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目前Physica A被引用次数最多的10篇论文中属于中国人发的有几篇
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Volume 390, Issue 6, March 2011, Pages 1150-1170
L. L. Linyuan | Tao Zhou
Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms. © 2010 Elsevier B.V. All rights reserved.
Volume 388, Issue 8, April 2009, Pages 1706-1712
Huawei Shen | Xueqi Cheng | Kai Cai | Maobin Hu
Clustering and community structure is crucial for many network systems and the related dynamic processes. It has been shown that communities are usually overlapping and hierarchical. However, previous methods investigate these two properties of community structure separately. This paper proposes an algorithm (EAGLE) to detect both the overlapping and hierarchical properties of complex community structure together. This algorithm deals with the set of maximal cliques and adopts an agglomerative framework. The quality function of modularity is extended to evaluate the goodness of a cover. The examples of application to real world networks give excellent results. © 2008 Elsevier B.V. All rights reserved.
Volume 388, Issues 2-3, January 2009, Pages 193-208
Ke Peng | Yupu Yang
In this paper, we study a leader-following consensus problem for a multi-agent system with a varying-velocity leader and time-varying delays. Here, the interaction graph among the followers is switching and balanced. At first, we propose a neighbor-based rule for every agent to track a leader whose states may not be measured. In addition, we consider the convergence analysis of this multi-agent system under two different conditions: the connection between the followers and the leader is time-invariant and time-varying. For the first case, a novel decomposition method is introduced to facilitate the convergence analysis. By utilizing a Lyapunov-Krasovskii functional, we obtain sufficient conditions for uniformly ultimately boundedness of the tracking errors. Finally, two simulations are also presented to illustrate our theoretical results. © 2008 Elsevier B.V. All rights reserved.
Volume 388, Issue 21, November 2009, Pages 4586-4592
Hongguang Sun | Wen Chen | Yangquan Yangquan Chen
The purpose of this paper is to offer a unified discussion of variable-order differential operators in anomalous diffusion modeling. The characteristics of the new models, in contrast to constant-order fractional diffusion models, change with time, space, concentration or other independent quantities. We introduced a classification of variable-order fractional diffusion models based on the possible physical origins which prompt the variable-order. Some potential applications of the variable-order fractional diffusion models are also discussed. © 2009 Elsevier B.V. All rights reserved.
Volume 389, Issue 1, January 2010, Pages 179-186
Zike Zhang | Tao Zhou | Yi Zhang
Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks accessorial information, such as user profiles, item attributes and explicit ratings. Collaborative tags contain rich information about personalized preferences and item contents, and are therefore potential to help in providing better recommendations. In this article, we propose a recommendation algorithm based on an integrated diffusion on user-item-tag tripartite graphs. We use three benchmark data sets, Del.icio.us, MovieLens and BibSonomy, to evaluate our algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and novelty of recommendations. © 2009 Elsevier B.V. All rights reserved.
Volume 388, Issue 8, April 2009, Pages 1571-1576
Didier Sornette | Ryan Woodard | Weixing Zhou
We present an analysis of oil prices in USD and in other major currencies that diagnoses unsustainable faster-than-exponential behavior. This supports the hypothesis that the recent oil price run-up was amplified by speculative behavior of the type found during a bubble-like expansion. We also attempt to unravel the information hidden in the oil supply-demand data reported by two leading agencies, the US Energy Information Administration (EIA) and the International Energy Agency (IEA). We suggest that the found increasing discrepancy between the EIA and IEA figures provides a measure of the estimation errors. Rather than a clear transition to a supply restricted regime, we interpret the discrepancy between the IEA and EIA as a signature of uncertainty, and there is no better fuel than uncertainty to promote speculation! Our post-crash analysis confirms that the oil peak in July 2008 occurred within the expected 80% confidence interval predicted with data available in our pre-crash analysis. © 2009.
Volume 389, Issue 12, June 2010, Pages 2434-2442
Sara Dadras | Hamid Reza Momeni
This paper deals with designing a sliding mode controller (SMC) for a fractional-order chaotic financial system. Using the sliding mode control technique, a sliding surface is determined. The sliding mode control law is derived to make the states of the fractional-order financial system asymptotically stable. The designed control scheme is robust against the system's uncertainty and guarantees the property of asymptotical stability in the presence of an external disturbance. An illustrative simulation result is given to demonstrate the effectiveness of the proposed sliding mode control design. © 2010 Elsevier B.V. All rights reserved.
Volume 388, Issue 17, September 2009, Pages 3377-3383
Anuar Ishak | Khamisah Jafar | Roslinda Mohd Nazar | Ioan Aurel Pop
The steady two-dimensional MHD stagnation point flow towards a stretching sheet with variable surface temperature is investigated. The governing system of partial differential equations are transformed into ordinary differential equations, which are then solved numerically using a finite-difference scheme known as the Keller-box method. The effects of the governing parameters on the flow field and heat transfer characteristics are obtained and discussed. It is found that the heat transfer rate at the surface increases with the magnetic parameter when the free stream velocity exceeds the stretching velocity, i.e. ε > 1, and the opposite is observed when ε < 1. © 2009 Elsevier B.V.
Volume 389, Issue 16, August 2010, Pages 3299-3306
Qingyun Wang | Matjaž Perc | Zhisheng Duan | Guanrong Chen
We study synchronization transitions and pattern formation on small-world networks consisting of Morris-Lecar excitable neurons in dependence on the information transmission delay and the rewiring probability. In addition, networks formed via gap junctional connections and coupling via chemical synapses are considered separately. For gap-junctionally coupled networks we show that short delays can induce zigzag fronts of excitations, whereas long delays can further detriment synchronization due to a dynamic clustering anti-phase synchronization transition. For the synaptically coupled networks, on the other hand, we find that the clustering anti-phase synchronization can appear as a direct consequence of the prolongation of information transmission delay, without being accompanied by zigzag excitatory fronts. Irrespective of the coupling type, however, we show that an appropriate small-world topology can always restore synchronized activity if only the information transmission delays are short or moderate at most. Long information transmission delays always evoke anti-phase synchronization and clustering, in which case the fine-tuning of the network topology fails to restore the synchronization of neuronal activity. © 2010 Elsevier B.V. All rights reserved.
Volume 388, Issue 14, July 2009, Pages 2956-2964
Weiqiang Huang | Xintian Zhuang | Shuang Yao
In many practical important cases, a massive dataset can be represented as a very large network with certain attributes associated with its vertices and edges. Stock markets generate huge amounts of data, which can be use for constructing the network reflecting the market's behavior. In this paper, we use a threshold method to construct China's stock correlation network and then study the network's structural properties and topological stability. We conduct a statistical analysis of this network and show that it follows a power-law model. We also detect components, cliques and independent sets in this network. These analyses allows one to apply a new data mining technique of classifying financial instruments based on stock price data, which provides a deeper insight into the internal structure of the stock market. Moreover, we test the topological stability of this network and find that it displays a topological robustness against random vertex failures, but it is also fragile to intentional attacks. Such a network stability property would be also useful for portfolio investment and risk management. © 2009 Elsevier B.V. All rights reserved.
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