Network Science Approach Research Articles

An investigation on the network topology of an evolving product family structure and its robustness and complexity

Products continuously change over time through product innovations satisfying new customer needs and technologies. The successive emergence of new products within a product family can be considered as product family evolution that necessarily involves changes in a product family design structure. Although product family design has been widely discussed in the extant literature, inherent evolving properties in a product family design structure have not been sufficiently explored in an analytical manner. To tackle this issue, this research aims to characterize underlying properties in an evolving product family structure based on a network science approach. First, a product family structure is represented as a network to describe relationships among the components of a product family structure. Then, topological properties and patterns in a product family structure network at each time period are investigated through a case study using the smartphone models of a major company. The results show that each product family structure network follows the topological properties observed in other real networks; the product family structure network evolves with both scale-free and small-world properties and with common and specific motifs during each specific time duration. Also, findings from this study suggest that a design structure with a scale-free network topology with commonality can have topological robustness due to a decrease in structural complexity.

Construction of Parkinson's disease marker-based weighted protein-protein interaction network for prioritization of co-expressed genes

Background: Parkinson's disease (PD) is a complex neurodegenerative movement disorder that primarily results due to the loss of dopaminergic neurons in the substantia nigra region. Studying gene expression in the substantia nigra region would potentially unravel disease-relevant protein-protein interactions. MethodsIn this study we have used network science approach to prioritize candidate genes by focussing on differentially expressed genes (DEGs) that interact with established PD associated-genes (PDAG). Prioritizing genes that interact with already established PDAG would reduce the probability of spurious protein-protein associations. The dataset GSE54282 with Parkinson's disease affected substantia nigra samples was extracted from Gene Expression Omnibus (GEO) database. Protein-Protein Interaction Network (PPIN) was constructed by retrieving all possible interactions between DEGs from high-throughput experiments and literature data using Bisogenet. This complex PPIN was decomposed to construct a subnetwork of Parkinson's Disease-Protein Interaction Map (PD-PIM) by including PDAG and following well-established concepts of network biology such as degree and betweenness centrality. We then implemented a “two-way analysis” where we selected genes belonging to PDPIM subnetwork with their primary interacting partners and highly coexpressed genes on the basis of Pearson score. ResultsA complex PPIN comprised of 5340 nodes (genes) and 39,199 edges (interactions) was obtained. A list of 205 genes (123 PDAGs, 69 hub genes and 13 bottleneck genes) with their respective first level interacting partners were extracted from PPIN interactome to build a PD-specific subnetwork, PD-PIM. This subnetwork PD-PIM comprised of 5078 nodes and 38,357 edges. We then employed a “two-way” gene prioritization method that delineated 267 genes of which 16 genes were found to intersect in the two networks of the “two-way analysis”. Of the 16 genes, we narrowed down to 7 novel candidate genes (VCAM1, BACH1, CALM3, EGR1, IKBKE, MYC and YWHAG) displaying significant changes in their network interactions between control and disease samples. Interestingly, these genes were associated with neuroinflammation signaling pathway, MAPK signaling apoptosis pathway, movement disorders and development of neurons that are linked with development of PD. ConclusionWe propose that VCAM1, BACH1, CALM3, EGR1, IKBKE, MYC and YWHAG genes might play important roles in PD pathogenesis, as well as facilitate the development of effective targeted therapies. Our integrative and network based approach for finding therapeutic targets in genomic data could accelerate the identification of novel drug targets for Parkinson's disease.

Evaluating transit network resilience through graph theory and demand-elastic measures: Case study of the Toronto transit system

The reliability of public transit networks is of critical importance the world over. As many transit systems are increasingly exposed to various causes of service disruptions, there exists a need to quantitatively measure the operational resilience of a transit network. This paper presents an approach for transit resilience measurement that combines several metrics from the existing literature. As a case study, the paper examines and quantifies the resilience of the public transit network in Toronto, Canada to operational disruptions. The approach adopted in this work is a combination of quantitative methods founded in Graph Theory, where the public transit network is represented as a directional graph, and demand-elastic methods using transportation network simulation models to complement the network science approaches. The research findings revealed the critical stations in Toronto's subway network, which if disrupted, would create major negative impacts on passenger trip times. The reasons for their inherent critical nature are discussed and analyzed. This work was also able to spatially quantify transit resilience by identifying low-risk and at-risk areas within Toronto. Although the results are specific to Toronto, making it the first study to analyze transit resilience elaborately in this city, the techniques employed can be applied to any sufficiently detailed transit network.

Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics

Network science provides a set of quantitative methods to investigate complex systems, including human cognition. Although cognitive theories in different domains are strongly based on a network perspective, the application of network science methodologies to quantitatively study cognition has so far been limited in scope. This review demonstrates how network science approaches have been applied to the study of human cognition and how network science can uniquely address and provide novel insight on important questions related to the complexity of cognitive systems and the processes that occur within those systems. Drawing on the literature in cognitive network science, with a focus on semantic and lexical networks, we argue three key points. (i) Network science provides a powerful quantitative approach to represent cognitive systems. (ii) The network science approach enables cognitive scientists to achieve a deeper understanding of human cognition by capturing how the structure, i.e., the underlying network, and processes operating on a network structure interact to produce behavioral phenomena. (iii) Network science provides a quantitative framework to model the dynamics of cognitive systems, operationalized as structural changes in cognitive systems on different timescales and resolutions. Finally, we highlight key milestones that the field of cognitive network science needs to achieve as it matures in order to provide continued insights into the nature of cognitive structures and processes.

Measuring regenerative economics: 10 principles and measures undergirding systemic economic health

Applying network science concepts and methods to economic systems is not a new idea. In the last few decades, however, advances in non-equilibrium thermodynamics (i.e., self-organizing, open, dissipative, far-from-equilibrium systems), and nonlinear dynamics, network science, information theory, and other mathematical approaches to complex systems have produced a new set of concepts and methods, which are powerful for understanding and predicting behavior in socio-economic systems. In several previous papers, for example, we used research from the new Energy Network Science (ENS) to show how and why systemic ecological and economic health requires a balance of efficiency and resilience be maintained within a particular a “window of vitality”. The current paper outlines the logic behind 10 principles of systemic, socio-economic health and the quantitative measures that go with them. Our particular focus is on “regenerative aspects”, i.e., the self-feeding, self-renewal, and adaptive learning processes that natural systems use to nourish their capacity to thrive for long periods of time. In socio-economic systems, we demonstrate how regenerative economics requires regular investment in human, social, natural, and physical capital. Taken as a whole, we propose these 10 metrics represent a new capacity to understand, and set better policy for solving, the entangled systemic suite of social, environmental, and economic problems now faced in industrial cultures.

A spectral analysis and network science approach to identify influential diseases based on disease-gene associations

We model a disease-disease network as an undirected graph of diseases (vertices) wherein two vertices are connected with an edge if the corresponding diseases have one or more common associated genes (the number of common genes is the weight of the edge). In such a weighted graph, a disease with a larger number of common genes with several other diseases is more likely to incur a higher eigenvector centrality (EVC). Our hypothesis is that a person with a higher EVC disease is more likely to acquire other related diseases compared to a person with a lower EVC disease. We tested our hypothesis on the disease-disease network constructed from the results of the disease-gene association studies reported in the NIH GWAS catalogue and OMIM database. The disease EVC values exhibited a Pareto distribution (80-20 rule): around 18 % of the diseases have larger and significantly different EVC values and the remaining 82% of the diseases had lower and similar EVC values. This implies that around 18% of the diseases in humans are more likely to have a larger likelihood of leading to other diseases.

A spectral analysis and network science approach to identify influential diseases based on disease-gene associations

We model a disease-disease network as an undirected graph of diseases (vertices) wherein two vertices are connected with an edge if the corresponding diseases have one or more common associated genes (the number of common genes is the weight of the edge). In such a weighted graph, a disease with a larger number of common genes with several other diseases is more likely to incur a higher eigenvector centrality (EVC). Our hypothesis is that a person with a higher EVC disease is more likely to acquire other related diseases compared to a person with a lower EVC disease. We tested our hypothesis on the disease-disease network constructed from the results of the disease-gene association studies reported in the NIH GWAS catalogue and OMIM database. The disease EVC values exhibited a Pareto distribution (80-20 rule): around 18 % of the diseases have larger and significantly different EVC values and the remaining 82% of the diseases had lower and similar EVC values. This implies that around 18% of the diseases in humans are more likely to have a larger likelihood of leading to other diseases.

Cross-project code clones in GitHub

Code reuse has well-known benefits on code quality, coding efficiency, and maintenance. Open Source Software (OSS) programmers gladly share their own code and they happily reuse others’. Social programming platforms like GitHub have normalized code foraging via their common platforms, enabling code search and reuse across different projects. Removing project borders may facilitate more efficient code foraging, and consequently faster programming. But looking for code across projects takes longer and, once found, may be more challenging to tailor to one’s needs. Learning how much code reuse goes on across projects, and identifying emerging patterns in past cross-project search behavior may help future foraging efforts. Our contribution is two fold. First, to understand cross-project code reuse, here we present an in-depth empirical study of cloning in GitHub. Using Deckard, a popular clone finding tool, we identified copies of code fragments across projects, and investigate their prevalence and characteristics using statistical and network science approaches, and with multiple case studies. By triangulating findings from different analysis methods, we find that cross-project cloning is prevalent in GitHub, ranging from cloning few lines of code to whole project repositories. Some of the projects serve as popular sources of clones, and others seem to contain more clones than their fair share. Moreover, we find that ecosystem cloning follows an onion model: most clones come from the same project, then from projects in the same application domain, and finally from projects in different domains. Second, we utilized these results to develop a novel tool named CLONE-HUNTRESS that streamlines finding and tracking code clones in GitHub. The tool is GitHub integrated, built around a user-friendly interface and runs efficiently over a modern database system. We describe the tool and make it publicly available at http://clone-det.ictic.sharif.edu/ .

Understanding the World Economy in Terms of Networks: A Survey of Data-Based Network Science Approaches on Economic Networks

The purpose of this paper is to survey studies for estimating and analyzing different types of economic networks We focus on data-based approaches that allow the direct estimation of the networks from empirical data without the need of relying on theoretical assumptions. Due to the fact that there is a large variety of different economic networks, e.g., interbank, investment, director, ownership, financial, product or trade networks, we present a systematic categorization of these by the meaning of the 'nodes' within these networks. These can correspond to banks, firms, investors, products, stocks etc. Furthermore, we review practical methods for graphically exploring such networks and discuss useful databases for obtaining the empirical data for the computational construction of economic networks.

Vulnerability analysis of power grid with the network science approach based on actual grid characteristics: A case study in Iran

The vulnerability analysis of power grids has long been of paramount interest to researchers and authorities. Network vulnerability analysis refers to beforehand evaluation of the impact of local failures on the network as a whole so that proper measures could be adopted before occurrence of any major crisis. Previous studies on power grid vulnerabilities that are based on network science concept have been mostly concentrated on topological measures and unweighted networks. In this paper, grid vulnerability identification is carried out by combined use of grid network topology and centrality measures along with real and physical characteristics of power grid. Namely line load and failure rate, with the help of Weighted PageRank algorithm. The main advantage of this approach is that it eliminates complex and time-consuming differential calculations, which results in reduced computation time, and allows real-time updating of the result based on changes in the actual grid characteristics. The proposed model was validated by implementation over a section of Iranian 400 kV and 230 kV power grids. The striking accuracy of the achieved result was confirmed by comparison against results that obtained through calculations by Iran’s national dispatching body.

Remotely Close Associations: Openness to Experience and Semantic Memory Structure

Openness to experience—the enjoyment of novel experiences and ideas—has many connections to cognitive processes. People high in openness to experience, for example, tend to be more creative and have broader general knowledge than people low in openness to experience. In the current study, we use a network science approach to examine if the organization of semantic memory differs between high and low groups of openness to experience. A sample of 516 adults completed measures of openness to experience (from the NEO Five–Factor Inventory–3 and Big Five Aspect Scales) and a semantic verbal fluency task. Next, the sample was split into half to form high ( n = 258) and low ( n = 258) openness to experience groups. Semantic networks were then constructed on the basis of their verbal fluency responses. Our results revealed that the high openness to experience group's network was more interconnected, flexible, and had better local organization of associations than the low openness to experience group. We also found that the high openness to experience group generated more responses on average and provided more unique responses than the low openness to experience group. Taken together, our results indicate that openness to experience is related to semantic memory structure. © 2018 European Association of Personality Psychology

Reopening Openness to Experience: A Network Analysis of Four Openness to Experience Inventories

ABSTRACTOpenness to Experience is a complex trait, the taxonomic structure of which has been widely debated. Previous research has provided greater clarity of its lower order structure by synthesizing facets across several scales related to Openness to Experience. In this study, we take a finer grained approach by investigating the item-level relations of four Openness to Experience inventories (Big Five Aspects Scale, HEXACO–100, NEO PI–3, and Woo et al.'s Openness to Experience Inventory), using a network science approach, which allowed items to form an emergent taxonomy of facets and aspects. Our results (N = 802) identified 10 distinct facets (variety-seeking, aesthetic appreciation, intellectual curiosity, diversity, openness to emotions, fantasy, imaginative, self-assessed intelligence, intellectual interests, and nontraditionalism) that largely replicate previous findings as well as three higher order aspects: two that are commonly found in the literature (intellect and experiencing; i.e., openness), and one novel aspect (open-mindedness). In addition, we demonstrate that each Openness to Experience inventory offers a unique conceptualization of the trait, and that some inventories provide broader coverage of the network space than others. Our findings establish a broader consensus of Openness to Experience at the aspect and facet level, which has important implications for researchers and the Openness to Experience inventories they use.

Restoration of services in disrupted infrastructure systems: A network science approach.

Due to the ubiquitous nature of disruptive extreme events, functionality of the critical infrastructure systems (CIS) is constantly at risk. In case of a disruption, in order to minimize the negative impact to the society, service networks operating on the CIS should be restored as quickly as possible. In this paper, we introduce a novel network science inspired measure to quantify the criticality of components within a disrupted service network and develop a restoration heuristic (Cent-Restore) that prioritizes restoration efforts based on this measure. As an illustrative case study, we consider a road network blocked by debris in the aftermath of a natural disaster. The debris obstructs the flow of relief aid and search-and-rescue teams between critical facilities and disaster sites, debilitating the emergency service network. In this context, the problem is defined as finding a schedule to clear the roads with the limited resources. First, we develop a mixed-integer programming model for the problem. Then we validate the efficiency and accuracy of the Cent-Restore heuristic on randomly generated instances by comparing it to the model. Furthermore, we use Cent-Restore to recommend real-time restoration plans for disrupted road networks of Boston and Manhattan and analyze the performance of the plans over time through resilience curves. We compare Cent-Restore to the current restoration guidelines proposed by FEMA and other strategies that prioritize the restoration efforts based on different measures. As a result we confirm the importance of including specific post-disruption attributes of the networks to create effective restoration strategies. Moreover, we explore the relationship between a service network’s resilience and its topological and operational characteristics under different disruption scenarios. The methods and insights provided in this work can be extended to other disrupted large-scale critical infrastructure systems in which the ultimate goal is to enable the functions of the overlaying service networks.

Alzheimer's disease research: a network science approach

A limit number of studies have applied bibliometric visualisation to explore the network structure of Alzheimer's disease (AD). This paper uses CiteSpace, Carrot and VOSviewer to analyse and visualise the intellectual structure of AD, characterising, quantitatively and qualitatively, the global scientific outputs and identifying their trends. The 9,753 articles obtained from the science citation index expanded database (SCI-E), from Web-of-Science, were analysed. The publication data is analysed computationally to identify publication patterns, a rate of growth of publications, types of authorship collaboration, the most productive authors, countries, institutions, journals, keywords, the citation and keyword patterns, the hotspots and the areas of research on the AD. The paper presents a detailed analytical mapping of AD research and charts the progress of discipline with various useful parameters. The authors expect to contribute to the theory, supplying researchers with new tools and enabling practitioners to improve their knowledge about the AD evolution and trends.

Network science in clinical trials: A patient-centered approach.

There has been a paradigm shift in translational oncology with the advent of novel molecular diagnostic tools in the clinic. However, several challenges are associated with the integration of these sophisticated tools into clinical oncology and daily practice. High-throughput profiling at the DNA, RNA and protein levels (omics) generate a massive amount of data. The analysis and interpretation of these is non-trivial but will allow a more thorough understanding of cancer. Linear modelling of the data as it is often used today is likely to limit our understanding of cancer as a complex disease, and at times under-performs to capture a phenotype of interest. Network science and systems biology-based approaches, using machine learning and network science principles, that integrate multiple data sources, can uncover complex changes in a biological system. This approach will integrate a large number of potential biomarkers in preclinical studies to better inform therapeutic decisions and ultimately make substantial progress towards precision medicine. It will however require development of a new generation of clinical trials. Beyond discussing the challenges of high-throughput technologies, this review will develop a framework on how to implement a network science approach in new clinical trial designs in order to advance cancer care.

Communication dynamics in complex brain networks.

Neuronal signalling and communication underpin virtually all aspects of brain activity and function. Network science approaches to modelling and analysing the dynamics of communication on networks have proved useful for simulating functional brain connectivity and predicting emergent network states. This Review surveys important aspects of communication dynamics in brain networks. We begin by sketching a conceptual framework that views communication dynamics as a necessary link between the empirical domains of structural and functional connectivity. We then consider how different local and global topological attributes of structural networks support potential patterns of network communication, and how the interactions between network topology and dynamic models can provide additional insights and constraints. We end by proposing that communication dynamics may act as potential generative models of effective connectivity and can offer insight into the mechanisms by which brain networks transform and process information.

Health policy and systems research collaboration pathways: lessons from a network science analysis

BackgroundThe 2004 Mexico Declaration, and subsequent World Health Assembly resolutions, proposed a concerted support for the global development of health policy and systems research (HPSR). This included coordination across partners and advocates for the field of HPSR to monitor the development of the field, while promoting decision-making power and implementing responsibilities in low- and middle-income countries (LMICs).MethodsWe used a network science approach to examine the structural properties of the HPSR co-authorship network across country economic groups in the PubMed citation database from 1990 to 2015. This analysis summarises the evolution of the publication, co-authorship and citation networks within HPSR.ResultsThis method allows identification of several features otherwise not apparent. The co-authorship network has evolved steadily from 1990 to 2015 in terms of number of publications, but more importantly, in terms of co-authorship network connectedness. Our analysis suggests that, despite growth in the contribution from low-income countries to HPSR literature, co-authorship remains highly localised. Lower middle-income countries have made progress toward global connectivity through diversified collaboration with various institutions and regions. Global connectivity of the upper middle-income countries (UpperMICs) are almost on par with high-income countries (HICs), indicating the transition of this group of countries toward becoming major contributors to the field.ConclusionsNetwork analysis allows examination of the connectedness among the HSPR community. Initially (early 1990s), research groups operated almost exclusively independently and, despite the topic being specifically on health policy in LMICs, HICs provided lead authorship. Since the early 1990s, the network has evolved significantly. In the full set analysis (1990–2015), for the first time in HPSR history, more than half of the authors are connected and lead authorship from UpperMICs is on par with that of HICs. This demonstrates the shift in participation and influence toward regions which HPSR primarily serves. Understanding these interactions can highlight the current strengths and future opportunities for identifying new strategies to enhance collaboration and support capacity-building efforts for HPSR.

How semantic memory structure and intelligence contribute to creative thought: a network science approach

ABSTRACTThe associative theory of creativity states that creativity is associated with differences in the structure of semantic memory, whereas the executive theory of creativity emphasises the role of top-down control for creative thought. For a powerful test of these accounts, individual semantic memory structure was modelled with a novel method based on semantic relatedness judgements and different criteria for network filtering were compared. The executive account was supported by a correlation between creative ability and broad retrieval ability. The associative account was independently supported, when network filtering was based on a relatedness threshold, but not when it was based on a fixed edge number or on the analysis of weighted networks. In the former case, creative ability was associated with shorter average path lengths and higher clustering of the network, suggesting that the semantic networks of creative people show higher small-worldness.

Technology transfer in innovation networks

This article offers a network perspective on the collaborative effects of technology transfer, providing a research methodology based on the network science paradigm. We argue that such an approach is able to map and describe the set of entities acting in the technology transfer environment and their mutual relationships. We outline how the connections’ patterns shape the organization of the networks by showing the role of the members within the system. By means of a case study of a transnational initiative aiming to support the technology transfer within European countries, we analyse the application of the network science approach, giving evidence of its relative implications.

The Next Decade of Big Data in Ecosystem Science

Ecosystem scientists will increasingly be called on to inform forecasts and define uncertainty about how changing planet conditions affect human well-being. We should be prepared to leverage the best tools available, including big data. Use of the term ‘big data’ implies an approach that includes capacity to aggregate, search, cross-reference, and mine large volumes of data to generate new understanding that can inform decision-making about emergent properties of complex systems. Although big-data approaches are not a panacea, there are large-scale environmental questions for which big data are well suited, even necessary. Ecosystems are complex biophysical systems that are not easily defined by any one data type, location, or time. Understanding complex ecosystem properties is data intensive along axes of volume (size of data), velocity (frequency of data), and variety (diversity of data types). Ecosystem scientists have employed impressive technology for generating high-frequency, large-volume data streams. Yet important challenges remain in both theoretical and infrastructural development to support visualization and analysis of large and diverse data. The way forward includes greater support for network science approaches, and for development of big-data infrastructure that includes capacity for visualization and analysis of integrated data products. Likewise, a new paradigm of cross-disciplinary training and professional evaluation is needed to increase the human capital to fully exploit big-data analytics in a way that is sustainable and adaptable to emerging disciplinary needs.