arXiv subjects

Chuang Ma

Publications and source records attributed to Chuang Ma.

29 records · Page 2Linked to original sources

arXiv2019

Spontaneous symmetry breaking and discontinuous phase transition for spreading dynamics in multiplex networks

We propose a spreading model in multilayer networks and study the nature of nonequilibrium phase transition in the model. The model integrates the susceptible-infected-susceptible (or susceptible-infected-recovered) spreading dynamics with a biased diffusion process among different layers. A parameter $α$ is introduced to control the bias of the diffusion process, such that each individual prefers to move to one layer with more infected (or recovered) neighbors for larger values of $α$. Using stochastic simulations and mean-field theory, we show that the type of phase transition from a disease-free phase to an endemic phase depends on the value of $α$. When $α$ is small enough, the system undergoes a usual continuous phase transition as an effective spreading rate $β$ increases, as in single-layer networks. Interestingly, when $α$ exceeds a critical value the system shows either a hybrid two-step phase transition or a one-step discontinuous phase transition as $β$ increases. The former contains a continuous transition between the disease-free phase and a low-prevalence endemic phase, and a discontinuous transition between the low-prevalence endemic phase and a high-prevalence endemic phase. For the latter, only a discontinuous transition occurs from the disease-free phase directly to the high-prevalence endemic phase. Moreover, we show that the discontinuous transition is always accompanied by a spontaneous symmetry breaking in occupation probabilities of individuals in each layer.

physics.soc-ph↗

arXiv2018

Electric field-induced creation and directional motion of domain walls and skyrmion bubbles

Magnetization dynamics driven by an electric field could provide long-term benefits to information technologies because of its ultralow power consumption. Meanwhile, the Dzyaloshinskii-Moriya interaction in interfacially asymmetric multilayers consisting of ferromagnetic and heavy-metal layers can stabilize topological spin textures, such as chiral domain walls, skyrmions, and skyrmion bubbles. These topological spin textures can be controlled by an electric field, and hold promise for building advanced spintronic devices. Here, we present an experimental and numerical study on the electric field-induced creation and directional motion of topological spin textures in magnetic multilayer films and racetracks with thickness gradient and interfacial Dzyaloshinskii-Moriya interaction at room temperature. We find that the electric field-induced directional motion of chiral domain wall is accompanied with the creation of skyrmion bubbles at certain conditions. We also demonstrate that the electric field variation can induce motion of skyrmion bubbles. Our findings may provide opportunities for developing skyrmion-based devices with ultralow power consumption.

cond-mat.mes-hall↗

arXiv2018

Data based reconstruction of complex multiplex networks

It has been recognized that many complex dynamical systems in the real world require a description in terms of multiplex networks, where a set of common, mutually connected nodes belong to distinct network layers and play a different role in each layer. In spite of recent progress towards data based inference of single-layer networks, to reconstruct complex systems with a multiplex structure remains largely open. We articulate a mean-field based maximum likelihood estimation framework to solve this outstanding and challenging problem. We demonstrate the power of the reconstruction framework and characterize its performance using binary time series from a class of prototypical duplex network systems that host two distinct types of spreading dynamics. In addition to validating the framework using synthetic and real-world multiplex networks, we carry out a detailed analysis to elucidate the impacts of structural and dynamical parameters as well as noise on the reconstruction accuracy and robustness.

physics.soc-ph↗

arXiv2018

An improved belief propagation algorithm for detecting meso-scale structure in complex networks

The framework of statistical inference has been successfully used to detect the meso-scale structures in complex networks, such as community structure, core-periphery (CP) structure. The main principle is that the stochastic block model (SBM) is used to fit the observed network and the learnt parameters indicate the group assignment, in which the parameters of model are often calculated via an expectation-maximization (EM) algorithm and a belief propagation (BP) algorithm is implemented to calculate the decomposition itself. In the derivation process of the BP algorithm, some approximations were made by omitting the effects of node's neighbors, the approximations do not hold if networks are dense or some nodes holding large degrees. As a result, for example, the BP algorithm cannot well detect CP structure in networks and even yields wrong detection because the nodal degrees in core group are very large. In doing so, we propose an improved BP algorithm to solve the problem in the original BP algorithm without increasing any computational complexity. By comparing the improved BP algorithm with the original BP algorithm on community detection and CP detection, we find that the two algorithms yield the same performance on the community detection when the network is sparse, for the community structure in dense networks or CP structure in networks, our improved BP algorithm is much better and more stable. The improved BP algorithm may help us correctly partition different types of meso-scale structures in networks.

physics.soc-ph↗

arXiv2018

Dynamics of magnetic skyrmion clusters driven by spin-polarized current with a spatially varied polarization

Magnetic skyrmions are promising candidates for future information technology. Here, we present a micromagnetic study of isolated skyrmions and skyrmion clusters in ferromagnetic nanodisks driven by the spin-polarized current with spatially varied polarization. The current-driven skyrmion clusters can be either dynamic steady or static, depending on the spatially varied polarization profile. For the dynamic steady state, the skyrmion cluster moves in a circle in the nanodisk, while for the static state, the skyrmion cluster is static. The frequency of the circular motion of skyrmion is also studied. Furthermore, the dependence of the skyrmion cluster dynamics on the magnetic anisotropy and Dzyaloshinskii-Moriya interaction is investigated. Our results may provide a pathway to realize magnetic skyrmion cluster based devices.

cond-mat.mes-hall↗

arXiv2018

A statistical inference approach to structural reconstruction of complex networks from binary time series

Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of previous works, to fully reconstruct the network structure from observed binary data remains to be challenging. We articulate a statistical inference based approach to this problem. In particular, exploiting the expectation-maximization (EM) algorithm, we develop a method to ascertain the neighbors of any node in the network based solely on binary data, thereby recovering the full topology of the network. A key ingredient of our method is the maximum likelihood estimation of the probabilities associated with actual or non-existent links, and we show that the EM algorithm can distinguish the two kinds of probability values without any ambiguity, insofar as the length of the available binary time series is reasonably long. Our method does not require any {\em a priori} knowledge of the detailed dynamical processes, is parameter free, and is capable of accurate reconstruction even in the presence of noise. We demonstrate the method using combinations of distinct types of binary dynamical processes and network topologies, and provide a physical understanding of the underlying reconstruction mechanism. Our statistical inference based reconstruction method contributes an additional piece to the rapidly expanding "toolbox" of data based reverse engineering of complex networked systems.

physics.soc-ph↗

arXiv2018

A Unified Method of Detecting Core-Periphery Structure and Community Structure in Networks

Core-periphery structure and community structure are two typical meso-scale structures in complex networks. Though the community detection has been extensively investigated from different perspectives, the definition and the detection of core-periphery structure have not received much attention. Furthermore, the detection problems of the core-periphery and community structure were separately investigated. In this paper, we develop a unified framework to simultaneously detect core-periphery structure and community structure in complex networks. Moreover, there are several extra advantages of our algorithm: our method can detect not only single but also multiple pairs of core-periphery structures; the overlapping nodes belonging to different communities can be identified; different scales of core-periphery structures can be detected by adjusting the size of core. The good performance of the method has been validated on synthetic and real complex networks. So we provide a basic framework to detect the two typical meso-scale structures: core-periphery structure and community structure.

physics.soc-ph↗

arXiv2017

Improving link prediction in complex networks by adaptively exploiting multiple structural features of networks

So far, many network-structure-based link prediction methods have been proposed. However, these methods only highlight one or two structural features of networks, and then use the methods to predict missing links in different networks. The performances of these existing methods are not always satisfied in all cases since each network has its unique underlying structural features. In this paper, by analyzing different real networks, we find that the structural features of different networks are remarkably different. In particular, even in the same network, their inner structural features are utterly different. Therefore, more structural features should be considered. However, owing to the remarkably different structural features, the contributions of different features are hard to be given in advance. Inspired by these facts, an \emph{adaptive} fusion model regarding link prediction is proposed to incorporate multiple structural features. In the model, a logistic function combing multiple structural features is defined, then the weight of each feature in the logistic function is \emph{adaptively} determined by exploiting the known structure information. Last, we use the "learnt" logistic function to predict the connection probabilities of missing links. According to our experimental results, we find that the performance of our adaptive fusion model is better than many similarity indices.

physics.soc-ph↗

arXiv2017

Detection of core-periphery structure in networks by 3-tuple motifs

Recently, the core-periphery (CP) structure of networks as one type of meso-scale structure has received attention. The CP structure is composed of a dense core and a sparse connected periphery. In this paper, we propose an algorithm to detect the CP structure based on the 3-tuple motif, which is inspired by the idea of motif. In this algorithm, we first define a 3-tuple motif by considering the property of nodes, and then a motif adjacency matrix is formed based on the defined motif, finally, the detection of the CP structure is converted to find a cluster that minimizes the smallest motif conductance. Our algorithm can detect different CP structures: including single or multiple CP structure; and local or global CP structures. Results in the synthetic and the empirical networks indicate that the method is efficient and can apply to large-scale networks. More importantly, our algorithm is parameter free, where the core and periphery are detected without the need for any predefined parameters.

physics.soc-ph↗

arXiv2016

Identifying influential spreaders in complex networks based on gravity formula

How to identify the influential spreaders in social networks is crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases and rumors, and so on. In this paper, by viewing the k-shell value of each node as its mass and the shortest path distance between two nodes as their distance, then inspired by the idea of the gravity formula, we propose a gravity centrality index to identify the influential spreaders in complex networks. The comparison between the gravity centrality index and some well-known centralities, such as degree centrality, betweenness centrality, closeness centrality, and k-shell centrality, and so forth, indicates that our method can effectively identify the influential spreaders in real networks as well as synthetic networks. We also use the classical Susceptible-Infected-Recovered (SIR) epidemic model to verify the good performance of our method.

physics.soc-ph↗

arXiv2016

Playing the role of weak clique property in link prediction: A friend recommendation model

An important fact in studying the link prediction is that the structural properties of networks have significant impacts on the performance of algorithms. Therefore, how to improve the performance of link prediction with the aid of structural properties of networks is an essential problem. By analyzing many real networks, we find a common structure property: nodes are preferentially linked to the nodes with the weak clique structure (abbreviated as PWCS to simplify descriptions). Based on this PWCS phenomenon, we propose a local friend recommendation (FR) index to facilitate link prediction. Our experiments show that the performance of FR index is generally better than some famous local similarity indices, such as Common Neighbor (CN) index, Adamic-Adar (AA) index and Resource Allocation (RA) index. We then explain why PWCS can give rise to the better performance of FR index in link prediction. Finally, a mixed friend recommendation index (labelled MFR) is proposed by utilizing the PWCS phenomenon, which further improves the accuracy of link prediction.

physics.soc-ph↗