Network Science Perspective Research Articles

Rank aggregation with limited information based on link prediction

Rank aggregation is a vital tool in facilitating decision-making processes that consider multiple criteria or attributes. While in many applications, the available ranked lists are often limited and quite partial for various reasons. This scarcity of ranking information presents a significant challenge to rank aggregation effectiveness. To address this problem of rank aggregation with limited information, in this study, on the basis of networked representation of ranking information, we employ the link prediction technology to mine potential ranking information. It aims to optimize the aggregation process, and maximize the aggregation effectiveness using available limited information. Experimental results indicate that our proposed approach can significantly enhance the aggregation effectiveness of existing rank aggregation methods, such as Borda’s method, competition graph method and Markov chain method. Our work provides a new way to solve rank aggregation problem with limited information and develops a new research paradigm for future rank aggregation studies from the perspective of network science.

Projection transformation-based link prediction for possible collaboration connections in bimodal supply chain networks

Aiming at the problems of information disconnection and business interruption in supply chain networks caused by uncertainties such as trade wars and COVID-19, a projection-transformation-based model for predicting possible collaborative relationship links in bimodal supply chain networks is designed from the perspective of network science. The model first transforms the bimodal network structure of supply chain into a corresponding unimodal projection network by projection transformation; then screens the candidate collaborative links in the network; finally assigns weights to the similarity links according to different influencing factors, and calculates the possibility of potential collaborative links in the bimodal supply chain network by this. The experimental results show that the model can achieve higher quality prediction in real bimodal supply chain networks compared with other baseline link prediction models, which provides theoretical and methodological support for better formulation of business contingency plans and risk defense measures.

Inter-firm patent litigation networks: a study of network motif analysis

Despite the recent complex intertwining of firms in fierce intellectual property disputes, the formation mechanisms of patent infringement litigation relationships between firms have been little studied from a network science perspective. We construct an inter-firm patent litigation network using longitudinal data on patent infringement litigation and the firms involved in the US, and examine its structural characteristics and the mechanisms of tie formation through network motif analysis, linking it to existing knowledge on negative ties. The results reveal the significant influence of mechanisms such as homophily, popularity, and activity on network formation, with reciprocity emerging as a pivotal factor. The absence of triadic closure is also observed. This distinct structural pattern is consistent across different technological fields and throughout the 20-year period. Furthermore, our analysis delves into the rapid countersuit strategies common within this network and provides valuable insights into patent litigation strategies between firms.

Research on the Operation Effect and Optimisation Path of Publishers’ Short Videos in the Context of Media Convergences

Short video has not only reshaped the ecological pattern of media communication, but also profoundly affected the marketing method of publishing houses, and the combination of “publishing + short video” has become the standard of many publishing houses, showing strong market potential and innovative vitality. In this context, from the perspective of network science, we analyse the short video operation initiatives of publishers, focusing on the core aspects of account operation, content operation and user operation. By exploring the effects and strategies of short video operation of publishing houses, it is proposed that by enhancing the density and intensity of connections in the relational network, the social influence and dissemination effect of short video accounts of publishing houses can be effectively enhanced, the integrated development of publishing can be promoted, and new vitality and kinetic energy can be injected into the publishing industry.

Quantifying the Resilience of Coal Energy Supply in China Toward Carbon Neutrality.

Facing the challenge of achieving the goal of carbon neutrality, China is decoupling the currently close dependence of its economy on coal use. The energy supply and demand decarbonization has substantial influence on the resilience of the coal supply. However, a general understanding of the precise impact of energy decarbonization on the resilience of the coal energy supply is still lacking. Here, from the perspective of network science, we propose a theoretical framework to explore the resilience of the coal market of China. We show that the processes of increasing the connectivity and the competition between the coal enterprises, which are widely believed to improve the resilience of the coal market, can undermine the sustainability of the coal supply. Moreover, our results reveal that the policy of closing small-sized coal mines may not only reduce the safety accidents in the coal production but also improve the resilience of the coal market network. Using our model, we also suggest a few practical policies for minimizing the systemic risk of the coal energy supply.

Uncovering the Role of Hubs: A Network Science Perspective on Platform Competition

Uncovering the Role of Hubs: A Network Science Perspective on Platform Competition

Social, lifestyle, and health status characteristics as a proxy for occupational burnout identification: A network approach analysis.

Occupational burnout is a type of psychological syndrome. It can lead to serious mental and physical disorders if not treated in time. However, individuals tend to conceal their genuine feelings of occupational burnout because such disclosures may elicit bias from superiors. This study aims to explore a novel method for estimating occupational burnout by elucidating its links with social, lifestyle, and health status factors. In this study 5,794 participants were included. Associations between occupational burnout and a set of features from a survey was analyzed using Chi-squared test and Wilcoxon rank sum test. Variables that are significantly related to occupational burnout were grouped into four categories: demographic, work-related, health status, and lifestyle. Then, from a network science perspective, we inferred the colleague's social network of all participants based on these variables. In this inferred social network, an exponential random graph model (ERGM) was used to analyze how occupational burnout may affect the edge in the network. For demographic variables, age (p < 0.01) and educational background (p < 0.01) were significantly associated with occupational burnout. For work-related variables, type of position (p < 0.01) was a significant factor as well. For health and chronic diseases variables, self-rated health status, hospitalization history in the last 3 years, arthritis, cardiovascular diseases, high blood lipid, breast diseases, and other chronic diseases were all associated with occupational burnout significantly (p < 0.01). Breakfast frequency, dairy consumption, salt-limiting tool usage, oil-limiting tool usage, vegetable consumption, pedometer (step counter) usage, consuming various types of food (in the previous year), fresh fruit and vegetable consumption (in the previous year), physical exercise participation (in the previous year), limit salt consumption, limit oil consumption, and maintain weight were also significant factors (p < 0.01). Based on the inferred social network among all airport workers, ERGM showed that if two employees were both in the same occupational burnout status, they were more likely to share an edge (p < 0.0001). The major limitation of this work is that the social network for occupational burnout ERGM analysis was inferred based on associated factors, such as demographics, work-related conditions, health and chronic diseases, and behaviors. Though these factors have been proven to be associated with occupational burnout, the results inferred by this social network cannot be warranted for accuracy. This work demonstrated the feasibility of identifying people at risk of occupational burnout through an inferred colleague's social network. Encouraging staff with lower occupational burnout status to communicate with others may reduce the risk of burnout for other staff in the network.

Performance assessment of a communication infrastructure with redundant topology: A complex network approach

The physical assets within a critical infrastructure system are pivotal to its efficient performance and protection and that of other dependent systems. This is particularly the case for communication systems where network protection strategies usually involve asset redundancy. Although such redundancy is well-modelled in the literature, there is a gap in knowledge from a network science perspective in terms of its implications for network modelling and performance assessment. This paper presents a multilayer complex network framework that takes into account the heterogeneity of the redundant infrastructure for realistic network modelling and further analysis, a step change from using a single network model. Key performance indicators (KPIs) for communication networks (i.e., latency and jitter, bandwidth and throughput, queue depth and packet drops) are redefined to evaluate key important features of a long-haul backbone network such as network capacity and average use. In addition, these KPIs are adapted to deal with the aforementioned redundancy and so inform network managers with values defined over a model closer to the real system. The paper analyses the use case of a nationwide core and metro network infrastructure of one of the main UK internet service providers. The results of the analysis of KPIs showcase the advantage of the proposed multilayer complex network framework over the traditional single network model. Critical network elements within different dimensions of a communication network are identified based on their performance for prioritising network management measures.

Networks, agriculture and geography: How business connections of agricultural enterprises shape the connection of settlements in Western Hungary

Networks and network science are not new: since the middle of the 20th century, networked structures are analyzed in geography. In recent years, however, with the emergence of network science, with the availability of big data, with improved computational capabilities and new software, the knowledge and analysis of networks have improved substantially. Present paper uses network science in economical geography: it analyzes the connections between settlements in western Hungary based on the business connections of agribusinesses. For the research, we used a questionnaire asking for purchase and sales connections of the selected agribusinesses and analyzed the results from the perspective of network science. Results show that in an agribusiness network the purchase network is more complex than the selling network and that in spatial networks connected to agribusinesses not large cities, but small towns and villages play a central role.

Assessment of Multivariate Information Transmission in Space-Time-Frequency Domain: A Case Study for EEG Signals.

Multivariate signal (MS) analysis, especially the assessment of its information transmission (for example, from the perspective of network science), is the key to our understanding of various phenomena in biology, physics and economics. Although there is a large amount of literature demonstrating that MS can be decomposed into space-time-frequency domain information, there seems to be no research confirming that multivariate information transmission (MIT) in these three domains can be quantified. Therefore, in this study, we attempted to combine dynamic mode decomposition (DMD) and parallel communication model (PCM) together to realize it. We first regarded MS as a large-scale system and then used DMD to decompose it into specific subsystems with their own intrinsic oscillatory frequencies. At the same time, the transition probability matrix (TPM) of information transmission within and between MS at two consecutive moments in each subsystem can also be calculated. Then, communication parameters (CPs) derived from each TPM were calculated in order to quantify the MIT in the space-time-frequency domain. In this study, multidimensional electroencephalogram (EEG) signals were used to illustrate our method. Compared with traditional EEG brain networks, this method shows greater potential in EEG analysis to distinguish between patients and healthy controls. This study demonstrates the feasibility of measuring MIT in the space-time-frequency domain simultaneously. This study shows that MIT analysis in the space-time-frequency domain is not only completely different from the MS decomposition in these three domains, but also can reveal many new phenomena behind MS that have not yet been discovered.

A Network Science Perspective of Graph Convolutional Networks: A Survey

The mining and exploitation of graph structural information have been the focal points in the study of complex networks. Traditional structural measures in Network Science focus on the analysis and modelling of complex networks from the perspective of network structure, such as the centrality measures, the clustering coefficient, and motifs and graphlets, and they have become basic tools for studying and understanding graphs. In comparison, graph neural networks, especially graph convolutional networks (GCNs), are particularly effective at integrating node features into graph structures via neighbourhood aggregation and message passing, and have been shown to significantly improve the performances in a variety of learning tasks. These two classes of methods are, however, typically treated separately with limited references to each other. In this work, aiming to establish relationships between them, we provide a network science perspective of GCNs. Our novel taxonomy classifies GCNs from three structural information angles, i.e., the layer-wise message aggregation scope, the message content, and the overall learning scope. Moreover, as a prerequisite for reviewing GCNs via a network science perspective, we also summarise traditional structural measures and propose a new taxonomy for them. Finally and most importantly, we draw connections between traditional structural approaches and graph convolutional networks, and discuss potential directions for future research.

Modular structures and the delivery of inpatient care in hospitals: a Network Science perspective on healthcare function and dysfunction

Reinforced by the COVID-19 pandemic, the capacity of health systems to cope with increasing healthcare demands has been an abiding concern of both governments and the public. Health systems are made up from non-identical human and physical components interacting in diverse ways in varying locations. It is challenging to represent the function and dysfunction of such systems in a scientific manner. We describe a Network Science approach to that dilemma. General hospitals with large emergency caseloads are the resource intensive components of health systems. We propose that the care-delivery services in such entities are modular, and that their structure and function can be usefully analysed by contemporary Network Science. We explore that possibility in a study of Australian hospitals during 2019 and 2020. We accessed monthly snapshots of whole of hospital administrative patient level data in two general hospitals during 2019 and 2020. We represented the organisations inpatient services as network graphs and explored their graph structural characteristics using the Louvain algorithm and other methods. We related graph topological features to aspects of observable function and dysfunction in the delivery of care. We constructed a series of whole of institution bipartite hospital graphs with clinical unit and labelled wards as nodes, and patients treated by units in particular wards as edges. Examples of the graphs are provided. Algorithmic identification of community structures confirmed the modular structure of the graphs. Their functional implications were readily identified by domain experts. Topological graph features could be related to functional and dysfunctional issues such as COVID-19 related service changes and levels of hospital congestion. Contemporary Network Science is one of the fastest growing areas of current scientific and technical advance. Network Science confirms the modular nature of healthcare service structures. It holds considerable promise for understanding function and dysfunction in healthcare systems, and for reconceptualising issues such as hospital capacity in new and interesting ways.

Stream gauge clustering and analysis for non-stationary time series through complex networks

Stream gauge clustering enables diverse studies, such as the analysis of spatial patterns and the physical reasons for these patterns. The clustering of monitoring gauges through complex networks combined with community detection algorithms is a strong alternative to classical methods. However, this approach involves several particularities that impact the clustering results, and the non-stationarity commonly present in streamflow series is typically not accounted for in studies that use this methodology. This article presents the application of a new framework developed to cluster stream gauge stations and to analyse changes in the clustering results across time. Weighted networks were created through Mutual Information combined with an automated threshold. Complex networks were obtained for the entire series and for sliding windows to investigate if significant differences occur across time. A more in-depth analysis was carried out with selected time windows through the perspective of Network Science and Complex Network Analysis, and the results were compared to those from a classic clustering approach. The framework provided robust and physically coherent clustering results and a more detailed clustering result than the classic approach. The weighted networks construction procedure successfully diminished time delimited effects induced by non-stationarity. However, the sliding windows networks results demonstrated that significant changes occurred across time, and three different community configurations were obtained. These results indicate that the use a single network can result in a misrepresentation of local characteristic and lead to wrong conclusions. The communities’ evolution across time showed spatial-time coherence with both the physical phenomenology of the study area and previous studies. The changes observed were associated with phase shifts of low-frequency sea surface temperature oscillations of both the Atlantic and the Pacific Oceans through the phase shifts’ direct and indirect influence on the South Atlantic Convergence Zone. The in-depth analysis resulted in the identification of a spatially coherent transition zone within the clusters obtained and attested the reliability of the framework results based on network typologies. Thus, the proposed framework can aid in clustering problems and provide better comprehension of local characteristics. Although the framework was developed for stream gauge clustering, its use can be extended to the clustering of any data with nonlinear and nonstationary characteristics.

Exploiting node metadata to predict interactions in bipartite networks using graph embedding and neural networks.

Networks are increasingly used in various fields to represent systems with the aim of understanding the underlying rules governing observed interactions, and hence predict how the system is likely to behave in the future. Recent developments in network science highlight that accounting for node metadata improves both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, to predict interactions in a network within existing statistical and machine learning frameworks, we need to learn objects that rapidly grow in dimension with the number of nodes. Thus, the task becomes computationally and conceptually challenging for networks. Here, we present a new predictive procedure combining a statistical, low-rank graph embedding method with machine learning techniques which reduces substantially the complexity of the learning task and allows us to efficiently predict interactions from node metadata in bipartite networks. To illustrate its application on real-world data, we apply it to a large dataset of tourist visits across a country. We found that our procedure accurately reconstructs existing interactions and predicts new interactions in the network. Overall, both from a network science and data science perspective, our work offers a flexible and generalizable procedure for link prediction.

Different networks for different purposes: A network science perspective on collaboration and communication in an engineering design project

During the design and implementation of engineering systems, as project members solve problems and exchange information with each other, a complex network of organisational interactions is established. The study of organisational networks can provide us with a deeper understanding of how organisations function and can help us devise strategies to improve collaboration and communication. However, it is widely believed that communication networks accurately capture the underlying collaboration amongst the members of an organisation. Here, through the use of multilayer and temporal network analysis methods, we analyse collaboration (through the project activity log) and communication (via emails) in a large engineering project and find that, on the contrary, collaboration and communication networks are only weakly correlated. More importantly, collaboration and communication networks show diametrically opposite purposes, underscored by crucial topological differences. While the email communication network is organised to facilitate the spread of information, the collaboration network is organised to suppress error propagation. We discuss the implications of our findings on how to design organisational networks and how to foster effective communication.

Organizational Geosocial Network: A Graph Machine Learning Approach Integrating Geographic and Public Policy Information for Studying the Development of Social Organizations in China

This study aims to give an insight into the development trends and patterns of social organizations (SOs) in China from the perspective of network science integrating geography and public policy information embedded in the network structure. Firstly, we constructed a first-of-its-kind database which encompasses almost all social organizations established in China throughout the past decade. Secondly, we proposed four basic structures to represent the homogeneous and heterogeneous networks between social organizations and related social entities, such as government administrations and community members. Then, we pioneered the application of graph models to the field of organizations and embedded the Organizational Geosocial Network (OGN) into a low-dimensional representation of the social entities and relations while preserving their semantic meaning. Finally, we applied advanced graph deep learning methods, such as graph attention networks (GAT) and graph convolutional networks (GCN), to perform exploratory classification tasks by training models with county-level OGNs dataset and make predictions of which geographic region the county-level OGN belongs to. The experiment proves that different regions possess a variety of development patterns and economic structures where local social organizations are embedded, thus forming differential OGN structures, which can be sensed by graph machine learning algorithms and make relatively accurate predictions. To the best of our knowledge, this is the first application of graph deep learning to the construction and representation learning of geosocial network models of social organizations, which has certain reference significance for research in related fields.

Three Decades in Econophysics-From Microscopic Modelling to Macroscopic Complexity and Back.

We explore recent contributions to research in Econophysics, switching between Macroscopic complexity and microscopic modelling, showing how each leads to the other and detailing the everyday applicability of both approaches and the tools they help develop. Over the past decades, the world underwent several major crises, leading to significant increase in interdependence and, thus, complexity. We show here that from the perspective of network science, these processes become more understandable and, to some extent, also controllable.

Extracting Network Patterns of Tourist Flows in an Urban Agglomeration Through Digital Footprints: The Case of Greater Bay Area

Network patterns of tourist flows can reveal differences in tourism resources among destinations from the perspective of network science, providing valuable suggestions for tourism managers and policymakers to promote the balanced and sustainable development of tourism. This paper focuses on urban agglomerations, a highly developed spatial form of integrated cities, and proposes a research framework to extract the network patterns of tourist flows through digital footprints. Based on an illustrative case study using geo-located travel blog data from Qunar.com, we built a tourist flow network for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China. The analysis shows: (1) GBA’s tourist flow network is obviously heterogeneous, showing a pattern of “four cores and three poles”; (2) the strong “administrative barrier effect,” revealed by community detection within the network, is the main obstacle to integrating regional tourism; (3) strengthening the infrastructure of tourism mediation cities such as Guangzhou, Zhuhai, and Shenzhen, so as to avoid the “structural hole” caused by the tourist flows between cities, is an urgent issue that the GBA government needs to address. To summarize, the research framework can provide a theoretical basis and concrete suggestions for the planning and management of the tourism industry in urban agglomerations.

Modularity in Biological Networks.

Network modeling, from the ecological to the molecular scale has become an essential tool for studying the structure, dynamics and complex behavior of living systems. Graph representations of the relationships between biological components open up a wide variety of methods for discovering the mechanistic and functional properties of biological systems. Many biological networks are organized into a modular structure, so methods to discover such modules are essential if we are to understand the biological system as a whole. However, most of the methods used in biology to this end, have a limited applicability, as they are very specific to the system they were developed for. Conversely, from the statistical physics and network science perspective, graph modularity has been theoretically studied and several methods of a very general nature have been developed. It is our perspective that in particular for the modularity detection problem, biology and theoretical physics/network science are less connected than they should. The central goal of this review is to provide the necessary background and present the most applicable and pertinent methods for community detection in a way that motivates their further usage in biological research.

Conspiracy of Corporate Networks in Corruption Scandals

Corruption in public procurement transforms state institutions into private entities where public resources get diverted for the benefit of a few. On this matter, much of the discussion centers on the legal fulfillment of the procurement process, while there are fewer formal analyses related to the corporate features which are most likely to signal organized crime and corruption. The lack of systematic evidence on this subject has the potential to bias our understanding of corruption, making it overly focused on the public sector. Nevertheless, corruption scandals worldwide tell of the importance of taking a better look at the misuse and abuse of corporations for corrupt purposes. In this context, the research presented here seeks to contribute to the understanding of the criminal conspiracy of companies involved in public procurement corruption scandals under a network and complexity science perspective. To that end, we make use of a unique dataset of the corporate ownership and management information of four important and recently documented cases of corruption in Mexico, where hundreds of companies were used to embezzle billions of dollars. Under a bipartite network approach, we explore the relations between companies and their personnel (shareholders, legal representatives, administrators, and commissioners) in order to characterize their static and dynamic networked structure. In terms of organized crime and using different network properties, we describe how these companies connect with each other due to the existence of shared personnel with role multiplicity, leading to very different conspiracy networks. To best quantify this behavior, we introduce a heuristic network-based conspiracy indicator that together with other network metrics describes the differences and similarities among the networks associated with each corruption case. Finally, we discuss some public policy elements that might be needed to be considered in anti-corruption efforts related to corporate organized crime.