Network Science Research Articles

The simpliciality of higher-order networks

Higher-order networks are widely used to describe complex systems in which interactions can involve more than two entities at once. In this paper, we focus on inclusion within higher-order networks, referring to situations where specific entities participate in an interaction, and subsets of those entities also interact with each other. Traditional modeling approaches to higher-order networks tend to either not consider inclusion at all (e.g., hypergraph models) or explicitly assume perfect and complete inclusion (e.g., simplicial complex models). To allow for a more nuanced assessment of inclusion in higher-order networks, we introduce the concept of “simpliciality” and several corresponding measures. Contrary to current modeling practice, we show that empirically observed systems rarely lie at either end of the simpliciality spectrum. In addition, we show that generative models fitted to these datasets struggle to capture their inclusion structure. These findings suggest new modeling directions for the field of higher-order network science.

Synchronization on higher-order networks

Network science has already been fruitful and confirmed effective on the description of real-world or abstract systems. An increasing number of researches and instances have successfully verified, however, that interactions in systems may occur among three, four, or even more components. The introduction of higher-order perspective brings a revolution on network science, and refreshes researchers’ understanding of synchronization. Hence, an overview is presented here in regard of synchronization on higher-order networks. We start from an introduction of how the higher-order networks are represented using algebraic tools. Then a series of landmark researches on synchronization is reviewed under circumstances of whether or not the dynamics contains control. Finally, we summarize our conclusions and propose our outlooks on expectations of future works.

Circular Economy and Tourism: A Bibliometric Journey Through Scholarly Discourse

This study conducts a bibliometric analysis of the integration of circular economy principles into the tourism industry, utilizing 181 articles. Employing Scientific Production Performance Analysis and Network and Cluster Analysis, this research explores key dimensions such as authorship, sources, countries, institutions, and keywords. The findings revealed a growing body of literature focusing on the link between circular economy and tourism, although this connection is in its early stages. From circular economy perspective, waste management and recycling dimensions emerge as the most prominent aspects contributing to the discourse in tourism studies. Conversely, from the tourism perspective, nearly all facets of tourism concepts appear receptive to a circular economy, regardless of whether the studies pertain to tourism development or management. In conclusion, this study enriches our scholarly understanding of how circular economy principles intersect with tourism. It guides researchers toward key areas of interest, shaping future exploration for this dynamic scientific relationship.

Circular Economy and Tourism: A Bibliometric Journey Through Scholarly Discourse

This study conducts a bibliometric analysis of the integration of circular economy principles into the tourism industry, utilizing 181 articles. Employing Scientific Production Performance Analysis and Network and Cluster Analysis, this research explores key dimensions such as authorship, sources, countries, institutions, and keywords. The findings revealed a growing body of literature focusing on the link between circular economy and tourism, although this connection is in its early stages. From circular economy perspective, waste management and recycling dimensions emerge as the most prominent aspects contributing to the discourse in tourism studies. Conversely, from the tourism perspective, nearly all facets of tourism concepts appear receptive to a circular economy, regardless of whether the studies pertain to tourism development or management. In conclusion, this study enriches our scholarly understanding of how circular economy principles intersect with tourism. It guides researchers toward key areas of interest, shaping future exploration for this dynamic scientific relationship.

Exploring motivations for social games in virtual reality for smart cities

The Metaverse is experiencing widespread adoption by organizations aiming to elevate brand engagement, particularly in computer engineering’s burgeoning field focused on virtual world gamification. This trend spans various sectors, including network science, smart cities, and online games. This study aims to reveal the motivations driving the use of virtual media, providing valuable insights into diverse user preferences. Integrating this information into smart city development holds promise for enhancing responsiveness to user needs, making our dataset relevant for both governmental and private entities seeking to create virtual media platforms or smart cities tailored to user demands. This research contributes to the ongoing evolution of smart city design, aligning with user motivations and adapting to evolving trends in user behavior. The findings offer a foundation for informed decision-making, promoting a user-centric and responsive approach in developing virtual experiences within the smart city context. Our research paper focuses on investigating the motivation and necessity of virtual reality (VR) games for potential integration into smart cities. Targeting a specific demographic of 40 individuals aged 18 to 24 in the capital of Thailand, we utilized correlational research methods to explore motivations and needs for VR games. Through the development of a socially virtual game-free asset using Unity, our experiments shed light on the significant motivations for Generation Z in Thailand, emphasizing convenience, product appearance, and game effects. The findings highlight the pivotal roles of social engagement and the motivation for self-disclosure in building relationships. Additionally, our study suggests that social connections and shared experiences strongly motivate individuals to participate in gaming activities. Notably, female players are significantly influenced by social connections, while branding has a minimal impact on their motivation and desire.

Leveraging Semantic Information for Enhanced Community Search in Heterogeneous Graphs

Community search (CS) is a vital research area in network science that focuses on discovering personalized communities for query vertices from graphs. However, existing CS methods mainly concentrate on homogeneous or simple attributed graphs, often disregarding complex semantic information and rich contents carried by entities in heterogeneous graphs (HGs). In this paper, we propose a novel problem, namely the “Semantic Network Oriented Community Search with Meta-Structures in Heterogeneous Graphs (SNCS),” which aims to find dense communities that contain the query vertex, with vertices of the same type sharing similar topics. In response to this new problem, we present a novel approach, also named SNCS, representing the first solution employing meta-structures and topic constraints to tackle community search, leveraging both topological and latent features. To overcome the high-time complexity challenge posed by searching through meta-structures, we introduce a unique graph reconstruction technique. Our proposed method’s superiority is validated through extensive evaluations on real-world datasets. The results demonstrate a significant improvement in the quality of the obtained communities, with increases of 3.5–4.4% in clustering coefficient and 5–11% in density while requiring only 4–46% of the running time when compared with the state-of-the-art methods.

Reconstruction of multiplex networks via graph embeddings.

Multiplex networks are collections of networks with identical nodes but distinct layers of edges. They are genuine representations of a large variety of real systems whose elements interact in multiple fashions or flavors. However, multiplex networks are not always simple to observe in the real world; often, only partial information on the layer structure of the networks is available, whereas the remaining information is in the form of aggregated, single-layer networks. Recent works have proposed solutions to the problem of reconstructing the hidden multiplexity of single-layer networks using tools proper for network science. Here, we develop a machine-learning framework that takes advantage of graph embeddings, i.e., representations of networks in geometric space. We validate the framework in systematic experiments aimed at the reconstruction of synthetic and real-world multiplex networks, providing evidence that our proposed framework not only accomplishes its intended task, but often outperforms existing reconstruction techniques.

Coalitions in international litigation: a network perspective.

We apply network science principles to analyse the coalitions formed by European Union nations and institutions during litigation proceedings at the European Court of Justice. By constructing Friends and Foes networks, we explore their characteristics and dynamics through the application of cluster detection, motif analysis and duplex analysis. Our findings demonstrate that the Friends and Foes networks exhibit disassortative behaviour, highlighting the inclination of nodes to connect with dissimilar nodes. Furthermore, there is a correlation among centrality measures, indicating that member states and institutions with a larger number of connections play a prominent role in bridging the network. An examination of the modularity of the networks reveals that coalitions tend to align along regional and institutional lines, rather than national government divisions. Additionally, an analysis of triadic binary motifs uncovers a greater level of reciprocity within the Foes network compared to the Friends network. This article is part of the theme issue 'A complexity science approach to law and governance'.

A network model of legal relations.

From at least the early twentieth century, legal scholars have recognized that rights and other legal relations inhere between individual legal actors, forming a vast and complex social network. Yet, no legal scholar has used the mathematical machinery of network theory to formalize these relationships. Here, we propose the first such approach by modelling a rudimentary, static set of real property relations using network theory. Then, we apply our toy model to measure the level of modularity-essentially, the community structure-among aggregations of these real property relations and associated actors. In so doing, we show that even for a very basic set of relations and actors, law may employ modular structures to manage complexity. Property, torts, contracts, intellectual property, and other areas of the law arguably reduce information costs in similar, quantifiable ways by chopping up the world of interactions between parties into manageable modules that are semi-autonomous. We also posit that our network science approach to jurisprudential issues can be adapted to quantify many other important aspects of legal systems. This article is part of the theme issue 'A complexity science approach to law and governance'.

Emerging studies of NMR-based metabolomics of fruits regarding botanic family species associated with postharvest quality

Fruit metabolic profiles indicate ripening stage, cultivar, phenotyping, and abiotic and biotic injuries, therefore constituting fruit quality parameters that are particularly important for the food industry. NMR-Metabolomics has been a powerful tool for studying metabolites and their biological processes. The fruit metabolic profile has been determined using NMR by several research groups worldwide. The present work aimed at compiling and organising the scientific literature on the metabolomics of fruits analysed by NMR, focusing on the botanic families. The scientific records related to the metabolomics of fruits were obtained from the Microsoft academic graph. Additionally, network science tools of clustering and topic modelling were used to analyse these data and identify the subjects of the papers. The results indicated that the most common botanic family studied is the Rosaceae family, which are the main commercial European fruits. By studying the scientific literature, it was possible to observe relationships among the taxonomy of fruits, their chemical compounds, and the scientific literature.

Multi-affiliation: a growing problem of scientific integrity

The past decade has witnessed a substantial increase in the number of affiliations listed by individual authors of scientific papers. Some authors now list an astonishing number of institutions, sometimes exceeding 20, 30, or more. This trend raises concerns regarding the genuine scientific contributions these authors make at each institution they claim to be affiliated with. To address this issue, our study conducted a comprehensive regional analysis of the growth of both domestic and international multi-affiliations over the past decade. Our findings reveal certain countries that have experienced an abnormal surge in international multi-affiliation authorships. Coupled with the high numbers of affiliations involved, this emphasizes the need for careful scrutiny of the actual scientific contributions made by these authors and the importance of safeguarding the integrity of scientific output and networks.

The conceptual model of the national blue economic system and its modeling principles: A supernetwork perspective

The national blue economic system is a complex system formed by the correlation of different agents, such as related blue industries, blue products, blue firms and blue provinces in a country. Its essence can be regarded as a complex system formed by the interconnection of a variety of heterogeneous networks. The supernetwork is an important method for studying such multilevel, multidimensional and multiagent correlation problems in network science. This method can effectively assess the intra- and interrelationships of each element entity. This paper incorporates the multielement entities in the national blue economic system into this multilayer network framework for systematic analysis and decomposes the development issue of the blue economy into the development issues of blue industries, blue products, blue firms and blue provinces. On this basis, this paper analyzes the conceptual model of the national blue economic system space, qualitatively describes the intra- and interexport relationships among multifactor entities in national blue economic development from the perspective of the multilayer network, and expounds the modeling principle of the national blue economic system space. This supernetwork approach provides effective new methods and tools for countries to study the development of the blue economy and formulate corresponding policies to achieve blue growth.

The role of social networks in communication in the scientific research community

Social networks have grown rapidly in recent years, enabling the application of social web technologies to the scientific process and creating platforms that enhance communication between researchers. The aim of this research is to go one step further and investigate whether the use of more general social networks, such as Twitter or Facebook, is also becoming more widespread for scientific research, thus contributing to the visibility of scientists and their collaborative networks. Social media analysis is carried out using the Brandwatch platform to assess the use of generalist social networks in research and compared with the use of scientific social networks through an online survey of university professors. The resulting conclusions show that scientific mentions in networks are rare and that, despite the importance of researchers having a profile in a social network, which allows them to give greater visibility to their results and receive feedback from their colleagues, many of them are still unaware of its usefulness.

A fast algorithm for diffusion source localization in large-scale complex networks

Abstract The identification of the origin of diffusion processes in complex networks is a subject of significant interest across numerous interdisciplinary fields. One approach to solving this issue involves the placement of a few observer nodes within the network and the estimation of the unknown source through the utilization of information gathered by these observer nodes. However, this approach presents certain drawbacks, particularly with regard to computational complexity. To address this limitation, this study introduces an innovative Hill-Climbing algorithm designed to efficiently identify diffusion sources within large-scale complex networks. Our approach, the Local Search Hill Climbing (LSHC) method, transforms the source localization problem into an optimization task, utilizing strategically deployed observer nodes. Experiments conducted on both random and scale-free network models demonstrate that our method significantly reduces computational time while maintaining high accuracy in pinpointing the diffusion source. This approach offers a substantial improvement over traditional methods and holds considerable promise for practical applications in network science.

A Network Science Approach to Sex-Polydrug Use Among Black Sexually Minoritized Men and Transgender Women: The N2 Cohort Study.

Black sexually minoritized men (SMM) and transgender women (TW) are subgroups with lower rates of substance use and comparable rates of condom use relative to White SMM and TW yet experience heightened vulnerability to HIV. This study sought to explore associations of substance use, including sex-drug use (i.e., drug or alcohol use during sex to enhance sex), and condomless sex among Black SMM and TW. Data were collected from Black SMM and TW living in Chicago, Illinois, enrolled in the Neighborhoods and Networks (N2) cohort study, from November 2018 to April 2019. We used bivariate analyses followed by a multilevel egocentric network analysis to identify factors associated with condomless sex. We conducted Spearman correlation coefficients to examine correlations between pairs of sex-drugs to enhance sex. We used a bipartite network analysis to identify correlates of sex-drug use and condomless sex. A total of 352 Black SMM and TW (egos) provided information about 933 sexual partners (alters). Of respondents, 45% reported condomless sex and 61% reported sex-drug use. In unadjusted analyses, marijuana (34%) and cocaine/crack (5%) sex-drug use were associated with condomless sex (p < 0.05). Condomless sex was positively associated with sex-polydrug use, or the use of 2+ drugs or 1 drug and alcohol (OR = 1.48; 95% CI: 1.02-2.14; p = 0.039), and negatively associated with sharing an HIV-negative serostatus with a sexual partner (OR = 0.57; 95% CI: 0.33-0.98; p = 0.041), having a different HIV serostatus with a sexual partner (OR = 0.37; 95% CI: 0.21-0.64; p < 0.001) or not knowing the HIV serostatus of a sexual partner (OR = 0.47; 95% CI: 0.26-0.84; p = 0.011). The following pairs of sex-polydrug use had Spearman correlation coefficients higher than 0.3: marijuana and alcohol, ecstasy and alcohol, cocaine/crack and ecstasy, and methamphetamine and poppers (p < 0.05). HIV prevention interventions for Black SMM and TW designed to reduce HIV transmission through egocentric sexual networks could address sex-drug use through sex-positive and pleasure-centered harm reduction strategies and provide and promote biomedical prevention and care options at supraoptimal levels.

Shifting sands: How change-point and community detection can enrich our understanding of international politics

When and how do international political arrangements change? International relations scholars have long examined the nature of shifts in international alliance and cooperation networks, often arguing that times of disruption in the international system are the most precarious for peace. In this research note, we rely on innovations in change-point and community detection methods to endogenously examine the timing and nature of shifts in country-to-country relationships through defense cooperative agreements. Using new methodical innovations from network science, we can see how countries move through different communities over time, changing the nature of polarity in the system. This empirical approach can help provide insights into determinants of peace, vulnerabilities in the international system, and potential aggressors in world politics.

Optimising peak energy reduction in networks of buildings

Buildings are amongst the world’s largest energy consumers and simultaneous peaks in demand from networks of buildings can decrease electricity system stability. Current mitigation measures either entail wasteful supply-side over-specification or complex centralised demand-side control. Hence, a simple schema is developed for decentralised, self-organising building-to-building load coordination that requires very little information exchange and no top-down management—analogous to other complex systems with short range interactions, such as coordination between flocks of birds or synchronisation in fireflies. Numerical and experimental results reveal that a high degree of peak flattening can be achieved using surprisingly small load-coordination networks. The optimum reductions achieved by the simple schema can outperform existing techniques, giving substantial peak-reductions as well as being remarkably robust to changes in other system parameters such as the interaction network topology. This not only demonstrates that significant reductions in network peaks are achievable using remarkably simple control systems but also reveals interesting theoretical results and new insights which will be of great interest to the complexity and network science communities.

Cyborgs for strategic communication on social media

Social media platforms are a key ground of information consumption and dissemination. Key figures like politicians, celebrities, and activists have leveraged on its wide user base for strategic communication. Strategic communications, or StratCom, is the deliberate act of information creation and distribution. Its techniques are used by key figures for establishing brand and amplifying messages. Automated scripts are used on top of personal touches to effectively perform these tasks. The combination of automation and manual online posting creates a Cyborg social media profile, which is a hybrid between bot and human. In this study, we establish a quantitative definition for a Cyborg account, an account that is detected as bot in one time window, and identified as human in another. This definition makes use of frequent changes in bot classification labels and large differences in bot likelihood scores to identify Cyborgs. We perform a large-scale analysis across over 3.1 million users from Twitter collected from two key events, the 2020 Coronavirus pandemic and the 2020 US Elections. We extract Cyborgs from two datasets and employ tools from network science, natural language processing, and manual annotation to characterize Cyborg accounts. Our analyses identify Cyborg accounts are constructed for strategic communication uses, have a strong duality in their bot/human classification and are tactically positioned in the social media network, aiding these accounts to promote their desired content. Cyborgs are also discovered to have long online lives, indicating their ability to evade bot detectors, or the graciousness of platforms to allow their operations.

Network science reveals the early signs of L1 lexical attrition: Introducing the Lexical Attrition Foundation (LeAF) framework

Abstract L1 lexical attrition is the decline of L1 lexical-semantic abilities due to reduced L1 exposure and/or L2 interference. Semantic fluency tasks are central in this research, but traditional analyses are often inconclusive. To address this, we employed an innovative network science approach to investigate the bilingual lexicon's structural properties. Semantic fluency data were collected from immersed/non-immersed late bilinguals with comparable L2 proficiencies. Our results indicate that L2 immersion led to more integrated and efficient L2 networks. Crucially, immersion also resulted in L1 attrition (only evident in the network analysis). Immersed participants exhibited less densely connected and sparser L1 networks. Furthermore, network measures suggest that L1 lexical attrition occurs gradually, initially impacting network interconnectivity, while information flow and community structure remain more stable. Drawing from these insights, we introduce the Lexical Attrition Foundation (LeAF) framework, offering a network-based perspective on lexical attrition development and laying the groundwork for future research.

On K-Banhatti, Revan Indices and Entropy Measures of MgO(111) Nanosheets via Linear Regression

The structure and topology of chemical compounds can be determined using chemical graph theory. Using topological indices, we may uncover much about connectivity, complexity, and other important aspects of molecules. Numerous research investigations have been conducted on the K-Banhatti indices and entropy measurements in various fields, including the study of natural polymers, nanotubes, and catalysts. At the same time, the Shannon entropy of a graph is widely used in network science. It is employed in evaluating several networks, including social networks, neural networks, and transportation systems. The Shannon entropy enables the analysis of a network’s topology and structure, facilitating the identification of significant nodes or structures that substantially impact network operation and stability. In the past decade, there has been a considerable focus on investigating a range of nanostructures, such as nanosheets and nanoparticles, in both experimental and theoretical domains. As a very effective catalyst and inert substrate, the MgO nanostructure has received a lot of interest. The primary objective of this research is to study different indices and employ them to look at entropy measures of magnesium oxide(111) nanosheets over a wide range of p values, including p=1,2,3,…,j. Additionally, we conducted a linear regression analysis to establish the correlation between indices and entropies.