Application Of Network Science Research Articles

Modeling and analysis of cascading failures in multilayer higher-order networks

With the increasing application of network science, understanding the behavior of higher-order networks has become particularly important. Especially, the study of multilayer higher-order networks is significant for revealing the interdependence and higher-order interactions in complex systems. This paper proposes a mathematical framework to explore multilayer higher-order networks composed of multiple fully interdependent hypergraphs. Each hypergraph consists of the same number of nodes, and nodes between layers are connected one-to-one. By analyzing the cascading failures of multilayer hypergraphs under different topologies, we found that although the network sizes are the same in the steady state, different topological structures (such as star-like, tree-like, and chain-like) significantly affect the time required to reach a stable state, with the star-like topology reaching stability the fastest. Furthermore, we explored the impact of network parameters on the robustness of networks. We found that in multilayer homogeneous hypergraphs, the robustness of the network becomes stronger with an increase in the average hyperdegree or average hyperedge cardinality; in multilayer heterogeneous hypergraphs, the robustness of the network becomes more fragile as the power exponent increases. Finally, experimental results indicate that with the rise in the number of network layers, the network becomes more fragile.

Robustness of Real-World Networks after Weight Thresholding with Strong Link Removal

Weight thresholding (WT) is a method intended to decrease the number of links within weighted networks that may otherwise be excessively dense for network science applications. WT aims to remove links to simplify the network by holding most of the features of the original network. Here, we test the robustness and the efficacy of the node attack strategies on real-world networks subjected to WT that remove links of higher weight (strong links). We measure the network robustness along node removal with the largest connected component (LCC). We find that the real-world networks under study are generally robust when subjected to WT. Nonetheless, WT with strong link removal changes the efficacy of the attack strategies and the rank of node centralities. Also, WT with strong link removal may trigger a more significant change in the node centrality rank than WT by removing weak links. Network science research with the aim to find important/influential nodes in the network has to consider that simplifying the network with WT methodologies may change the node centrality.

Catalyzing communities of research rigour champions.

The biomedical sciences must maintain and enhance a research culture that prioritizes rigour and transparency. The US National Institute of Neurological Disorders and Stroke convened a workshop entitled 'Catalyzing Communities of Research Rigor Champions' that brought together a diverse group of leaders in promoting research rigour and transparency (identified as 'rigour champions') to discuss strategies, barriers and resources for catalyzing technical, cultural and educational changes in the biomedical sciences. This article summarizes 2 days of panels and discussions and provides an overview of critical barriers to research rigour, perspectives behind reform initiatives and considerations for stakeholders across science. Additionally, we describe applications of network science to foster, maintain and expand cultural changes related to scientific rigour and opportunities to embed rigourous practices into didactic courses, training experiences and degree programme requirements. We hope this piece provides a primer for the wider research community on current discussions and actions and inspires individuals to build, join or expand collaborative networks within their own institutions that prioritize rigourous research practices.

Cognitive Network Science Reveals Bias in GPT-3, GPT-3.5 Turbo, and GPT-4 Mirroring Math Anxiety in High-School Students

Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. In this study, we introduce a novel application of network science and cognitive psychology to understand biases towards math and STEM fields in LLMs from ChatGPT, such as GPT-3, GPT-3.5, and GPT-4. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have negative perceptions of math and STEM fields, associating math with negative concepts in 6 cases out of 10. We observe significant differences across OpenAI’s models: newer versions (i.e., GPT-4) produce 5× semantically richer, more emotionally polarized perceptions with fewer negative associations compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.

Identifying the phonological backbone in the mental lexicon.

Previous studies used techniques from network science to identify individual nodes and a set of nodes that were "important" in a network of phonological word-forms from English. In the present study we used a network simplification process-known as the backbone-that removed redundant edges to extract a subnetwork of "important" words from the network of phonological word-forms. The backbone procedure removed 68.5% of the edges in the original network to extract a backbone with a giant component containing 6,211 words. We compared psycholinguistic and network measures of the words in the backbone to the words that did not survive the backbone extraction procedure. Words in the backbone occurred more frequently in the language, were shorter in length, were similar to more phonological neighbors, and were closer to other words than words that did not survive the backbone extraction procedure. Words in the backbone of the phonological network might form a "kernel lexicon"-a small but essential set of words that allows one to communicate in a wide-range of situations-and may provide guidance to clinicians and researchers on which words to focus on to facilitate typical development, or to accelerate rehabilitation efforts. The backbone extraction method may also prove useful in other applications of network science to the speech, language, hearing and cognitive sciences.

Amphora Traceability in the Roman West: Recognition of Patterns of Commercial Connectivity in the Roman Empire through the Application of Network Science to Amphoric Epigraphy

Amphora Traceability in the Roman West: Recognition of Patterns of Commercial Connectivity in the Roman Empire through the Application of Network Science to Amphoric Epigraphy

Deciphering the Basis of Molecular Biology of Selected Cardiovascular Diseases: A View on Network Medicine.

Cardiovascular diseases are the leading cause of death across the world. For decades, researchers have been studying the causes of cardiovascular disease, yet many of them remain undiscovered or poorly understood. Network medicine is a recently expanding, integrative field that attempts to elucidate this issue by conceiving of disease as the result of disruptive links between multiple interconnected biological components. Still in its nascent stages, this revolutionary application of network science facilitated a number of important discoveries in complex disease mechanisms. As methodologies become more advanced, network medicine harbors the potential to expound on the molecular and genetic complexities of disease to differentiate how these intricacies govern disease manifestations, prognosis, and therapy. This is of paramount importance for confronting the incredible challenges of current and future cardiovascular disease research. In this review, we summarize the principal molecular and genetic mechanisms of common cardiac pathophysiologies as well as discuss the existing knowledge on therapeutic strategies to prevent, halt, or reverse these pathologies.

A Distributed System-Based Multiplex Networks to Extract Texture Feature

Defect detection is an indispensable part of quality detection in manufacturing. It is a challenging task to recognize defects on the surface of castings with random textures. This paper proposes a texture extraction method based on multiplex networks for defect segmentation in a random background. The proposed method redefines the image information in the form of multiplex network topologies according to the different properties of casting surface texture. Finally, the proposed method segments different texture regions by extracting the similarity of texture primitives in the multiplex networks. The study conducted experiments in a distributed system environment, and the results show that the proposed method is effective in actual industrial data sets. As an interdisciplinary application of network science and machine vision, the proposed method provides a valuable application mode for the development of complex networks in new fields and provides a new research idea for the texture analysis of castings.

Rethinking Healthcare Teams’ Practices Using Network Science: Implications, Challenges, and Benefits

Healthcare teams act in a very complex environment and present extremely peculiar features since they are multidisciplinary, work under quickly changing conditions, and often stay together for a short period with a dynamically fluctuating team membership. Thus, in the broad discussions about the future of healthcare, the strategy for improving providers’ collaboration and team dynamics is becoming a central topic. Within this context, this paper aims to discuss different viewpoints about the application of network science to teamworking. Our results highlight the potential benefits deriving from network science-enabled analysis, and also show some preliminary empirical evidence through a real case study. In so doing, we intend to stimulate discussions regarding the implications of network science in the investigation and improvement of healthcare teams. The intention is to pave the way for future research in this context by suggesting the potential advantages of healthcare teamwork analysis, as well as recognising its challenges and threats.

Analysis of travel mobility under Covid-19: application of network science

ABSTRACT COVID-19 is substantially reshaping the tourism and hospitality industries but studies on the changes in travel behaviour in response to the pandemic are limited. Using tourism big data, this research applies network science analytics to determine behavioural changes in travel mobility of domestic travellers who visited Jeju Island, Korea, from June 2019 to December 2020. The findings reveal significant reductions in the number of trips to a destination but also limited spatial connectivity and diversity in travel flow during the pandemic. A higher intensity of travel mobility to outdoor and coastal areas and shorter travel distances are evident during COVID-19.

Network Analysis of Time Series: Novel Approaches to Network Neuroscience.

In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as “network neuroscience.” In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, “intrinsic manifold” from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.

An Unbiased Symmetric Matrix Estimator for Topology Inference Under Partial Observability

Network topology inference is a fundamental problem in many applications of network science, such as locating the source of fake news, brain connectivity networks detection, etc. Many real-world situations suffer from a critical problem that only a limited part of observed nodes are available. This letter considers the problem of network topology inference under the framework of partial observability. Based on the vector autoregressive model, we propose a novel unbiased estimator for the symmetric network topology with the Gaussian noise and the Laplacian combination rule. Theoretically, we prove that it converges to the network combination matrix in probability. Furthermore, by utilizing the Gaussian mixture model algorithm, an effective algorithm called network inference Gauss algorithm is developed to infer the network structure. Finally, compared with the state-of-the-art methods, numerical experiments demonstrate the proposed algorithm enjoys better performance in the case of small sample sizes.

Emerging Bioinformatics Methods and Resources in Drug Toxicology.

Assessing the drug safety at an early stage of a drug discovery program is a critical issue. With the recent advances in molecular biology and genomic, massive amounts of generated and accumulated data by advanced experimental technologies such as RNA sequencing or proteomics start to be at the disposal of the scientific community. Innovative and adequate bioinformatic methods, tools, and protocols are required to analyze properly these diverse and extensive data sources with the aim to identify key features that are related to toxicity observations. Furthermore, the assessment of drug safety can be performed across multiple scales of complexity from molecular, cellular to phenotypic levels; therefore, the application of network science contributes to a better interpretation of the drug's exposure effect on human health. Here, we review databases containing toxicogenomics and chemical-phenotype information, as well as appropriated bioinformatics approaches that are currently used to analyze such data. Extension to others methods such as dose-responses, time-dependent processes, and text mining is also presented giving an overview of suitable tools available for a best practice of drug safety analysis.

Delta-Screening: A Fast and Efficient Technique to Update Communities in Dynamic Graphs

Detecting communities in time-evolving/dynamic networks is an important operation used in many real-world network science applications. While there have been several proposed strategies for dynamic community detection, such approaches do not necessarily take advantage of the locality of changes. In this paper, we present a new technique called Delta-Screening (or simply, Δ-screening) for updating communities in a dynamic graph. The technique assumes that the graph is given as a series of time steps, and outputs a set of communities for each time step. At the start of each time step, the Δ-screening technique examines all changes (edge additions and deletions) and computes a subset of vertices that are likely to be impacted by the change (using the modularity objective). Subsequently, only the identified subsets are processed for community state updates. Our experiments demonstrate that this scheme, despite its ability to prune vertices aggressively, is able to generate significant savings in runtime performance (up to 38× speedup over static baseline and 5 × over dynamic baseline implementations), without compromising on the quality. We test on both real-world and synthetic network inputs containing both edge additions and deletions. The Δ-screening technique is generic to be incorporated into any of the existing modularity-optimizing clustering algorithms. We tested using two state-of-the-art clustering implementations, namely, Louvain and SLM. In addition, we also show how to use the Δ-screening approach to delineate appropriate intervals of temporal resolutions at which to analyze a given input network.

A method for assessing resilience of high-speed EMUs considering a network-based system topology and performance data

The high complexity of the system topology and the uncertainty of threats necessitate the consideration of the resilience of high-speed electric multiple units (EMUs). This paper first gives a definition of high-speed EMU resilience. Then, the structure of a high-speed EMU is described in the form of a network to enable the application of network science for resilience evaluation based on corresponding mathematical expressions. Afterward, a measure of system performance (MSP) is constructed that considers the influence of the high-speed EMU topology and performance data. According to the definition of resilience of a high-speed EMU, a system resilience measurement (SRM) is proposed that comprehensively considers the degree and time of the change in system performance. The validity of our method is then illustrated by applying it to the system topology and performance data associated with the Chinese standard electric multiple units (CEMUs) that serve on high-speed railways in China. Experiments are reported to illustrate the concept of resilience and the procedure for its measurement and to present comparisons with an existing resilience index. Our results indicate that the SRM proposed here can capture the sensitivity of the response of high-speed EMUs to the disturbances, thereby supporting the optimal design and risk management.

Exploring the Protein Interactome Related to Hepatitis C Virus

Hepatitis C virus (HCV) stands as a health problem experienced across the globe leading to chronic or acute liver diseases such as cirrhosis, hepatocellular carcinoma and various others. It is a complex disease with extensive genetic heterogeneity with little known about the interactions of complex intra- and intercellular processes. The evolving tools in the application of network science to identify diseases have paved a way for the study of complex diseases at system level. This study focuses on identifying the significant proteins and the biological regulatory pathways involved in Hepatitis C virus and performing topological analysis of the PPIs derived by the proteins encoded by the susceptible genes in order to look for the molecular connectivity between these pathways.icant proteins and the biological regulatory pathways involved in Hepatitis C virus and performing topological analysis of the PPIs derived by the proteins encoded by the susceptible genes in order to look for the molecular connectivity between these pathways.

A humán agyi aktivitás hálózatelemzési modellezése – Humán agyi hálózatok

Összefoglalás. A hálózatkutatás idegtudományi alkalmazása áttörő eredményt hozott a humán kogníció és a neurális rendszerek közötti kapcsolat megértésében. Jelen tanulmány célja a neurális hálózatok néhány kutatási területét mutatja be a laborunkban végzett vizsgálatok eredményein keresztül. Bemutatjuk az agyi aktivitás mérésének és az agyi területek közötti kommunikációs hálózatok modellezésének technikáját. Majd kiemelünk két kutatási terület: 1) az agyi hálózatok életkori változásainak vizsgálatát, ami választ ad arra, hogy hogyan öregszik az emberi agy; 2) az emberi agyak közötti hálózat modelljének vizsgálatát, amely a hatékony emberi kommunikáció idegrendszeri mechanizmusait próbálja feltárni. Tárgyaljuk a humán kommunikációra képes mesterséges intelligencia fejlesztésének lehetőségét is. Végül kitérünk az agyi hálózatok kutatásának biztonságpolitikai vonatkozásaira. Summary. The human brain consists of 100 billion neurons connected by about 100 trillion synapses, which are hierarchically organized in different scales in anatomical space and time. Thus, it sounds reasonable to assume that the brain is the most complex network known to man. Network science applications in neuroscience are aimed to understand how human feeling, thought and behavior could emerge from this biological system of the brain. The present review focuses on the recent results and the future of network neuroscience. The following topics will be discussed: Modeling the network of communication among brain areas. Neural activity can be recorded with high temporal precision using electroencephalography (EEG). Communication strength between brain regions then might be estimated by calculating mathematical synchronization indices between source localized EEG time series. Finally, graph theoretical models can describe the relationship between system elements (i.e. efficiency of communication or centrality of an element). How does the brain age? While for a newborn the high plasticity of the brain provides the foundation of cognitive development, cognition declines with advanced age due to so far largely unknown neural mechanisms. In one of our studies, we demonstrated that there is a correlation between the anatomical development of the brain (at prenatal age) and its network topology. Specifically, the more developed the baby’s brain, the more functionally specialized/modular it was. In another study we found that in older adults, when compared to young adults, connectivity within modules of their brain network is decreased, with an associated decline in their short-term memory capacity. Moreover, Mild Cognitive Impairment patients (early stage of Alzheimer) were characterized with a significantly lower level of connectivity between their brain modules than the healthy elderly. Human communication via shared network of brain activity. In another study we recorded the brain activity of a speaker and multiple listeners. We investigated the brain network similarity across listeners and between the speaker and listeners. We found that brain activity was significantly correlated among listeners, providing evidence for the fact that the same content is processed via similar neural computations within different brains. The data also suggested that the more the brain activity synchronizes the more the mental state of the individuals overlap. We also found significantly synchronized brain activity between speaker and listeners. Specifically 1) listeners’ brain activity within the speech processing cortices was synchronized to speaker’s brain activity with a time lag, indicating that listeners’ speech comprehension processes replicated the speaker’s speech production processes; and 2) listeners’ frontal cortical activity was synchronized to speaker’s later brain activity, that is, listeners preceded the speaker, indicating that speech content is predicted by the listeners based on the context. Future challenges. Future research could target artificial intelligence development that is capable of human-like communication. To achieve this, the simultaneous recording of brain activity from listener and speaker is needed together with efficiency of the communication. These data could be then modelled via AI to detect biomarkers of communication efficiency. In general, neurotechnology has been rapidly developing within and outside of research and in clinical fields thus it is time for re-conceptualizing the corresponding human right law in order to avoid unwanted consequences of technological applications.

Understanding RT’s Audiences: Exposure Not Endorsement for Twitter Followers of Russian State-Sponsored Media

The Russian state-funded international broadcaster RT (formerly Russia Today) has attracted much attention as a purveyor of Russian propaganda. To date, most studies of RT have focused on its broadcast, website, and social media content, with little research on its audiences. Through a data-driven application of network science and other computational methods, we address this gap to provide insight into the demographics and interests of RT’s Twitter followers, as well as how they engage with RT. Building upon recent studies of Russian state-sponsored media, we report three main results. First, we find that most of RT’s Twitter followers only very rarely engage with its content and tend to be exposed to RT’s content alongside other mainstream news channels. This indicates that RT is not a central part of their online news media environment. Second, using probabilistic computational methods, we show that followers of RT are slightly more likely to be older and male than average Twitter users, and they are far more likely to be bots. Third, we identify thirty-five distinct audience segments, which vary in terms of their nationality, languages, and interests. This audience segmentation reveals the considerable heterogeneity of RT’s Twitter followers. Accordingly, we conclude that generalizations about RT’s audience based on analyses of RT’s media content, or on vocal minorities among its wider audiences, are unhelpful and limit our understanding of RT and its appeal to international audiences.

Network Topology Inference From Heterogeneous Incomplete Graph Signals

Inferring network topologies from observed graph-structured data (also known as graph signals) is a crucial task in many applications of network science. Existing papers on network topology inference typically assume that the observations at all nodes are available. However, there are many situations where only partial observations can be collected due to application-specific constraints. To handle the missing data problem, we propose a framework that relies on heterogeneous incomplete data from a collection of related networks to identify multiple network topologies simultaneously. This work advocates the Gaussian graphical model (GGM) and casts the topology inference problem in terms of estimating the precision matrix that has a form of graph Laplacian. Firstly, an unbiased estimator for the covariance matrix of incomplete data is established and then algorithms based on the alternating direction method of multipliers (ADMM) are developed to jointly estimate graph topologies, rather than estimate each graph topology separately. So that we can borrow information across the multiple related networks to eliminate the impact of missing data. Moreover, non-asymptotic statistical analysis is provided, which proves the consistency of the graph estimator and enables us to investigate the effect of several key factors on the graph estimation error bound. Furthermore, based on the consistent graph estimator, an adaptive algorithm that utilizes the reweighting scheme is proposed to improve the estimation accuracy of the graph-edge structure. Finally, we evaluate our method on both real and synthetic datasets, and the experimental results demonstrate the advantage of our method in comparison with benchmarking algorithms.

Robustness and rich clubs in collaborative learning groups: a learning analytics study using network science

Productive and effective collaborative learning is rarely a spontaneous phenomenon but rather the result of meeting a set of conditions, orchestrating and scaffolding productive interactions. Several studies have demonstrated that conflicts can have detrimental effects on student collaboration. Through the application of network science, and social network analysis in particular, this learning analytics study investigates the concept of group robustness; that is, the capacity of collaborative groups to remain functional despite the withdrawal or absence of group members, and its relation to group performance in the frame of collaborative learning. Data on all student and teacher interactions were collected from two phases of a course in medical education that employed an online learning environment. Visual and mathematical analysis were conducted, simulating the removal of actors and its effect on the group’s robustness and network structure. In addition, the extracted network parameters were used as features in machine learning algorithms to predict student performance. The study contributes findings that demonstrate the use of network science to shed light on essential elements of collaborative learning and demonstrates how the concept and measures of group robustness can increase understanding of the conditions of productive collaborative learning. It also contributes to understanding how certain interaction patterns can help to promote the sustainability or robustness of groups, while other interaction patterns can make the group more vulnerable to withdrawal and dysfunction. The finding also indicate that teachers can be a driving factor behind the formation of rich clubs of well-connected few and less connected many in some cases and can contribute to a more collaborative and sustainable process where every student is included.