Showing papers on "Network theory published in 2021"

Trust Mechanism of Feedback Trust Weight in Multimedia Network

Abstract: It is necessary to solve the inaccurate data arising from data reliability ignored by most data fusion algorithms drawing upon collaborative filtering and fuzzy network theory. Therefore, a model i...

Networks, Creativity, and Time: Staying Creative through Brokerage and Network Rejuvenation

Abstract: In this paper, we adopt a dynamic perspective on networks and creativity to propose that the oft-theorized creative benefits of open networks and heterogeneous content are less likely to be...

Person-specific networks in psychopathology: Past, present, and future.

Abstract: In the psychological network approach, mental disorders such as major depressive disorder are conceptualized as networks. The network approach focuses on the symptom structure or the connections between symptoms instead of the severity (i.e., mean level) of a symptom. To infer a person-specific network for a patient, time-series data are needed. By far the most common model to statistically model the person-specific interactions between symptoms or momentary states has been the vector autoregressive (VAR) model. Although the VAR model helps to bring psychological network theory into clinical research and closer to clinical practice, several discrepancies arise when we map the psychological network theory onto the VAR-based network models. These challenges and possible solutions are discussed in this review.

Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace

Abstract: The development of models and methodology for the analysis of data from multiple heterogeneous networks is of importance both in statistical network theory and across a wide spectrum of application domains. Although single-graph analysis is well-studied, multiple graph inference is largely unexplored, in part because of the challenges inherent in appropriately modeling graph differences and yet retaining sufficient model simplicity to render estimation feasible. This paper addresses exactly this gap, by introducing a new model, the common subspace independent-edge multiple random graph model, which describes a heterogeneous collection of networks with a shared latent structure on the vertices but potentially different connectivity patterns for each graph. The model encompasses many popular network representations, including the stochastic blockmodel. The model is both flexible enough to meaningfully account for important graph differences, and tractable enough to allow for accurate inference in multiple networks. In particular, a joint spectral embedding of adjacency matrices-the multiple adjacency spectral embedding-leads to simultaneous consistent estimation of underlying parameters for each graph. Under mild additional assumptions, the estimates satisfy asymptotic normality and yield improvements for graph eigenvalue estimation. In both simulated and real data, the model and the embedding can be deployed for a number of subsequent network inference tasks, including dimensionality reduction, classification, hypothesis testing, and community detection. Specifically, when the embedding is applied to a data set of connectomes constructed through diffusion magnetic resonance imaging, the result is an accurate classification of brain scans by human subject and a meaningful determination of heterogeneity across scans of different individuals.

Joint Virtual Network Topology Design and Embedding for Cybertwin-Enabled 6G Core Networks

Abstract: To efficiently allocate heterogeneous resources for customized services, in this article, we propose a network virtualization (NV)-based network architecture in cybertwin-enabled 6G core networks. In particular, we investigate how to optimize the virtual network (VN) topology (which consists of several virtual nodes and a set of intermediate virtual links) and determine the resultant VN embedding in a joint way over a cybertwin-enabled substrate network. To this end, we formulate an optimization problem whose objective is to minimize the embedding cost, while ensuring that the end-to-end (E2E) packet delay requirements are satisfied. The queueing network theory is utilized to evaluate each service’s E2E packet delay, which is a function of the resources assigned to the virtual nodes and virtual links for the embedded VN. We reveal that the problem under consideration is formally a mixed-integer nonlinear program (MINLP) and propose an improved brute-force search algorithm to find its optimal solutions. To enhance the algorithm’s scalability and reduce the computational complexity, we further propose an adaptively weighted heuristic algorithm to obtain near-optimal solutions to the problem for large-scale networks. Simulations are conducted to show that the proposed algorithms can effectively improve network performance compared to other benchmark algorithms.

Deciphering the generating rules and functionalities of complex networks.

Abstract: Network theory helps us understand, analyze, model, and design various complex systems. Complex networks encode the complex topology and structural interactions of various systems in nature. To mine the multiscale coupling, heterogeneity, and complexity of natural and technological systems, we need expressive and rigorous mathematical tools that can help us understand the growth, topology, dynamics, multiscale structures, and functionalities of complex networks and their interrelationships. Towards this end, we construct the node-based fractal dimension (NFD) and the node-based multifractal analysis (NMFA) framework to reveal the generating rules and quantify the scale-dependent topology and multifractal features of a dynamic complex network. We propose novel indicators for measuring the degree of complexity, heterogeneity, and asymmetry of network structures, as well as the structure distance between networks. This formalism provides new insights on learning the energy and phase transitions in the networked systems and can help us understand the multiple generating mechanisms governing the network evolution.

Integrating Network Centrality and Node-Place Model to Evaluate and Classify Station Areas in Shanghai

Abstract: Transit-oriented development (TOD) is generally understood as an effective urban design model for encouraging the use of public transportation. Inspired by TOD, the node-place (NP) model was developed to investigate the relationship between transport stations and land use. However, existing studies construct the NP model based on the statistical attributes, while the importance of travel characteristics is ignored, which arguably cannot capture the complete picture of the stations. In this study, we aim to integrate the NP model and travel characteristics with systematic insights derived from network theory to classify stations. A node-place-network (NPN) model is developed by considering three aspects: land use, transportation, and travel network. Moreover, the carrying pressure is proposed to quantify the transport service pressure of the station. Taking Shanghai as a case study, our results show that the travel network affects the station classification and highlights the imbalance between the built environment and travel characteristics.

Lagrangian betweenness as a measure of bottlenecks in dynamical systems with oceanographic examples.

Abstract: The study of connectivity patterns in networks has brought novel insights across diverse fields ranging from neurosciences to epidemic spreading or climate. In this context, betweenness centrality has demonstrated to be a very effective measure to identify nodes that act as focus of congestion, or bottlenecks, in the network. However, there is not a way to define betweenness outside the network framework. By analytically linking dynamical systems and network theory, we provide a trajectory-based formulation of betweenness, called Lagrangian betweenness, as a function of Lyapunov exponents. This extends the concept of betweenness beyond the context of network theory relating hyperbolic points and heteroclinic connections in any dynamical system to the structural bottlenecks of the network associated with it. Using modeled and observational velocity fields, we show that such bottlenecks are present and surprisingly persistent in the oceanic circulation across different spatio-temporal scales and we illustrate the role of these areas in driving fluid transport over vast oceanic regions. Analyzing plankton abundance data from the Kuroshio region of the Pacific Ocean, we find significant spatial correlations between measures of diversity and betweenness, suggesting promise for ecological applications.

Social Network and Temporal Myopia

Abstract: This paper examines the link between the social networks surrounding business leaders and temporal myopia in strategic planning. Specifically, we hypothesize that processes characteristic of being ...

A Narrative Review of Network Studies in Depression: What Different Methodological Approaches Tell Us About Depression.

Abstract: The network theory of psychopathology proposes that mental disorders arise from direct interactions between symptoms. This theory provides a promising framework to understand the development and maintenance of mental disorders such as depression. In this narrative review, we summarize the literature on network studies in the field of depression. Four methodological network approaches are distinguished: (i) studies focusing on symptoms at the macro-level vs. (ii) on momentary states at the micro-level, and (iii) studies based on cross-sectional vs. (iv) time-series (dynamic) data. Fifty-six studies were identified. We found that different methodological approaches to network theory yielded largely inconsistent findings on depression. Centrality is a notable exception: the majority of studies identified either positive affect or anhedonia as central nodes. To aid future research in this field, we outline a novel complementary network theory, the momentary affect dynamics (MAD) network theory, to understand the development of depression. Furthermore, we provide directions for future research and discuss if and how networks might be used in clinical practice. We conclude that more empirical network studies are needed to determine whether the network theory of psychopathology can indeed enhance our understanding of the underlying structure of depression and advance clinical treatment.

Tie-Decay Networks in Continuous Time and Eigenvector-Based Centralities

Abstract: Network theory is a useful framework for studying interconnected systems of interacting entities Many networked systems evolve continuously in time, but most existing methods for the analysis of time-dependent networks rely on discrete or discretized time In this paper, we propose an approach for studying networks that evolve in continuous time by distinguishing between interactions , which we model as discrete contacts, and ties , which encode the strengths of relationships over time To illustrate our tie-decay network formalism, we adapt the well-known PageRank centrality score to our tie-decay framework in a mathematically tractable and computationally efficient way We apply this framework to a synthetic example and then use it to study a network of retweets during the 2012 National Health Service controversy in the United Kingdom Our work also provides guidance for similar generalizations of other tools from network theory to continuous-time networks with tie decay, including for applications to streaming data

Mixing Patterns in Social Trust Networks: A Social Identity Theory Perspective

Abstract: Mixing patterns (MPs) in social trust networks (STNs) are increasingly attracting attention because they can assist analysts in designing information dissemination tactics and planning electronic word-of-mouth (eWOM) campaigns. However, the existing studies on MPs do not explain the assortative or disassortative tendencies of STNs due to their omission of the support of the sociological theory, as well as that of network theory. To address this issue, this study investigates the MPs in STNs from the standpoint of social identity theory (SIT). The user trust networks (UTNs) are modeled by a directed multigraph (DMG). Then, the structural properties of homogeneous trust networks and heterogeneous trust networks are explored via measures that include degree centrality, the correlation coefficient (CC), the cumulative distribution of the ratio of trust degree to distrust degree (CDRTD), and the assortativity coefficient. The MPs of homogeneous trust networks and heterogeneous trust networks are explained from the perspective of SIT. An experiential evaluation is conducted in the constructed homogeneous trust networks and heterogeneous trust networks using a real-world data set crawled from Epinions. The research findings indicate that the MPs in homogeneous trust networks tend toward assortative mixing (AM), and those in heterogeneous trust networks tend toward disassortative mixing (DM). The experimental results show that the performance of the proposed approach is superior to that of the state-of-the-art approach to influential user identification.

The Network Theory of Psychiatric Disorders: A Critical Assessment of the Inclusion of Environmental Factors.

Abstract: Borsboom and colleagues have recently proposed a “network theory” of psychiatric disorders that conceptualizes psychiatric disorders as relatively stable networks of causally interacting symptoms They have also claimed that the network theory should include non-symptom variables such as environmental factors How are environmental factors incorporated in the network theory, and what kind of explanations of psychiatric disorders can such an “extended” network theory provide? The aim of this article is to critically examine what explanatory strategies the network theory that includes both symptoms and environmental factors can accommodate We first analyze how proponents of the network theory conceptualize the relations between symptoms and between symptoms and environmental factors Their claims suggest that the network theory could provide insight into the causal mechanisms underlying psychiatric disorders We assess these claims in light of network analysis, Woodward’s interventionist theory, and mechanistic explanation, and show that they can only be satisfied with additional assumptions and requirements Then, we examine their claim that network characteristics may explain the dynamics of psychiatric disorders by means of a topological explanatory strategy We argue that the network theory could accommodate topological explanations of symptom networks, but we also point out that this poses some difficulties Finally, we suggest that a multilayer network account of psychiatric disorders might allow for the integration of symptoms and non-symptom factors related to psychiatric disorders and could accommodate both causal/mechanistic and topological explanations

Incentivizing of inside sales units — the interplay of incentive types and unit structures

Abstract: Although companies are increasingly deploying inside sales units, knowledge is scarce regarding how to effectively incentivize them In addressing this neglect, this study draws on network theory t

A Survey of Compositional Signal Flow Theory

Abstract: Signal flow graphs are combinatorial models for linear dynamical systems, playing a foundational role in control theory and engineering. In this survey, we overview a series of works [3, 10, 11, 13, 15–18, 31, 51, 63] that develop a compositional theory of these structures, and explore several striking insights emerging from this approach. In particular, the use of string diagrams, a categorical syntax for graphical models, allows to switch from the traditional combinatorial treatment of signal flow graphs to an algebraic characterisation. Within this framework, signal flow graphs may then be treated as a fully-fledged (visual) programming language, and equipped with important meta-theoretical properties, such as a complete axiomatisation and a full abstraction theorem. Moreover, the abstract viewpoint offered by string diagrams reveals that the same algebraic structures modelling linear dynamical systems may also be used to interpret diverse kinds of models, such as electrical circuits and Petri nets.In this respect, our work is a contribution to compositional network theory (see e.g., [1, 2, 4–6, 9, 12, 20, 21, 23, 24, 26, 28–30, 32, 37, 49, 59], ?), an emerging multidisciplinary research programme aiming at a uniform compositional study of different sorts of computational models.

Economic Interplay Forecasting Business Success

Abstract: A startup ecosystem is a dynamic environment in which several actors, such as investors, venture capitalists, angels, and facilitators, are the protagonists of a complex interplay. Most of these interactions involve the flow of capital whose size and direction help to map the intricate system of relationships. This quantity is also considered a good proxy of economic success. Given the complexity of such systems, it would be more desirable to supplement this information with other informative features, and a natural choice is to adopt mathematical measures. In this work, we will specifically consider network centrality measures, borrowed by network theory. In particular, using the largest publicly available dataset for startups, the Crunchbase dataset, we show how centrality measures highlight the importance of particular players, such as angels and accelerators, whose role could be underestimated by focusing on collected funds only. We also provide a quantitative criterion to establish which firms should be considered strategic and rank them. Finally, as funding is a widespread measure for success in economic settings, we investigate to which extent this measure is in agreement with network metrics; the model accurately forecasts which firms will receive the highest funding in future years.

Transitions in random graphs of fixed degrees with many short cycles

Abstract: We analyze maximum entropy random graph ensembles with constrained degrees, drawn from arbitrary degree distributions, and a tuneable number of three-cycles (triangles). We find that such ensembles generally exhibit two transitions, a clustering and a shattering transition, separating three distinct regimes. At the clustering transition, the graphs change from typically having only isolated cycles to forming cycle clusters. At the shattering transition the graphs break up into many small cliques to achieve the desired three-cycle density. The locations of both transitions depend nontrivially on the system size. We derive a general formula for the three-cycle density in the regime of isolated cycles, for graphs with degree distributions that have finite first and second moments. For bounded degree distributions we present further analytical results on cycle densities and phase transition locations, which, while non-rigorous, are all validated via MCMC sampling simulations. We show that the shattering transition is of an entropic nature, occurring for all three-cycle density values, provided the system is large enough.

The Common Core State Standards Initiative as an Innovation Network

Abstract: This article analyzes the Common Core State Standards initiative as an innovation network. Using narrative data and quantitative analysis of hypertext linkages on the World Wide Web, we describe a ...

The network origins of Schumpeterian innovation

Abstract: This paper investigates the key driving features of the evolving long-term division of innovative labor in biotechnology and pharmaceuticals from 1981 to 2012. Our main goal is to find if technological trajectories and mechanisms discovered by Orsenigo et al. (Res Policy 30(3): 485–508, 2001) as the main drivers of the structural configuration of the network of collaborative alliances have been at work in the long-term evolution of the industry. We extensively analyze the evolving dynamics of the degree distribution and the higher order properties of the R&D network. As in Orsenigo et al. (Res Policy 30(3): 485–508, 2001), we find that polarization through preferential attachment driven by large pharmaceutical companies as Developers and by the entry of new specialized biotechnology companies acting as Originators of new R&D opportunities dominated the early stages of the biotechnology revolution. Later on the evolution of the collaborative network has been shaped by roles’ transitions between Originators and Developers of innovative ideas. Against this background, we introduce parsimonious model of network formation and evolution is introduced, to account for some essential features of the data generating processes underlying the evolution of the network.

Network Theory–Driven Construction Logic Knowledge Network: Process Modeling and Application in Highway Projects

Abstract: Determining a reasonable project duration is one of the most critical activities required by project owner agencies for successful project letting and delivery. Most owner agencies, specifi...

Graph and Network Theory for the Analysis of Criminal Networks

Abstract: Social Network Analysis is the use of Network and Graph Theory to study social phenomena, which was found to be highly relevant in areas like Criminology. This chapter provides an overview of key methods and tools that may be used for the analysis of criminal networks, which are presented in a real-world case study. Starting from available juridical acts, we have extracted data on the interactions among suspects within two Sicilian Mafia clans, obtaining two weighted undirected graphs. Then, we have investigated the roles of these weights on the criminal networks properties, focusing on two key features: weight distribution and shortest path length. We also present an experiment that aims to construct an artificial network which mirrors criminal behaviours. To this end, we have conducted a comparative degree distribution analysis between the real criminal networks, using some of the most popular artificial network models: Watts-Strogats, Erdős-Renyi, and Barabasi-Albert, with some topology variations. This chapter will be a valuable tool for researchers who wish to employ social network analysis within their own area of interest.

Transfer network of high-speed rail and aviation: Structure and critical components

Abstract: In this paper, we utilize network theory to model and study the coupled high-speed rail–aviation network (HSR–AN) in China. The HSR–AN depicting the transport services is modelled as a transfer network in P-space (TNP) in this research. The basic topology properties of the TNP and the corresponding critical components (e.g., nodes and areas) are analysed. As one type of spatial networks coupled two transportation networks, TNP of HSR–AN, counterintuitively, exhibits small-world properties which can be attributed by offering service connecting long-distance cities without transfer and complementary relationship between two transport modes. Additionally, an analysis of vital node (area) in vulnerability environment indicates that the importance of network components critically depends on the economic development level, population density, and geographical features in the corresponding city. Besides, the failure of high-speed rail station generates worse effect than that on airport in HSR–ANs. The findings are beneficial for enhancing the efficiency of HSR–ANs and developing emergency response plans for transport managers.