Shi‐Min Cai

University of Electronic Science and Technology of China

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i-10 index

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Tao Zhou

University of Electronic Science and Technology of China

Wei Wang

Chongqing Public Health Medical Center

Zhongqian Fu

Huawei Technologies (Sweden)

Pei-Ling Zhou

Henan University

Ya-Chun Gao

University of Electronic Science and Technology of China

Xuzhen Zhu

Beijing University of Posts and Telecommunications

Ming Tang

Southwest Petroleum University

Zhi-Dan Zhao

Shantou University

Zhao Zhuo

Air Force Engineering University

Xiaolong Chen

Southwestern University of Finance and Economics

Cooperative Institutions

University of Electronic Science and Technology of China

111

University of Science and Technology of China

Chinese Academy of Sciences

Central South University

Beijing University of Posts and Telecommunications

Southeast University

Tsinghua University

Sun Yat-sen University

Zhejiang University

Sichuan University

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Synchronization-based approach for detecting functional activation of brain

Chaos An Interdisciplinary Journal of Nonlinear Science (2012)

Hong Lei Shi‐Min Cai Jie Zhang Zhao Zhuo Zhongqian Fu

In this paper, we investigate a synchronization-based, data-driven clustering approach for the analysis of functional magnetic resonance imaging (fMRI) data, and specifically for detecting functional activation from fMRI data. We first define a new measure of similarity between all pairs of data points (i.e., time series of voxels) integrating both complete phase synchronization and amplitude correlation. These pairwise similarities are taken as the coupling between a set of Kuramoto oscillators, which in turn evolve according to a nearest-neighbor rule. As the network evolves, similar data points naturally synchronize with each other, and distinct clusters will emerge. The clustering behavior of the interaction network of the coupled oscillators, therefore, mirrors the clustering property of the original multiple time series. The clustered regions whose cross-correlation coefficients are much greater than other regions are considered as the functionally activated brain regions. The analysis of fMRI data in auditory and visual areas shows that the recognized brain functional activations are in complete correspondence with those from the general linear model of statistical parametric mapping, but with a significantly lower time complexity. We further compare our results with those from traditional K-means approach, and find that our new clustering approach can distinguish between different response patterns more accurately and efficiently than the K-means approach, and therefore more suitable in detecting functional activation from event-related experimental fMRI data.

Statistical parametric mapping

Phase synchronization

Similarity (geometry)

10.1063/1.4747710

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Citations (11)

Heterogeneous behavioral adoption in multiplex networks

New Journal of Physics (2018)

Xuzhen Zhu Hui Tian Xiaolong Chen Wei Wang Shi‐Min Cai

Heterogeneity is found widely in populations, e.g. different individuals have diverse personalities and a different willingness to accept novel ideas or behaviors. Whereas population heterogeneity is rarely considered in studying the social contagions on complex networks, especially on multiplex networks. To explore the effect of population heterogeneity on the dynamics of social contagions, a novel model based on double-layer multiplex networks is proposed, in which information diffuses synchronously on the two layers, and each layer is assigned with different adoption thresholds. Meanwhile, populations are classified into the activists and conservatives according to their willingness to adopt new behaviors. To qualitatively understand the effect of population heterogeneity on social contagions, a generalized edge-based compartmental theory is proposed. Through rigorous theoretical analysis and extensive simulations, we find the activists in the two layers promote the adoption of behavior. More interestingly, the crossover phenomena in phase transition are found in the growth of the final adopted size when increasing the information transmission rate. When the proportion of activists is relatively large, the phase transition exhibits continuous pattern. Reducing the proportion of activists induces a hybrid transition, in which the final adoption size first grows continuously at the first threshold, and then increases discontinuously at the second threshold. In addition, we find that the network heterogeneity will change the crossover phenomena in phase transition.

Multiplex

Information transmission

10.1088/1367-2630/aaf25d

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Citations (28)

Empirical study on scaling of human behaviors in e-commerce

Acta Physica Sinica (2013)

Dong Yu-Wei Shi‐Min Cai Shang Mingsheng

The detrended fluctuation analysis (DFA) method is used to analyze the human on-line activities in e-commerce. We comprehensively investigate the scale laws of browse and purchase behaviors, which have received little attention before. The time series of browse and purchase behaviors each obviously show a periodical character, and their probability density distributions each have a significant bimodal form. Based on the Fourier transform method, the power spectra of time series indicate that each of them obeys a stochastic process with a long-range self-similar feature (i.e., deviation far from the Poisson process). After identifying and filtering the influence of periodic trend based on power spectra, the detrended fluctuation analysis is used to study the scaling law of time series. Several interesting results can be found that their scaling behaviors on small and large scales show similar values that confirm the long-range correlations rooting in the time series of human on-line activities, and their average scaling exponent approximately equaling 1 suggests that the human online activity may be associated with a self-organized criticality. Although the empirical results are only the observed phenomena like those found in the Internet traffic and stock price fluctuation of financial market, we still think that they may provide an important insight to deeply understand the mechanism of human dynamic behaviors in e-commerce and predict their fluctuation trend for the potential business application.

Hurst exponent

Exponent

Empirical Research

Zipf's law

10.7498/aps.62.028901

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Citations (3)

MP-Based Method on Detecting and Eliminating the Synchronous ECG Artifacts in the EEG Signals

International Conference on Bioinformatics and Biomedical Engineering (2010)

Yanbo Zhou Shi‐Min Cai Tao Zhou Pei-Ling Zhou

A method is proposed to detect and eliminate the synchronous ECG artifacts in the EEG Signals based on the matching pursuit algorithm, which doesn't require the additional synchronous ECG channel or multichannel signals. By using the mixed over-complete dictionary, the EEG signals based on the matching pursuit algorithm are decomposed into atoms. The atoms that have the similar morphological characteristics of R-wave are selected to detect the synchronous ECG R-peak artifacts. Then, the adaptive threshold algorithm is introduced to filter the false detection induced by the morphological characteristics of both ECG artifacts and background EEG signals. At last, the elimination of detected ECG artifacts is realized by the ensemble average subtraction method. By analyzing EEG signals in MIT/BIH database, it presents the excellent result with mean error ratio $1.70\%$.

Subtraction

Artifact (error)

10.1109/icbbe.2010.5516334

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Citations (5)

Enhancing speed of pinning synchronizability: low-degree nodes with high feedback gains

arXiv (Cornell University) (2015)

Mingyang Zhou Zhao Zhuo Hao Liao Zhongqian Fu Shi‐Min Cai

Controlling complex networks is of paramount importance in science and engineering. Despite recent efforts to improve controllability and synchronous strength, little attention has been paid to the speed of pinning synchronizability (rate of convergence in pinning control) and the corresponding pinning node selection. To address this issue, we propose a hypothesis to restrict the control cost, then build a linear matrix inequality related to the speed of pinning controllability. By solving the inequality, we obtain both the speed of pinning controllability and optimal control strength (feedback gains in pinning control) for all nodes. Interestingly, some low-degree nodes are able to achieve large feedback gains, which suggests that they have high influence on controlling system. In addition, when choosing nodes with high feedback gains as pinning nodes, the controlling speed of real systems is remarkably enhanced compared to that of traditional large-degree and large-betweenness selections. Thus, the proposed approach provides a novel way to investigate the speed of pinning controllability and can evoke other effective heuristic pinning node selections for large-scale systems.

Degree (music)

10.48550/arxiv.1511.02012

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Citations (0)

Hierarchical Denoising for Robust Social Recommendation

IEEE Transactions on Knowledge and Data Engineering (2024)

Zheng Hu Satoshi Nakagawa Zhuang Yan Jiawen Deng Shi‐Min Cai

Robustness

10.1109/tkde.2024.3508778

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Multifractal Analysis of Human Heartbeat in Sleep

Chinese Physics Letters (2007)

Ding Liang-Jing Peng Hu Shi‐Min Cai Pei-Ling Zhou

We study the dynamical properties of heart rate variability (HRV) in sleep by analysing the scaling behaviour with the multifractal detrended fluctuation analysis method. It is well known that heart rate is regulated by the interaction of two branches of the autonomic nervous system: the parasympathetic and sympathetic nervous systems. By investigating the multifractal properties of light, deep, rapid-eye-movement (REM) sleep and wake stages, we firstly find an increasing multifractal behaviour during REM sleep which may be caused by augmented sympathetic activities relative to non-REM sleep. In addition, the investigation of long-range correlations of HRV in sleep with second order detrended fluctuation analysis presents irregular phenomena. These findings may be helpful to understand the underlying regulating mechanism of heart rate by autonomic nervous system during wake–sleep transitions.

Multifractal system

Heart beat

Sleep

Sleep Stages

10.1088/0256-307x/24/7/101

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Citations (7)

Hybrid influence of degree and H-index in the link prediction of complex networks

EPL (Europhysics Letters) (2018)

Xuzhen Zhu Wenya Li Hui Tian Shi‐Min Cai

Previous link prediction researchers paid more attention to the delivery ability of paths between two unlinked endpoints, but less to the influences of endpoints. In this letter, we uncover that synthesizing degree and H -index as the hybrid influences of endpoints can more reliably capture such endpoints with great and extensive maximum connected subgraph, which can more possibly attract other unlinked endpoints. In addition, the influence involving small heterogeneity of degree and H -index can further improve the accuracy of link prediction. Based on the hybrid influences of endpoints, we propose link prediction methods to explore the mechanism of link evolution. Extensive experiments on twelve real datasets suggest that the proposed methods can remarkably promote accuracy of link prediction.

Link (geometry)

Degree (music)

10.1209/0295-5075/122/68003

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Citations (12)

Phase diagrams of interacting spreading dynamics in complex networks

Physical Review Research (2020)

Liming Pan Dan Yang Wei Wang Shi‐Min Cai Tao Zhou

The authors develop a theory for coevolving spreading dynamics on complex networks through the approach of dimension reduction for complex networks. The theory predicts phase transitions and the interplay between discontinuous outbreak transition and hysteresis as well as the emergence of tri-critical points

Hysteresis

Dynamics

Complex dynamics

Complex system

Network Dynamics

10.1103/physrevresearch.2.023233

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Citations (14)

Roles of degree, H-index and coreness in link prediction of complex networks

International Journal of Modern Physics B (2018)

Xuzhen Zhu Yujie Yang Lanxi Li Shi‐Min Cai

Link prediction based on topological similarity attracts more and more interests. Traditionally, researchers almost focus on utility of the paths between two unlinked endpoints, but pay little attention to the influence of the endpoints with only degree considered. Through profound investigations, we find, besides of degree, H-index and coreness also can play important roles in link prediction as the influence of endpoint especially in models based on representative SRW which is for the first time introduce influence into link prediction. In this paper, we mainly research degree, H-index and coreness in SRW-based models to explore their roles in accurate link prediction. Extensive experiments on twelve real benchmark datasets suggest that in most cases H-index serves as a better tradeoff in accurate link prediction than either degree or coreness.

Link (geometry)

Degree (music)

Benchmark (surveying)

Similarity (geometry)

10.1142/s0217979218501977

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Citations (8)