Network Theory Approaches Research Articles

Computational psychiatry and the evolving concept of a mental disorder

As a discipline, psychiatry is in the process of finding the right set of concepts to organize research and guide treatment. Dissatisfaction with the status quo as expressed in standard manuals has animated a number of computational paradigms, each proposing to rectify the received concept of mental disorder. We explore how different computational paradigms: normative modeling, network theory and learning-theoretic approaches like reinforcement learning and active inference, reconceptualize mental disorders. Although each paradigm borrows heavily from machine learning, they differ significantly in their methodology, their preferred level of description, the role they assign to the environment and, especially, the degree to which they aim to assimilate psychiatric disorders to a standard medical disease model. By imagining how these paradigms might evolve, we bring into focus three rather different visions for the future of psychiatric research. Although machine learning plays a crucial role in the articulation of these paradigms, it is clear that we are far from automating the process of conceptual revision. The leading role continues to be played by the theoretical, metaphysical and methodological commitments of the competing paradigms.

Esearch3D: propagating gene expression in chromatin networks to illuminate active enhancers.

Most cell type-specific genes are regulated by the interaction of enhancers with their promoters. The identification of enhancers is not trivial as enhancers are diverse in their characteristics and dynamic in their interaction partners. We present Esearch3D, a new method that exploits network theory approaches to identify active enhancers. Our work is based on the fact that enhancers act as a source of regulatory information to increase the rate of transcription of their target genes and that the flow of this information is mediated by the folding of chromatin in the three-dimensional (3D) nuclear space between the enhancer and the target gene promoter. Esearch3D reverse engineers this flow of information to calculate the likelihood of enhancer activity in intergenic regions by propagating the transcription levels of genes across 3D genome networks. Regions predicted to have high enhancer activity are shown to be enriched in annotations indicative of enhancer activity. These include: enhancer-associated histone marks, bidirectional CAGE-seq, STARR-seq, P300, RNA polymerase II and expression quantitative trait loci (eQTLs). Esearch3D leverages the relationship between chromatin architecture and transcription, allowing the prediction of active enhancers and an understanding of the complex underpinnings of regulatory networks. The method is available at: https://github.com/InfOmics/Esearch3D and https://doi.org/10.5281/zenodo.7737123.

In silico antibody-peptide epitope prediction for personalized cancer therapy

The human leukocyte antigen (HLA) system is a complex of genes on chromosome 6 in humans that encodes cell surface proteins responsible for regulating the immune system. Viral peptides presented to cancer cell surfaces by the HLA trigger the immune system to kill the cells, creating antibody–peptide epitopes (APE). This study proposes an in silico approach to identify patient-specific APEs by applying complex networks diagnostics on a novel multiplex data structure as an input for a deep learning model. The proposed analytical model identifies patient- and tumor-specific APEs with as few as 20 labeled data points. In addition, the proposed data structure employs complex network theory and other statistical approaches that can better explain and reduce the black box effect of deep learning. The proposed approach achieves an F1-score of 80% and 93% on patients one and two, respectively, and above 90% on tumor-specific tasks. In addition, it minimizes the required training time and the number of parameters.

Spatial-temporal network analysis of the public bicycle sharing system in Medellín, Colombia

The bicycle sharing system in Medellin has become one of the alternatives in the city to achieve more sustainable transportation. To know how people use it on a daily basis is a fundamental resource to plan future expansions and to make the most out of the public investments. Therefore, we present an analysis of the bicycle sharing system and the emerging patterns according to its usage, based on exploratory data analysis and network theory approaches, we characterize users (single-trip vs. multi-trip users, female vs. male, and young vs. adult users). The results show that the main challenges for the future planning of the bicycle sharing system in Medellin are the inclusion of female users, which represents half of the potential users but are less prone to use the system. We also consider the foresight of the placement of new stations and infrastructure according to the purpose of the trip, the topographical configuration of the city, and the articulation with the integrated transportation system in the city.

To Study the Impact of Social Network Analysis on Social Media Marketing Using Graph Theory

Marketing requires an understanding of relationships and current research has progressed much beyond the simple dyadic relationships to look at how social media networks influence the behavior of customers. Social media's power is fascinating as a seemingly inconsequential figure emerges from the ruins and attracts tens of thousands, if not millions, of followers and thus providing an average individual a huge platform to interact with the rest of the world. Academics have used Network theory and formal network analysis approaches to harvest the large pool of social media influencers available on the internet. The goal of this paper is to use various graph theory algorithms to portray the impact of social network analysis on internet marketing, with a primary focus on social media influencers, and to illustrate a variety of network measurements ideas that may be employed in social media management research that takes into account the enormous social media communication network.

Peat macropore networks – new insights into episodic and hotspot methane emission

Abstract. Peatlands are important natural sources of atmospheric methane (CH4) emissions. The production and emission of CH4 are strongly influenced by the diffusion of oxygen into the soil and of CH4 from the soil to the atmosphere, respectively. This diffusion, in turn, is controlled by the structure of macropore networks. The characterization of peat pore structure and connectivity through complex network theory approaches can give conceptual insight into how the relationship between the microscale pore space properties and CH4 emissions on a macroscopic scale is shaped. The evolution of the pore space that is connected to the atmosphere can also be conceptualized through a pore network modeling approach. Pore regions isolated from the atmosphere may further develop into anaerobic pockets, which are local hotspots of CH4 production in unsaturated peat. In this study, we extracted interconnecting macropore networks from three-dimensional X-ray micro-computed tomography (µCT) images of peat samples and evaluated local and global connectivity metrics for the networks. We also simulated the water retention characteristics of the peat samples using a pore network modeling approach and compared the simulation results with measured water retention characteristics. The results showed large differences in peat macropore structure and pore network connectivity between vertical soil layers. The macropore space was more connected and the flow paths through the peat matrix were less tortuous near the soil surface than at deeper depths. In addition, macroporosity, structural anisotropy, and average pore throat diameter decreased with depth. Narrower and more winding air-filled diffusion channels may reduce the rate of gas transport as the distance from the peat layer to the soil–air interface increases. The network analysis also suggests that both local and global network connectivity metrics, such as the network average clustering coefficient and closeness centrality, might serve as proxies for assessing the efficiency of gas diffusion in air-filled pore networks. However, the applicability of the network metrics was restricted to the high-porosity near-surface layer. The spatial extent and continuity of the pore network and the spatial distribution of the pores may be reflected in different network metrics in contrasting ways. The hysteresis of peat water content between wetting and drying was found to affect the evolution of the volume of connected air-filled pore space in unsaturated peat. Thus, the formation of anaerobic pockets may occur in a smaller soil volume and methanogenesis may be slower when the peat is wetting compared to in drying conditions. This hysteretic behavior might explain the hotspots and episodic spikes of CH4 emissions, and therefore, it should be taken into account in biogeochemical models.

Microbiome network traits in the rumen predict average daily gain in beef cattle under different backgrounding systems

BackgroundBackgrounding (BKG), the stage between weaning and finishing, significantly impacts feedlot performance in beef cattle; however, the contributions of the rumen microbiome to this growth stage remain unexplored. A longitudinal study was designed to assess how BKG affects rumen bacterial communities and average daily gain (ADG) in beef cattle. At weaning, 38 calves were randomly assigned to three BKG systems for 55 days (d): a high roughage diet within a dry lot (DL, n = 13); annual cover crop within a strip plot (CC, n = 13); and perennial pasture vegetation within rotational paddocks (PP, n = 12), as before weaning. After BKG, all calves were placed in a feedlot for 142 d and finished with a high energy ration. Calves were weighed periodically from weaning to finishing to determine ADG. Rumen bacterial communities were profiled by collecting fluid samples via oral probe and sequencing the V4 region of the 16S rRNA bacterial gene, at weaning, during BKG and finishing.ResultsRumen bacterial communities diverged drastically among calves once they were placed in each BKG system, including sharp decreases in alpha diversity for CC and DL calves only (P < 0.001). During BKG, DL calves showed a substantial increase of Proteobacteria (Succinivibrionaceae family) (P < 0.001), which also corresponded with greater ADG (P < 0.05). At the finishing stage, Proteobacteria bloomed for all calves, with no previous alpha or beta diversity differences being retained between groups. However, at finishing, PP calves showed a compensatory ADG, particularly greater than that in calves coming from DL BKG (P = 0.02). Microbiome network traits such as lower average shortest path length, and increased neighbor connectivity, degree, number and strength of bacterial interactions between rumen bacteria better predicted ADG during BKG and finishing than variation in specific taxonomic profiles.ConclusionsBacterial co-abundance interactions, as measured by network theory approaches, better predicted growth performance in beef cattle during BKG and finishing, than the abundance of specific taxa. These findings underscore the importance of early post weaning stages as potential targets for feeding interventions that can enhance metabolic interactions between rumen bacteria, to increase productive performance in beef cattle.

Sectoral Analysis of the US Stock Market through Complex Networks

Purpose: This study was carried out to analyze the structure of the aggregated network at the level of economic sectors and to reveal the central/peripheral sectors. Design/Methodology/Approach: The study uses the method of complex networks, with the two-step procedure employed to construct the network of economic sectors. First, the MST approach is utilized based on the cross-correlation of 496 stock price returns of the S&P500 Index. Then, the network is aggregated at the level of economic sectors. In addition, to analyze the graph, the network theory, multi-dimensional scaling (MDS), and relative importance approaches are employed. Findings: The results indicate that the sectoral network has a core/periphery structure. Based on the centrality measures, the ranking of sectors is provided. Of the 11 sectors, 3 are classified as central nodes, 4 as peripheral nodes, and the remaining 4 are classified as intermediate. In addition, the network configuration analysis demonstrates that the graph consists of two parts with a star-like structure, connected through the industrials sector. Practical Implications: An analysis of the cross-correlation network of aggregated assets at the level of economic sectors can be applied to ascertain the direction of stock price movements in the stock market. The division of sectors in the network into central and peripheral nodes has important implications for the management of an optimal portfolio of stocks. Originality/value: This study contributes to complex network theory and portfolio strategy design. A unique procedure is proposed to construct the network of economic sectors using the MST-based approach. Detection of the stock market network structure is vital for investors and regulators alike.

Analyzing the structural properties of bike-sharing networks: Evidence from the United States, Canada, and China

Abstract To solve the last-mile problem and promote low carbon transportation, hundreds of cities around the world have launched bike-sharing services. A growing number of studies have been mining bike-sharing data from different cities to understand bike-sharing systems. However, there is still a lack of comprehensive understanding of a bike-sharing network structure. Thus, this paper attempts to use complex network theory and spatial autocorrelation analysis approaches to examine structural properties of bike-sharing systems, quantify the importance of bike stations in the network, and evaluate their spatial clustering patterns. This research analyzes bike-sharing trip data from five different sized bike-sharing systems in the United States, Canada, and China. The results of network analysis reveal that the bike-sharing networks have a small-world property with a small average path length and a high clustering coefficient. Compared with medium-scale networks, the connectivity and accessibility of bike stations are relatively low in the large-scale bike-sharing network. Also, the connectivity of bike stations is positively related to their accessibility while they are not highly correlated with their intermediateness. The spatial clustering analysis results indicate that the spatial distributions of connectivity and accessibility of bike stations have a strong global spatial autocorrelation. Also, the stations with high connectivity and accessibility are concentrated in the urban centers, and the stations with low connectivity and accessibility are clustered in the periphery of the urban areas. However, their intermediateness does not show a strong global spatial clustering pattern, which implies that bike-sharing networks consist of multiple sub-groups. The findings provide new insights for transport planners and managers to understand bike-sharing systems and to improve their service quality.

The interaction between methionine and two aromatic amino acids is an abundant and multifunctional motif in proteins

Many types of non-covalent interactions give rise to a protein's natural structure and function. One such interaction involves an aromatic amino acid (phenylalanine (Phe), tryptophan (Trp), or tyrosine (Tyr)) and the sulfur of methionine (Met), the so-called methionine-aromatic interaction. The Met-aromatic interaction is well-established, and it is defined as involving one aromatic and one Met residue. However, in a small-scale survey, we recently noted that more than one aromatic residue can interact with one Met in a “bridging” motif of the general form Aro-Met-Aro. In the present work, a systematic survey of all protein structures available in the Protein Data Bank was carried out. About 70% of those structures contain any Met-aromatic interaction and over 40% contain a Met-aromatic bridge. Analysis of a smaller subset of protein structures, which omits entries with low resolution or high sequence homology, shows the same distribution. The relationship of bridging interactions and longer aromatic amino acid chains also was explored using network theory approaches. Met-Aro bridges were found in 8.4% of extended aromatic chains. Analysis of a different subset of proteins that contain embedded metal ions as reference points revealed that many Met-Aro bridges are at/near protein surfaces. These analyses, and some specific examples, lead to the proposal that Met-aromatic bridges play biological roles as stabilizers and protectors of protein structures, motifs for molecular recognition, and electron transfer mediators.

Review of the Quantitative Resilience Methods in Water Distribution Networks

Water distribution networks (WDNs) are critical contributors to the social welfare, economic growth, and public health in cities. Under the uncertainties that are introduced owing to climate change, urban development, aging components, and interdependent infrastructure, the WDN performance must be evaluated using continuously innovative methods and data acquisition. Quantitative resilience assessments provide useful information for WDN operators and planners, enabling support systems that can withstand disasters, recover quickly from outages, and adapt to uncertain environments. This study reviews contemporary approaches for quantifying the resilience of WDNs. 1508 journal articles published from 1950 to 2018 are identified under systematic review guidelines. 137 references that focus on the quantitative resilience methods of WDN are classified as surrogate measures, simulation methods, network theory approaches, and fault detection and isolation approaches. This study identifies the resilience capability of the WDNs and describes the related terms of absorptive, restorative, and adaptive capabilities. It also discusses the metrics, research progresses, and limitations associated with each method. Finally, this study indicates the challenges associated with the quantification of WDNs that should be overcome for achieving improved resilience assessments in the future.

Differences between jurors in classical music competitions: the MCDM and Network Theory approaches

Differences between jurors in classical music competitions: the MCDM and Network Theory approaches

Knowledge diversity and coordination: The effect of intrafirm inventor task networks on absorption speed

Research Summary: We combine the absorptive capacity and social network theory approaches to predict how intrafirm “whole” network characteristics affect the firm's speed of absorption of external knowledge to produce inventions. We start from the widely accepted view that distant, externally‐developed knowledge is difficult to absorb into the focal firm's own knowledge production. We suggest that high levels of intrafirm inventor task network diversity and task network density are essential for a diversity of knowledge inputs and coordinated actions regarding knowledge transfer, which in turn, reduces problems related to the absorption of knowledge—especially in the case of knowledge that is distant from the focal firm. The results of an event history study of 113 pharmaceutical firms that engaged in technology in‐licensing from 1986 to 2003 provide general support for our hypotheses.Managerial Summary: Firms keen to keep up with an uncertain and ever‐changing industry environment, can benefit from the speedy introduction of inventions. We examine how firms absorb licensed‐in technologies to nurture the rapid development of own related inventions. We show that a firm's absorption speed depends on the characteristics of the internal collaboration networks among the firm's inventor employees. More specifically, technologically diverse and well‐connected inventor networks improve the firm's ability to absorb external technologies quickly. This applies especially to externally acquired technologies that are unfamiliar to the firm. Depending on the distance of the acquired technology from the focal firm combined with speed‐inducing inventor network characteristics, our estimates suggest that firms can reduce the time needed for absorption by several months.

Eigenvector centrality for geometric and topological characterization of porous media.

Solving flow and transport through complex geometries such as porous media is computationally difficult. Such calculations usually involve the solution of a system of discretized differential equations, which could lead to extreme computational cost depending on the size of the domain and the accuracy of the model. Geometric simplifications like pore networks, where the pores are represented by nodes and the pore throats by edges connecting pores, have been proposed. These models, despite their ability to preserve the connectivity of the medium, have difficulties capturing preferential paths (high velocity) and stagnation zones (low velocity), as they do not consider the specific relations between nodes. Nonetheless, network theory approaches, where a complex network is a graph, can help to simplify and better understand fluid dynamics and transport in porous media. Here we present an alternative method to address these issues based on eigenvector centrality, which has been corrected to overcome the centralization problem and modified to introduce a bias in the centrality distribution along a particular direction to address the flow and transport anisotropy in porous media. We compare the model predictions with millifluidic transport experiments, which shows that, albeit simple, this technique is computationally efficient and has potential for predicting preferential paths and stagnation zones for flow and transport in porous media. We propose to use the eigenvector centrality probability distribution to compute the entropy as an indicator of the "mixing capacity" of the system.

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks.

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. AVAILABILITY: QuIN’s web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.

Network analysis and cross species comparison of protein-protein interaction networks of human, mouse and rat cytochrome P450 proteins that degrade xenobiotics.

Cytochrome P450 (CYP) enzymes that degrade xenobiotics play a critical role in the metabolism and biotransformation of drugs and xenobiotics in humans as well as experimental animal models such as mouse and rat. These proteins function as a network collectively as well as independently. Though there are several reports on the organization, regulation and functionality of various CYP enzymes at the molecular level, the understanding of organization and functionality of these proteins at the holistic level remain unclear. The objective of this study is to understand the organization and functionality of xenobiotic degrading CYP enzymes of human, mouse and rat using network theory approaches and to study species differences that exist among them at the holistic level. For our analysis, a protein-protein interaction (PPI) network for CYP enzymes of human, mouse and rat was constructed using the STRING database. Topology, centrality, modularity and robustness analyses were performed for our predicted CYP PPI networks that were then validated by comparison with randomly generated network models. Network centrality analyses of CYP PPI networks reveal the central/hub proteins in the network. Modular analysis of the CYP PPI networks of human, mouse and rat resulted in functional clusters. These clusters were subjected to ontology and pathway enrichment analysis. The analyses show that the cluster of the human CYP PPI network is enriched with pathways principally related to xenobiotic/drug metabolism. Endo-xenobiotic crosstalk dominated in mouse and rat CYP PPI networks, and they were highly enriched with endogenous metabolic and signaling pathways. Thus, cross-species comparisons and analyses of human, mouse and rat CYP PPI networks gave insights about species differences that existed at the holistic level. More investigations from both reductionist and holistic perspectives can help understand CYP metabolism and species extrapolation in a much better way.

Building Ties that Bind: Hub Firms and Collaborative Capacity in Inter-Organizational Networks

This conceptual paper focuses on the role of central “hub” organizations in inter-organizational networks building Collaborative Capacity (CC), or the ability to engage in inter-organizational systems with collective outcomes (Ostrom, 1999; Bardach, 2001; Hocevar et al., 2006). Within the context of the nonprofit sector, central capacity building “hub” organizations take on orchestrating roles within inter-organizational nonprofit networks by providing channels of communication, knowledge creation, and shared resources for network members. Network, stakeholder, and institutional theory approaches help to explore the influence of hub organizations on increased CC within networks and to examine CC as both an outcome (Ahuja, 2000; Weber, Lovrich, & Gaffney, 2007) as well as a driving force (Hocevar et al. 2006; Weinberg et. al., 2011) of an organizations’ network participation. Within this model, hub firms serve bridging and bonding roles for network participants (Burt, 1997), depending on the level of colla...

Variation in Macau Cantonese: the case of initial and final segments

AbstractThis article considers how meaningful social differences are conveyed through the use of initial and final segments in Macau Cantonese. The research presented in this article provides a sociolinguistic account of the use of initial and final segments in Macau Cantonese, and specifically illustrates how social meanings of initials and finals are variable, and do not only subsume explanations that derive from internal linguistic constraint categories. This study employed an eclectic and multidimensional sociolinguistic approach that combines sociolinguistic survey methods, social network theory and constructionist approaches, with a view to accounting for the dynamics underlying initial and final segment variation in Macau Cantonese. Results of this study indicate that social constraints such as conversation topic, the affective relations between interlocutors, and other social factors such as gender and social class possibly impinge on the distribution and use of initials and finals in Macau Cantonese.

Variation in the use of sentence final particles in Macau Cantonese

This paper considers how meaningful social information is conveyed with the use of sentence final particles in Macau Cantonese. The purpose in this research is to provide a general sociolinguistic account of sentence final particles in Macau Cantonese, and specifically to illustrate that social meanings of SFPs are variable, and do not constitute rigid or fixed meanings and interpretations. These social meanings, this paper argues, are a potential for indicating speaker identity at the individual level, and constitute a rich resource for communicating speaker identity in Macau Cantonese. This study uses an eclectic sociolinguistic approach, and combines elements of distributionist analyses, social network theory and constructionist approaches with a view to accounting for the dynamics underlying sentence final particle variation. Finally, this research considers constraints such as conversation topic, the affective relations between interlocutors, and gender as impinging on the distribution and use of SFPs in Macau Cantonese.

Analyzing divorce from cultural and network approaches

Because of the high divorce rate and subsequent outpouring of research on divorce in the United States and other Westernized countries, divorce is often framed from an individualistic perspective as a process that is negotiated between two individuals – as ‘his and her divorce. ’A primary assumption is that the spouses negotiate what is best for them and their children, seemingly irrespective of extended kin or culture. This study provides a more complex understanding of the role of culture in the divorce process by examining divorce from culture and network theory approaches. Interviews with 60 Mexican Americans who experienced divorce are combined with the extant literature to illustrate how culture and social networks shape divorce decisions and behaviors. Five themes surfaced from the interviews: (1) power differentials and gender roles, (2) female collective empowerment, (3) social capital, network density, and family members as stakeholders, (4) family members as bridges of structural holes and (5) religion as culture and law.