Showing papers on "Complex network published in 2021"

Evolutionary dynamics of higher-order interactions in social networks.

Abstract: We live and cooperate in networks. However, links in networks only allow for pairwise interactions, thus making the framework suitable for dyadic games, but not for games that are played in larger groups. Here, we study the evolutionary dynamics of a public goods game in social systems with higher-order interactions. First, we show that the game on uniform hypergraphs corresponds to the replicator dynamics in the well-mixed limit, providing a formal theoretical foundation to study cooperation in networked groups. Second, we unveil how the presence of hubs and the coexistence of interactions in groups of different sizes affects the evolution of cooperation. Finally, we apply the proposed framework to extract the actual dependence of the synergy factor on the size of a group from real-world collaboration data in science and technology. Our work provides a way to implement informed actions to boost cooperation in social groups. Alvarez-Rodriguez et al. examine group interactions by means of higher-order social networks. They propose a theoretical framework for studying real-world interactions and provide a case study of collaboration in science and technology.

The physics of higher-order interactions in complex systems

Abstract: Complex networks have become the main paradigm for modelling the dynamics of interacting systems. However, networks are intrinsically limited to describing pairwise interactions, whereas real-world systems are often characterized by higher-order interactions involving groups of three or more units. Higher-order structures, such as hypergraphs and simplicial complexes, are therefore a better tool to map the real organization of many social, biological and man-made systems. Here, we highlight recent evidence of collective behaviours induced by higher-order interactions, and we outline three key challenges for the physics of higher-order systems. Network representations of complex systems are limited to pairwise interactions, but real-world systems often involve higher-order interactions. This Perspective looks at the new physics emerging from attempts to characterize these interactions.

Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey

Abstract: Dynamic networks are used in a wide range of fields, including social network analysis, recommender systems and epidemiology. Representing complex networks as structures changing over time allow network models to leverage not only structural but also temporal patterns. However, as dynamic network literature stems from diverse fields and makes use of inconsistent terminology, it is challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a lot of attention in recent years for their ability to perform well on a range of network science tasks, such as link prediction and node classification. Despite the popularity of graph neural networks and the proven benefits of dynamic network models, there has been little focus on graph neural networks for dynamic networks. To address the challenges resulting from the fact that this research crosses diverse fields as well as to survey dynamic graph neural networks, this work is split into two main parts. First, to address the ambiguity of the dynamic network terminology we establish a foundation of dynamic networks with consistent, detailed terminology and notation. Second, we present a comprehensive survey of dynamic graph neural network models using the proposed terminology.

Deep attributed network representation learning of complex coupling and interaction

Abstract: Networks that can describe complex systems in nature are increasingly coupled and interacted, and effective modeling on complex coupling and interaction information is an important research direction of artificial intelligence. Representation learning provides us with a paradigm to solve such issues, but the current network representation learning methods are difficult to capture the coupling and interaction information in complex networks. In this paper, we propose a novel deep attributed network representation learning model framework (RolEANE), which can effectively preserve the highly nonlinear coupling and interactive network topological structure and attribute information. We design two different structural role proximity enhancement strategies for the deep autoencoder in the model framework, so that it can efficiently capture network topological structure and attribute information. In addition, the neighbor-modified Skip-Gram model in our model framework can efficiently and seamlessly integrate network topological structure and attribute information, and the selection of an appropriate representation learning output strategy can significantly improve the final performance of the algorithm. The experiments on four real datasets show that our method consistently outperforms the state-of-the-art network representation learning methods. On the node classification task, the average performance is improved by 4.52%–10.28% than the optimal baseline method; on the link prediction task, the average performance is 4.63% higher than the optimal baseline method.

Data-driven control of complex networks

Abstract: Our ability to manipulate the behavior of complex networks depends on the design of efficient control algorithms and, critically, on the availability of an accurate and tractable model of the network dynamics. While the design of control algorithms for network systems has seen notable advances in the past few years, knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. In this paper we overcome this limitation, and develop a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics. Our optimal controls are constructed using a finite set of data, where the unknown network is stimulated with arbitrary and possibly random inputs. Although our controls are provably correct for networks with linear dynamics, we also characterize their performance against noisy data and in the presence of nonlinear dynamics, as they arise in power grid and brain networks.

Percolation on complex networks: Theory and application

Abstract: In the last two decades, network science has blossomed and influenced various fields, such as statistical physics, computer science, biology and sociology, from the perspective of the heterogeneous interaction patterns of components composing the complex systems. As a paradigm for random and semi-random connectivity, percolation model plays a key role in the development of network science and its applications. On the one hand, the concepts and analytical methods, such as the emergence of the giant cluster, the finite-size scaling, and the mean-field method, which are intimately related to the percolation theory, are employed to quantify and solve some core problems of networks. On the other hand, the insights into the percolation theory also facilitate the understanding of networked systems, such as robustness, epidemic spreading, vital node identification, and community detection. Meanwhile, network science also brings some new issues to the percolation theory itself, such as percolation of strong heterogeneous systems, topological transition of networks beyond pairwise interactions, and emergence of a giant cluster with mutual connections. So far, the percolation theory has already percolated into the researches of structure analysis and dynamic modeling in network science. Understanding the percolation theory should help the study of many fields in network science, including the still opening questions in the frontiers of networks, such as networks beyond pairwise interactions, temporal networks, and network of networks. The intention of this paper is to offer an overview of these applications, as well as the basic theory of percolation transition on network systems.

The Physics of Financial Networks

Abstract: The field of Financial Networks is a paramount example of the novel applications of Statistical Physics that have made possible by the present data revolution. As the total value of the global financial market has vastly outgrown the value of the real economy, financial institutions on this planet have created a web of interactions whose size and topology calls for a quantitative analysis by means of Complex Networks. Financial Networks are not only a playground for the use of basic tools of statistical physics as ensemble representation and entropy maximization; rather, their particular dynamics and evolution triggered theoretical advancements as the definition of DebtRank to measure the impact and diffusion of shocks in the whole systems. In this review we present the state of the art in this field, starting from the different definitions of financial networks (based either on loans, on assets ownership, on contracts involving several parties -- such as credit default swaps, to multiplex representation when firms are introduced in the game and a link with real economy is drawn) and then discussing the various dynamics of financial contagion as well as applications in financial network inference and validation. We believe that this analysis is particularly timely since financial stability as well as recent innovations in climate finance, once properly analysed and understood in terms of complex network theory, can play a pivotal role in the transformation of our society towards a more sustainable world.

The dynamics of epidemic spreading on signed networks

Abstract: Over the past two decades, epidemic spreading on complex network has been a vibrant and highly successful research avenue. The dynamics of epidemic spreading on signed networks has nonetheless received fairly little attention. Signed networks contain edges that are labeled as either positive or negative, in relation to their propensity to either accelerate or mitigate epidemic spreading. To that effect, we here propose a modified signed-susceptible-infectious-susceptible epidemiological model, which incorporates positive and negative transmission rates based on structural balance theory. We also consider dynamical transmission rates to determine the influence of structural balance on the dynamics of epidemic spreading. We use Erdős-Renyi random networks and Barabasi-Albert scale-free networks, together with the Monte Carlo method, to determine the peak fraction of infected nodes and the epidemic thresholds. We also use the mean field analysis to show analytically the origin of the computationally obtained results, although of course the agreement is not perfect due to the impact of network structure.

Synchronization of Resilient Complex Networks Under Attacks

Abstract: One fundamental yet challenging issue in security control for resilient complex networks is to construct distributed control laws for the networks to perform various cooperative tasks in the presence of failures and attacks, where resilient indicates that the complex networks are exposed to the environment with cyber uncertainties and malicious adversaries. This is particularly important in today’s critical infrastructure networks since most of them are vulnerable to attacks in the era of the Internet. Inspired by this observation, this paper focuses on synchronization control for resilient complex networks subject to cyber and physical attacks, where the states of nodes being attacked may change abruptly (i.e., the synchronization error may suffer impulsive disturbances), and some nodes as well as their corresponding connections may not work in some instances. Suppose that a smart control center is equipped in the considered network to detect the attacks in real time. Furthermore, the nodes and communication channels are assumed to be recovered through some repair work after detecting the attacks. On the theoretical side, by using the ${M}$ -matrix theory, we get a few sufficient criteria to guarantee the achievement of secure synchronization against attacks on both nodes and communication links. On the algorithmic side, security control algorithm and architecture are proposed to select the coupling strength and the feedback gain matrix to realize synchronization. Finally, we perform two simulation examples to validate our theoretical results.

Learning function from structure in neuromorphic networks

Abstract: The connection patterns of neural circuits in the brain form a complex network. Collective signalling within the network manifests as patterned neural activity and is thought to support human cognition and adaptive behaviour. Recent technological advances permit macroscale reconstructions of biological brain networks. These maps, termed connectomes, display multiple non-random architectural features, including heavy-tailed degree distributions, segregated communities and a densely interconnected core. Yet, how computation and functional specialization emerge from network architecture remains unknown. Here we reconstruct human brain connectomes using in vivo diffusion-weighted imaging and use reservoir computing to implement connectomes as artificial neural networks. We then train these neuromorphic networks to learn a memory-encoding task. We show that biologically realistic neural architectures perform best when they display critical dynamics. We find that performance is driven by network topology and that the modular organization of intrinsic networks is computationally relevant. We observe a prominent interaction between network structure and dynamics throughout, such that the same underlying architecture can support a wide range of memory capacity values as well as different functions (encoding or decoding), depending on the dynamical regime the network is in. This work opens new opportunities to discover how the network organization of the brain optimizes cognitive capacity. The relationship between brain organization, connectivity and computation is not well understood. The authors construct neuromorphic artificial neural networks endowed with biological connection patterns derived from diffusion-weighted imaging. The neuromorphic networks are trained to perform a memory task, revealing an interaction between network structure and dynamics.

Combining network topology and information theory to construct representative brain networks.

Abstract: Network neuroscience employs graph theory to investigate the human brain as a complex network, and derive generalizable insights about the brain's network properties. However, graph-theoretical results obtained from network construction pipelines that produce idiosyncratic networks may not generalize when alternative pipelines are employed. This issue is especially pressing because a wide variety of network construction pipelines have been employed in the human network neuroscience literature, making comparisons between studies problematic. Here, we investigate how to produce networks that are maximally representative of the broader set of brain networks obtained from the same neuroimaging data. We do so by minimizing an information-theoretic measure of divergence between network topologies, known as the portrait divergence. Based on functional and diffusion MRI data from the Human Connectome Project, we consider anatomical, functional, and multimodal parcellations at three different scales, and 48 distinct ways of defining network edges. We show that the highest representativeness can be obtained by using parcellations in the order of 200 regions and filtering functional networks based on efficiency-cost optimization-though suitable alternatives are also highlighted. Overall, we identify specific node definition and thresholding procedures that neuroscientists can follow in order to derive representative networks from their human neuroimaging data.

Deep Learning for Security Problems in 5G Heterogeneous Networks

Abstract: With increasingly complex network structure, requirements for heterogeneous 5G are also growing. The aim of this study is to meet the network security performance under the existing high-capacity and highly reliable transmission. In this context, deep learning technology is adopted to solve the security problem of the 5G heterogeneous network. First, the security problems existing in 5G heterogeneous networks are presented, mainly from two aspects of the physical layer security problems and application prospects of deep learning in communication technology. Then the combination of deep learning and 5G heterogeneous networks is analyzed. The combination of deep learning technology, modulation information recognition, and beam formation is introduced. The application of deep learning in communications technology is analyzed, and the modulation information recognition and beamforming based on deep learning are introduced. Finally, the challenges of solving security problems in 5G heterogeneous networks by deep learning are explored. The results show that the deep learning model can solve the modulation recognition problem well, and the modulation mode of the convolutional neural network can well identify the modulation signals involved in the experiment. Therefore, deep learning has a good advantage in solving modulation recognition. In addition, compared to the traditional algorithm, the unsupervised beamforming algorithm based on deep learning proposed in this research can effectively reduce the computational complexity under different numbers of transmitting antennas, which verifies the superiority of the unsupervised beamforming algorithm based on deep learning proposed in this research. Therefore, the present work provides a good idea for solving the security problem of 5G heterogeneous networks.

Sequential Restorations of Complex Networks After Cascading Failures

Abstract: Cascading failure on complex networks has been extensively studied over the past decade. However, restoration of networks from cascading failure is still relatively unexplored. In this paper, we consider cascading failure in conjunction with the restoration process involving repairing the failed nodes in a sequential fashion. Depending on the availability of resources, we tackle the sequential recovery problem from two distinct approaches, namely, result-oriented and resource-oriented restoration approaches. In the result-oriented approach, we aim to restore the network to the largest extent and within the shortest time. Heuristic network restoration strategies based on node load or degree are proposed. For resource-oriented restoration, we aim to maximize the increase of network size with a given number of nodes to be repaired, and we propose a novel iterative strategy to improve performance. Simulation results on the Barabasi–Albert scale-free network, Internet autonomous system-level network, and IEEE 300 bus power system have demonstrated the effectiveness of the proposed sequential recovery strategies.

Temporal properties of higher-order interactions in social networks.

Abstract: Human social interactions in local settings can be experimentally detected by recording the physical proximity and orientation of people Such interactions, approximating face-to-face communications, can be effectively represented as time varying social networks with links being unceasingly created and destroyed over time Traditional analyses of temporal networks have addressed mostly pairwise interactions, where links describe dyadic connections among individuals However, many network dynamics are hardly ascribable to pairwise settings but often comprise larger groups, which are better described by higher-order interactions Here we investigate the higher-order organizations of temporal social networks by analyzing five publicly available datasets collected in different social settings We find that higher-order interactions are ubiquitous and, similarly to their pairwise counterparts, characterized by heterogeneous dynamics, with bursty trains of rapidly recurring higher-order events separated by long periods of inactivity We investigate the evolution and formation of groups by looking at the transition rates between different higher-order structures We find that in more spontaneous social settings, group are characterized by slower formation and disaggregation, while in work settings these phenomena are more abrupt, possibly reflecting pre-organized social dynamics Finally, we observe temporal reinforcement suggesting that the longer a group stays together the higher the probability that the same interaction pattern persist in the future Our findings suggest the importance of considering the higher-order structure of social interactions when investigating human temporal dynamics

An improved gravity model to identify influential nodes in complex networks based on k-shell method

Abstract: To find the important nodes in complex networks is a fundamental issue. A number of methods have been recently proposed to address this problem but most previous studies have the limitations, and few of them considering both local and global information of the network. The location of node, which is a significant property of a node in the network, is seldom considered in identifying the importance of nodes before. To address this issue, we propose an improved gravity centrality measure on the basis of the k-shell algorithm named KSGC to identify influential nodes in the complex networks. Our method takes the location of nodes into consideration, which is more reasonable compared to original gravity centrality measure. Several experiments on real-world networks are conducted to show that our method can effectively evaluate the importance of nodes in complex networks.

Efficient Hyperparameter Optimization for Convolution Neural Networks in Deep Learning: A Distributed Particle Swarm Optimization Approach

Abstract: Convolution neural network (CNN) is a kind of powerful and efficient deep learning approach that has obtained great success in many real-world applications. However, due to its complex network stru...

Functional networks of the brain: from connectivity restoration to dynamic integration

Abstract: A review of physical and mathematical methods for reconstructing the functional networks of the brain based on recorded brain activity is presented. Various methods are considered, as are their advantages and disadvantages and limitations of the application. Problems applying the theory of complex networks to reconstructed functional networks of the brain to explain the effects of dynamic integration in the brain and their influence on the diverse functionality of the brain and consciousness, as well as processes leading to the pathological activity of the central nervous system, are examined. Questions concerning the application of these approaches are considered both to describe the functioning of the brain in various cognitive and pathological processes and to create new brain–computer interfaces based on the detection of changes in functional connections in the brain.

The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling.

Abstract: The ability to map causal interactions underlying genetic control and cellular signaling has led to increasingly accurate models of the complex biochemical networks that regulate cellular function. These network models provide deep insights into the organization, dynamics, and function of biochemical systems: for example, by revealing genetic control pathways involved in disease. However, the traditional representation of biochemical networks as binary interaction graphs fails to accurately represent an important dynamical feature of these multivariate systems: some pathways propagate control signals much more effectively than do others. Such heterogeneity of interactions reflects canalization-the system is robust to dynamical interventions in redundant pathways but responsive to interventions in effective pathways. Here, we introduce the effective graph, a weighted graph that captures the nonlinear logical redundancy present in biochemical network regulation, signaling, and control. Using 78 experimentally validated models derived from systems biology, we demonstrate that 1) redundant pathways are prevalent in biological models of biochemical regulation, 2) the effective graph provides a probabilistic but precise characterization of multivariate dynamics in a causal graph form, and 3) the effective graph provides an accurate explanation of how dynamical perturbation and control signals, such as those induced by cancer drug therapies, propagate in biochemical pathways. Overall, our results indicate that the effective graph provides an enriched description of the structure and dynamics of networked multivariate causal interactions. We demonstrate that it improves explainability, prediction, and control of complex dynamical systems in general and biochemical regulation in particular.

Synchronization Analysis for Nonlinear Complex Networks With Reaction-Diffusion Terms Using Fuzzy-Model-Based Approach

Abstract: The synchronization of a class variable-coefficient complex networks with reaction-diffusion terms is investigated. The objective of this article is to synthesize a state-feedback controller, which is designed based on a Takagi–Sugeno (T–S) fuzzy model of the variable-coefficient complex network with reaction-diffusion terms such that the closed-loop system is synchronized. With the support of Green formula and some matrix inequality techniques, and utilizing the upper and lower bounds of the membership functions, membership-function-dependent (MFD) synchronization conditions guaranteeing the system's synchronization are obtained in the form of linear matrix inequalities (LMIs). A numerical example is presented to verify the analysis results and illustrate the effectiveness of the proposed synchronization conditions.

A generalized gravity model for influential spreaders identification in complex networks

Abstract: How to identify influential spreaders in complex networks is still an open issue in network science. Many approaches from different perspectives have been proposed to identify vital nodes in complex networks. In these models, gravity model is an effective model to find vital nodes based on local information and path information. However, gravity model just uses degree of the node to judge local information, which is not precise. To address this issue, a generalized gravity model is proposed in this paper. Generalized gravity model measures local information from both local clustering coefficient and degree of each node, which is more comprehensive. Also, parameter α can be modified in different applications to get better performance. Generalized gravity model can degenerate into gravity model when α = 0 . Promising results from experiments on four real-world networks demonstrate the effectiveness of the proposed method.

Bilateral Grid Learning for Stereo Matching Networks

Abstract: Real-time performance of stereo matching networks is important for many applications, such as automatic driving, robot navigation and augmented reality (AR). Although significant progress has been made in stereo matching networks in recent years, it is still challenging to balance real-time performance and accuracy. In this paper, we present a novel edge-preserving cost volume upsampling module based on the slicing operation in the learned bilateral grid. The slicing layer is parameter-free, which allows us to obtain a high quality cost volume of high resolution from a low-resolution cost volume under the guide of the learned guidance map efficiently. The proposed cost volume upsampling module can be seamlessly embedded into many existing stereo matching networks, such as GCNet, PSMNet, and GANet. The resulting networks are accelerated several times while maintaining comparable accuracy. Furthermore, we design a real-time network (named BGNet) based on this module, which outperforms existing published real-time deep stereo matching networks, as well as some complex networks on the KITTI stereo datasets. The code is available at https://github.com/YuhuaXu/BGNet.