consider the underlying drivers such as academic burnout and emotional states. This is important due to the complex and reciprocal associations uncovered through our longitudinal network analysis.

Analyzing Mexico's Indigenous internal migration dynamics through network centrality measures, 1990–2020

AbstractThis study examines the internal migration patterns among Mexico's Indigenous population from 1990 to 2020. We begin by estimating the total interstate migration flows for Indigenous groups and employ an advanced interaction component model to identify migration flows that exceed expectations. This model allows us to detect significant deviations and patterns within the migration data. Additionally, we apply network analysis techniques to identify states that are central to these migration flows and categorize states into distinct communities based on their migration interactions. Our findings reveal that Indigenous migration exhibits higher‐than‐expected flows, particularly from the West Central and North regions of Mexico. By contrast, non‐Indigenous migration shows greater flows, predominantly in the southern and central states. Through network analysis, particularly the use of eigenvector centrality, we identify Nayarit and Durango as key hubs for Indigenous migration, whereas Estado de Mexico and Ciudad de Mexico emerge as central nodes in non‐Indigenous migration. Our study highlights the growing significance of Mexico's northern region, with Nuevo León playing a crucial role in Indigenous and non‐Indigenous migration flow networks. This study's findings contribute valuable insights regarding the spatial dynamics of internal migration and the evolving migration patterns of Indigenous populations in Mexico.

Disruption of bacterial interactions and community assembly in Babesia-infected Haemaphysalis longicornis following antibiotic treatment

BackgroundA previous study highlighted the role of antibiotic-induced dysbiosis in the tick microbiota, facilitating the transstadial transmission of Babesia microti from nymph to adult in Haemaphysalis longicornis. This study builds on previous findings by analyzing sequence data from an earlier study to investigate bacterial interactions that could be linked to enhanced transstadial transmission of Babesia in ticks. The study employed antibiotic-treated (AT) and control-treated (CT) Haemaphysalis longicornis ticks to investigate shifts in microbial community assembly. Network analysis techniques were utilized to assess bacterial interactions, comparing network centrality measures between AT and CT groups, alongside studying network robustness and connectivity loss. Additionally, functional profiling was conducted to evaluate metabolic diversity in response to antibiotic treatment.ResultsThe analysis revealed notable changes in microbial community assembly in response to antibiotic treatment. Antibiotic-treated (AT) ticks displayed a greater number of connected nodes but fewer correlations compared to control-treated (CT) ticks, indicating a less interactive yet more connected microbial community. Network centrality measures such as degree, betweenness, closeness, and eigenvector centrality, differed significantly between AT and CT groups, suggesting alterations in local network dynamics due to antibiotic intervention. Coxiella and Acinetobacter exhibited disrupted connectivity and roles, with the former showing reduced interactions in AT group and the latter displaying a loss of connected nodes, emphasizing their crucial roles in microbial network stability. Robustness tests against node removal showed decreased stability in AT networks, particularly under directed attacks, confirming a susceptibility of the microbial community to disturbances. Functional profile analysis further indicated a higher diversity and richness in metabolic capabilities in the AT group, reflecting potential shifts in microbial metabolism as a consequence of antimicrobial treatment.ConclusionsOur findings support that bacterial interaction traits boosting the transstadial transmission of Babesia could be associated with reduced colonization resistance. The disrupted microbial interactions and decreased network robustness in AT ticks suggest critical vulnerabilities that could be targeted for managing tick-borne diseases.

Node attribute analysis for cultural data analytics: a case study on Italian XX–XXI century music

Cultural data analytics aims to use analytic methods to explore cultural expressions—for instance art, literature, dance, music. The common thing between cultural expressions is that they have multiple qualitatively different facets that interact with each other in non trivial and non learnable ways. To support this observation, we use the Italian music record industry from 1902 to 2024 as a case study. In this scenario, a possible research objective could be to discuss the relationships between different music genres as they are performed by different bands. Estimating genre similarity by counting the number of records each band published performing a given genre is not enough, because it assumes bands operate independently from each other. In reality, bands share members and have complex relationships. These relationships cannot be automatically learned, both because we miss the data behind their creation, but also because they are established in a serendipitous way between artists, without following consistent patterns. However, we can be map them in a complex network. We can then use the counts of band records with a given genre as a node attribute in a band network. In this paper we show how recently developed techniques for node attribute analysis are a natural choice to analyze such attributes. Alternative network analysis techniques focus on analyzing nodes, rather than node attributes, ending up either being inapplicable in this scenario, or requiring the creation of more complex n-partite high order structures that can result less intuitive. By using node attribute analysis techniques, we show that we are able to describe which music genres concentrate or spread out in this network, which time periods show a balance of exploration-versus-exploitation, which Italian regions correlate more with which music genres, and a new approach to classify clusters of coherent music genres or eras of activity by the distance on this network between genres or years.

Investigating the network ties between affect, attachment, and psychopathology

ObjectivesRecent years have seen an increased interest regarding theoretical and empirical associations of adult attachment security and primary affective traits concerning psychiatric disorders. In this study, network analysis technique is applied to dissect the links between both psychodynamic personality constructs and an array of psychopathological symptoms. MethodsA total sample of 921 (69.9 % female) participants from the general population was investigated. A regularized cross-sectional partial correlation network between attachment (Experiences in Close Relationships-Revised [ECR-RD8]), primary affective traits (Brief Affective Neuroscience Personality Scales [BANPS-GL]) and psychopathological symptoms (ICD-10-Symptom-Rating Questionnaire [ISR]) was estimated via the EBICglasso algorithm. Node centrality, predictability and bridge centrality were analyzed. To evaluate the stability of the network and evaluate the significance of differences, we employed bootstrap techniques. ResultsThe network was found to be stable, allowing reliable interpretations. We observed SADNESS, as well as depressive, PTSD and anxiety symptoms as the most influential nodes within the investigated network. Attachment AV and SADNESS were observed as nodes with the highest bridge centrality. ConclusionsThe results provide a data-driven in-depth look into the complex dynamics between psychopathological symptoms, attachment security and basic affective traits. Results underscore the critical interconnections between affect, attachment, and psychopathology, advocating for a psychodynamically informed systems approach in psychological research that considers the affective dimensions underlying human mental health.

Credit card fraud detection with advanced graph based machine learning techniques

&lt;p&gt;In the realm of credit card fraud detection, the landscape is continually evolving, demanding innovative approaches to stay ahead of increasingly sophisticated fraudulent activities. Our research pioneers a groundbreaking methodology that amalgamates the power of bipartite graph visualization with advanced machine learning techniques. This fusion yields a comprehensive framework capable of effectively evaluating the efficacy of a random forest classifier in uncovering fraudulent credit card transactions. Our study showcases the compelling application of this methodology, offering a paradigm shift in how we analyze and understand credit card fraud detection systems. By seamlessly integrating machine learning algorithms with network analysis, we provide a holistic view of the data, unveiling intricate patterns hidden within. At the heart of our approach lies the innovative use of bipartite graphs, which serve as a dynamic visual bridge between model predictions and real-world outcomes. This visual representation not only enhances interpretability but also facilitates a deeper understanding of the classifier’s performance. By visually mapping the relationships between transactions and their respective classifications, our methodology offers actionable insights into both successful detection and potential areas for improvement. Empowering analysts and stakeholders, our approach facilitates informed decision-making by enabling them to fine-tune model parameters and enhance the overall effectiveness of fraud detection systems. Through this synergy between cutting-edge machine learning and network analysis techniques, we provide a powerful tool to combat the critical challenge of credit card fraud prevention. Step into the future of fraud detection with our innovative methodology, where every transaction is scrutinized with precision, and where security is not just a possibility, but a promise fulfilled.&lt;/p&gt;

Network Subgraph-based Method: Alignment-free Technique for Molecular Network Analysis

Background: Comparing directed networks using the alignment-free technique offers the advantage of detecting topologically similar regions that are independent of the network size or node identity. Objective: We propose a novel method to compare directed networks by decomposing the network into small modules, the so-called network subgraph approach, which is distinct from the network motif approach because it does not depend on null model assumptions. Methods: We developed an alignment-free algorithm called the Subgraph Identification Algorithm (SIA), which could generate all subgraphs that have five connected nodes (5-node subgraph). There were 9,364 such modules. Then, we applied the SIA method to examine 17 cancer networks and measured the similarity between the two networks by gauging the similarity level using Jensen- Shannon entropy (HJS). Results: We identified and examined the biological meaning of 5-node regulatory modules and pairs of cancer networks with the smallest HJS values. The two pairs of networks that show similar patterns are (i) endometrial cancer and hepatocellular carcinoma and (ii) breast cancer and pathways in cancer. Some studies have provided experimental data supporting the 5-node regulatory modules. Conclusion: Our method is an alignment-free approach that measures the topological similarity of 5-node regulatory modules and aligns two directed networks based on their topology. These modules capture complex interactions among multiple genes that cannot be detected using existing methods that only consider single-gene relations. We analyzed the biological relevance of the regulatory modules and used the subgraph method to identify the modules that shared the same topology across 2 cancer networks out of 17 cancer networks. We validated our findings using evidence from the literature.