Structure properties and synchronizability of cobweb-like networks Physica A 387 (2008) 6646–6656.pdf The cobweb-like network was constructed, and its structure properties and synchronizability were studied by numerical simulation and probability theory. For cobweb-like networks, the behaviors of structure properties with the adding probability are similar to those for the small-world network. However, the properties of cobweb-like networks also distinctly depend on the spoke–ring ratio r. With increasing r, the average path length decreases, and the average clustering coefficient increases. Simultaneously, the connectivity distribution becomes heterogeneous. Moreover, in the case of bounded synchronized region, the synchronizability shows complicated behaviors with r, and is enhanced favorably at r = 2.28with homogeneous load distribution. The reason is that homogeneous load distribution has an advantage over the communication between oscillators, which efficiently leads to global synchronization. This work could be useful for design and kinetic property research of cobweb-like networks. 真菌生长网络 北京市交通网络 辐弦比在2.28附近时同步能力最强 辐弦结构小世界性示意图
Bayesian Networks :贝叶斯信念网络或信念网络,主要用于概率推理及决策 书籍： Neapolitan, R. E. Learning Bayesian Networks . Pearson Prentice Hall, 2004 Russell, S. and Norvig, P. Artificial Intelligence: A Modern Approach , 2nd ed. Prentice Hall, 2003 Korb, K. B. and Nicholson, A. E. Bayesian Artificial Intelligence . Chapman and Hall/CRC, 2004 Charniak, Eugene “ Bayesian Networks without Tears ”, AI Magazine, 12(4), Winter 91, 50-63 会议： Conference in Uncertainty in Artificial Intelligence (UAI)： www.auai.org American Association for Artificial Intelligence Conference (AAAI)： www.aaai.org International Joint Conference on Artificial Intelligence (IJCAI)： www.ijcai.org Neural Information Processing Systems Conference (NIPS)： www.nips.cc （神经计算方面最好会议之一,） Kevin Murphy （cowman） tutorial on Graphical models and Bayesian networks： http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html 工具： 国内好书： 茆诗松 《贝叶斯统计》: 讲贝叶斯方法最通俗易懂的书 张连文 《贝叶斯网引论》
师弟王延博士来组讨论，谈及Wen-Xu Wang等新近之作。 PRL 106, 154101 (2011) Predicting Catastrophes in Nonlinear Dynamical Systems by Compressive Sensing Abstract:An extremely challenging problem of significant interest is to predict catastrophes in advance of their occurrences. We present a general approach to predicting catastrophes in nonlinear dynamical systems under the assumption that the system equations are completely unknown and only time series reflecting the evolution of the dynamical variables of the system are available. Our idea is to expand the vector field or map of the underlying system into a suitable function series and then to use the compressive-sensing technique to accurately estimate the various terms in the expansion. Examples using paradigmatic chaotic systems are provided to demonstrate our idea. 另一篇文章：EPL, 94 (2011) 48006 Time-series–based prediction of complex oscillator networks via compressive sensing Abstract – Complex dynamical networks consisting of a large number of interacting units are ubiquitous in nature and society. There are situations where the interactions in a network of interest are unknown and one wishes to reconstruct the full topology of the network through measured time series. We present a general method based on compressive sensing. In particular, by using power series expansions to arbitrary order, we demonstrate that the network-reconstruction problem can be casted into the form X=G· a, where the vector X and matrix G are determined by the time series and a is a sparse vector to be estimated that contains all nonzero power series coefficients in the mathematical functions of all existing couplings among the nodes. Since a is sparse, it can be solved by the standard L1-norm technique in compressive sensing. The main advantages of our approach include sparse data requirement and broad applicability to a variety of complex networked dynamical systems, and these are illustrated by concrete examples of model and real-world complex networks. 除了其核心部分，其他粗粗地看了一下，有如下感觉： 1.作者善为文； 2.问遍身边网上的朋友也不知道compressive sensing如何翻译成中文，但关于这个方法的应用和原理的文章很多可见 http://dsp.rice.edu/cs ； 3.问题：如果有一个网络，并非全部结点的时间序列都知道，那么对于已知的结点和时序，用所给出的方法的准确性会有怎么样的变化。如果将“遗漏”信息作为“噪声”，可想而知的是“Our method is robust against noise, due to the optimization nature of the compressive-sensing method.”但从抗噪声的工作来看，一般都是弱噪声才成。所以“遗漏”不能太多。 4. 在非线性化学实验中，如果将关心的反应物或中间产物的时间序列测量得到，那么相应的化学反应速率常数完全可以用这种方法给出。不知道以前组内师弟(妹)们是如何估计这些常数的。 5. 多讨论才能知道更多的相关信息。
Controllability of complex networks .pdf ARTICLE doi:10.1038/nature10011 Controllability of complex networks Yang-Yu Liu1,2, Jean-Jacques Slotine3,4 Albert-Laszlo Barabasi1,2,5 1:Center for Complex Network Research and Departments of Physics, Computer Science and Biology, Northeastern University, Boston, Massachusetts 02115, USA. 2:Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. 3:Nonlinear Systems Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. 4:Department of Mechanical Engineering and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139,USA. 5Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. 1, 2: M AY 2 0 1 1 | VO L 4 7 3 | N AT U R E | 1 6 7 2011 Macmillan The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them.Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, aframework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system’s entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network’s degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes.
The Fourth Week Schedule Interdisciplinary Applications of Statistical Physics Complex Networks (KITPC/ITP-CAS, Feb 28 - Apr 1, 2011) Week 4, Mar 21 – Mar 25, 2011 Monday 21 / 3 /201 1 Room: 6620 Chairperson: Bing-Hong Wang 10:00-11:00 Chin-Kun Hu （ Institute of Physics, Academia Sinica ， Taipei ） Some recent results for complex networks of nonlinear and biological systems' 11:00-11:30 Discussion Lunch Break Room: 6620 Chairperson: Tao Zhou 15:00-16:00 Bin Jiang (University of G?vle, Dept of Technology and Built Environment, Sweden) Scaling of geographic space with data-intensive geospatial computing. 16:00-16:20 Break 16:20-17:00 Jing Zhao (Department of Mathematics, Logistical Engineering University) Network based TCM pharmacology Tuesday 22 / 3 /201 1 Room: 6620 Chairperson: Bing-Hong Wang 10:00-11:00 Sumiyoshi Abe (Department of Physical Engineering, Mie University ) Aftershocks 11:00-11:30 Discussion Lunch Break Room: 6620 Chairperson: Tao Zhou 15:00-15:50 Renaud Lambiotte (Imperial College London; Institute for Mathematical Sciences ) Beyond Space For Spatial Networks 15:50-16:10 Break 16:10-17:00 Wen-Xu Wang (Arisona University, USA) TBA Wednesday 23 / 3 /201 1 Room: 6620 Chairperson: Bing-Hong Wang 10:00-11:00 Hyunggyu Park (School of Physics, Korea Institute for Advanced Study, Korea) Phase transitions on networks: Annealed verses quenched 11:00-11:30 Discussion Lunch Break Room: 6620 Chairperson: Tao Zhou 15:00-16:00 Renaud Lambiotte (Imperial College London; Institute for Mathematical Sciences) Multi-scale Modularity in Complex Networks 16:00-16:20 Break 16:20-17:00 Yueheng Lan (Department of Physics, Tsinghua University) Novel computation of the growth rate of generalized random Fibonacci sequences Thursday 24 / 3 /201 1 Room: 6620 Chairperson: Bing-Hong Wang 10:00-11:00 Beom Jun Kim (Department of Physics, Sungkyunkwan University, Korea) Synchronization in two coupled networks 11:00-11:30 Discussion Lunch Break Room: 6620 Chairperson: Tao Zhou 15:00-16:00 Renaud Lambiotte (Imperial College London; Institute for Mathematical Sciences) Multirelational Organization of Large-scale Social Networks in an Online World 16:00-16:30 Lin-Yun Lv ( ) TBA Friday 25 / 3 /201 1 Room: 6620 Chairperson: Bing-Hong Wang 10:00-11:00 Jae Don Noh (Department of Physics, University of Seoul, Korea ) Scaling behavior of random walk centrality in general graphs 11:00-11:30 Discussion Lunch Break Room: 6620 Chairperson: Tao Zhou 15:00-16:00
VLDB2010的文章，今天才看到，看来还需要在努力些呀~~~~~~~ Personalized Privacy Protection in Social Networks（http://www.vldb.org/pvldb/vol4/p141-yuan.pdf） 在之前的Social Networks隐私保护中，都是假设攻击者具有相同的背景知识。这篇paper的作者根据facebook中的用户隐私保护策略，提出不同的用户可以规定自己隐私的保护级别。论文中一共提出了3个隐私级别： 1.攻击者只知道发布图中节点u的label信息； 2.攻击者不仅知道发布图中节点u的label信息，而且还知道u的degree信息； 3.攻击者不仅知道发布图中节点u的label信息，u的degree信息。而且还知道u的边上的label信息。 论文摘要： Due to the popularity of social networks, many proposals have been proposed to protect the privacy of the networks. All these works assume that the attacks use the same background knowledge. However, in practice, different users have different privacy protect requirements. Thus, assuming the attacks with the same background knowledge does not meet the personalized privacy requirements, meanwhile, it looses the chance to achieve better utility by taking advantage of differences of users’ privacy requirements. In this paper, we introduce a framework which provides privacy preserving services based on the user’s personal privacy requests. Specifically, we define three levels of protection requirements based on the gradually increasing attacker’s background knowledge and combine the label generalization protection and the structure protection techniques (i.e. adding noise edge or nodes) together to satisfy different users’ protection requirements. We verify the effectiveness of the framework through extensive experiments.
Call for Papers 2011 International Symposium on Neural Networks May 29 - June 1, 2011, Guilin, China http://isnn2011.mae.cuhk.edu.hk/ Important dates: Special session proposals due: November 1, 2010 Paper submission deadline: December 1, 2010 Notification of acceptance: January 1, 2011 The 8th International Symposium on Neural Networks (ISNN 2011) will be held in Guilin, China, as a sequel of ISNN 2004–2010. Guilin has a long history as the most beautiful place in China. It is famous for its wonderful scenery, rivers and karst peaks covered in lush greenery. ISNN 2011 aims to provide a high-level international forum for scientists, engineers, and educators to present the state of the art of neural network research and applications in diverse fields. The symposium will feature plenary lectures given by world renowned scholars, regular sessions with broad coverage, and some special sessions focusing on popular topics. Prospective authors are invited to submit full-length papers (6-8 pages normally and 10 pages maximum) by the submission deadline through the online submission system. All papers accepted by and presented at ISNN2011 will be published by Springer as multiple volumes of Lecture Notes in Computer Science which will be indexed by EI and ISTP. Selected papers will be published in special issues of several SCI journals. ISNN 2011 paper submission website is already open: http://isnn2011.mae.cuhk.edu.hk/econfpal/author/ Potential organizers are invited to enlist five or more papers with cohesive topics to form special sessions. Please contact Special Sessions Chair "Jinhu Lu" jhlu@iss.ac.cn for more details if you would like to organize special sessions.
Web networks of the science system:Weighted hubs and authorities MARIANNE HRLESBERGER, EDGAR SCHIEBEL Technology Management, ARC system research GmbH, Seibersdorf (Austria) Visualization with the algorithm of BibTechMon provides the out-degree as well as the indegree. The analysis shows that both frequency and co-occurrences of objects (nodes in the network) support the idea of Kleinbergs algorithm. The analysis of the algorithm shows clearly that strongly linked scores lead the iteration to a convergence and give the highest weights. Therefore BibTechMon visualizes the results well. Introduction The measure of visibility on the Web is the number of links. A website gets more visible for a visitor when it is linked by other webpages and links to other webpages.The emphasis in this contribution lies in calculating hubs weights and authorities weights AND visualizing the results in networks calculated via BibTechMon (Bibliometric Technology Monitoring, Kopcsa, A., Schiebel, E. (1998). In particular, Kleinbergs algorithm2 is applied to a special dataset and we try to find an answer to the following question: In which way does BibTechMon support the idea of Kleinbergs algorithm? Although Kleinbergs algorithm and BibTechMon are not the latest research results it is the first time they are brought together successfully. The analysed dataset is a set of URLs in biotechnology of German universities and research institutes of 2002 (see more in chapter Data). This analysis tries to answer the question: Which are the relevant websites of the considered dataset? Some background Data Methods Results Conclusions Visualization with the algorithm of BibTechMon provides the out-degree as well as the in-degree. The analysis shows that both frequency and co-occurrences of objects (nodes in the network) support the idea of Kleinbergs algorithm. The analysis of the algorithm shows clearly that strongly linked scores lead the iteration to a convergence and give the highest weights. Therefore BibTechMon visualizes the results well. Scientists and evaluators are confronted with an increasing amount of data to be analysed. Combing the calculation of the indicator with the visualisation of the data considered helps to get a new approach to the analysis. The pattern of the networks combined with the calculated weight like hubs and authorities widens the perception and gives new ideas of connections. BibTechMon works with not directed graphs. So you need several networks to explain the whole graph of a considered dataset. For some research questions it could be of interest to extend the method so it can visualize directed graphs and offers possibilities of analysing them. Today the different algorithms such as PageRank, Kleinberg, WebQuery are controversial because they might be outdated as the World Wide Web is changing so quickly. So scientists are challenged to go ahead with their research and keep with the rapid development. 来源：Scientometrics, Vol. 66, No. 2 (2006) 263278
Cluster synchronization in networks of coupled nonidentical dynamical systems (Chaos 20,01320,2010) Wenlian Lu, 1, a _ Bo Liu, 2, b _ and Tianping Chen 2, c _ 1 Centre for Computational Systems Biology, Fudan University , Shanghai 200433, Peoples Republic of China 2 School of Mathematical Sciences, Fudan University , Shanghai 200433, Peoples Republic of China and Shanghai Key Laboratory for Contemporary Applied Mathematics, Fudan University , Shanghai 200433, Peoples Republic of China 按语: 我把陈天平小组三位合作的文章推荐到这个专栏,以推动博文比赛,提供大家参考. （方锦清2010、3、27） _ Received 11 July 2009; accepted 28 January 2010; published online 17 March 2010 _ In this paper, we study cluster synchronization in networks of coupled nonidentical dynamical systems. The vertices in the same cluster have the same dynamics of uncoupled node system but the uncoupled node systems in different clusters are different. We present conditions guaranteeing cluster synchronization and investigate the relation between cluster synchronization and the unweighted graph topology. We indicate that two conditions play key roles for cluster synchronization: the common intercluster coupling condition and the intracluster communication. From the latter one, we interpret the two cluster synchronization schemes by whether the edges of communication paths lie in inter- or intracluster. By this way, we classify clusters according to whether the communications between pairs of vertices in the same cluster still hold if the set of edges inter- or intracluster edges is removed. Also, we propose adaptive feedback algorithms to adapting the weights of the underlying graph, which can synchronize any bi-directed networks satisfying the conditions of common intercluster coupling and intracluster communication. We also give several numerical examples to illustrate the theoretical results. 2010 American Institute of Physics . _ doi: 10.1063/1.3329367 _ Cluster synchronization is considered to be more momentous than complete synchronization in brain science and engineering control, ecological science and communication engineering, and social science and distributed computation. Most of the existing works only focused on networks with either special topologies such as regular lattices or coupled two/three groups. For the general coupled dynamical systems, theoretical analysis to clarify the relationship between the (unweighted) graph topology and the cluster scheme, including both self-organization and driving, is absent. In this paper, we study this topic and find two essential conditions for an unweighted graph topology to realize cluster synchronization: the common intercluster coupling condition and the intracluster communication. Thus under these conditions, we present two manners of weighting to achieve cluster synchronization. One is adding positive weights on each edge with keeping the invariance of the cluster synchronization manifold and the other is an adaptive feedback weighting algorithm. We prove the availability of each manner. From these results, we give an interpretation of the two clustering synchronization schemes via the communication between pairs of individuals in the same cluster. Thus, we present one way to classify the clusters via whether the set of inter- or intracluster edges is removable if still wanting to keep the communication between vertices in the same cluster. Cluster Synchronization of.coupled nonidentical dynamical systems
PRL 98, 034101 (2007) Abstract:The understanding of emergent collective phenomena in natural and social systems has driven the interest of scientists from different disciplines during decades. Among these phenomena, the synchronization of a set of interacting individuals or units has been intensively studied because of its ubiquity in the natural world. In this Letter, we show how for fixed coupling strengths local patterns of synchronization emerge differently in homogeneous and heterogeneous complex networks, driving the process towards a certain global synchronization degree following different paths. The dependence of the dynamics on the coupling strength and on the topology is unveiled. This study provides a new perspective and tools to understand this emerging phenomena. 这篇文献，我没有仔细地阅读，但也有粗略的观感如下： 1.Kuramoto 模型，研究了ER和SF两种拓扑结构的网络，在由完全非同步向完全同步转化过程中的差异。这种转化过程是发生时，网络拓扑结构是静态的，但耦合的强度是改变的。据了解，这个思路也不是第一次出现在这篇文章中。 2.利用KM模型的相对简单特性，给出了 关于全局和局域同步范围的量度r，rlink。这样的思路，如何体现在一个具体一些极限环模型中呢？即在其他模型中如何定义这两个量度？如果说KM就抽象可以代表所有极限环振子系统，那么对于相位不是那么方便定义的混沌振子系统，如Lronz系统，这种方法还如何办呢？ 3.In the presence of hubs, a giant component of synchronized pairs of oscillators forms and grows by recruiting nodes linked to them.这说明了了hubs结点在同步过程中的作用。 4.我感兴趣的是给定一个耦合强度，如果在完全非同步的初始状态下，向可能的同步状态演化过程中hubs结点的作用是不是与本文所述的一样呢？
Chaos 16, 015104 (2006) Changsong Zhou and Jurgen Kurths 这是一篇我看到的极出色的文章，所研究的复杂网络上的分层同步（我就直译了）问题对识别网络的拓扑结构具有启发性。PRE80，016116（2009）也正是基于此，明确提出了用来网络探测。在没有看到更早的文献之前，我先当它是利用动力学来探测网络度分布的第一文了。如此说，EPL82，68001（2008）可能要觉得有点冤，因为那里宣称是首次。但在仔细阅读这三文章之后，我还是认为这里闪烁的原创性更明亮！ 文章的摘要：We study synchronization behavior in networks of coupled chaotic oscillators with heterogeneous connection degrees. Our focus is on regimes away from the complete synchronization state, when the coupling is not strong enough, when the oscillators are under the influence of noise or when the oscillators are nonidentical. We have found a hierarchical organization of the synchronization behavior with respect to the collective dynamics of the network. Oscillators with more connections (hubs) are synchronized more closely by the collective dynamics and constitute the dynamical core of the network. The numerical observation of this hierarchical synchronization is supported with an analysis based on a mean field approximation and the master stability function. 1.在简介里隐约给出了复杂网络的研究对象主要就是指联接，或称为复杂的拓扑结构。而对于结点的研究则是动力学的范围。所以是否可以将我们研究的对象分为这么两层次：复杂系统是第一层、复杂网络与动力系统（微分动力系统）是第二层，这一层是拓扑结构与单一结点动力学。在第二层次上的单独研究都已经开展得很多了，而第一层的研究则包括常见的什么传播动力学、网络同步等。 2.MSF分析的是网络的完全同步（CS），但是网络的最自然状态往往是非完全同步的。在这种情况下，局部群体行为的分析也是十分令人感兴趣的。这对应以往斑图研究中的发达湍流与全局同步运动（规则斑图之一）之间的状态。当然以前斑图研究的也可以说成是简单连接的网络上的动力学研究。 3.Interestingly, the stability analysis of the CS state can be adopted to provide an understanding of the hierarchical synchronization.这是在简介结尾时说的一句话，可能是对应II（E）部分的，平均场分析。因为这一部分的分析方法与MSF相似。不同的是normalized耦合强度是结点度的显函数，且由之可知度大的结点耦合强度也大。所以可想而知的是有权连接对所讨论的对象也有很大的影响。 4.文章最后一句Our present interest is on self-organization of structures and dynamics due to the interplay between them令人浮想连翩。 5.总体还有个感觉就是工作量很大，做得很细，值得学习。
这是一篇PRE 80， 030624（2009）的文章，作者为Liang Huang等。 摘要：Master-stability functions （MSFs） are fundamental to the study of synchronization in complex dynamical systems. For example, for a coupled oscillator network, a necessary condition for synchronization to occur is that the MSF at the corresponding normalized coupling parameters be negative. To understand the typical behaviors of the MSF for various chaotic oscillators is key to predicting the collective dynamics of a network of these oscillators. We address this issue by examining, systematically, MSFs for known chaotic oscillators. Our computations and analysis indicate that it is generic for MSFs being negative in a finite interval of a normalized coupling parameter. A general scheme is proposed to classify the typical behaviors of MSFs into four categories. These results are verified by direct simulations of synchronous dynamics on networks of actual coupled oscillators. 1. The MSF can be obtained independent of the topoloty of the underlying network that supports a large number of such oscillators.这是否意味着，我们没办法用MSF方法来讨论具体一个网络中，某些特定的点对稳定同步的作用呢？如度数最大的点，n个点构成的团簇等子网络等。 2.A necessary condition ofr synchronization to occur is that the MSF be negative and the corresponding normalized coupling parameters fall in the negative region of the MSf. 这将意味着存在某类动力系统，其特定拓扑结构的网络，用MSF分析可以完全同步，但实际上却不可能同步。这篇文章说的都是成功的例子，可曾有实例反倒？ 3.由normalized parameter引起的：正则（canonical）、正规(normal，regular)、重整(renormal)、归一之间的关系问题。汉语对应翻译是不是有点乱？ 4. For nonlinear oscillators, the Jacobian matrix DF typically depends on the trajectory S(t). 这里没有指出同步解S（t）是唯的，所以当非线性振子是多稳振子时，MSF应用时会是怎么样的呢？这可不可以作为问题2的一个回答？因为可能对一个稳态是可同步的，另一个稳态是不可同步的。如果两个态都是稳定同步态，系统最终选择哪个稳定性也要取决于初始条件。 5.The Turing bifurcation 看到这个词颇感亲切，当年做复杂系统中的斑图嘛！但发现图3的最后一个图，可能是作者没画全吧？当K进一步增大时Phsi要小于0才行呢。（从表中可知，的确如此） 6.synchronization 在这里指完全同步，而且说一个系统是不是同步的，指的是非完全同步状态作为初始状态，或完全同步状态下给一定的扰动后，系统还能不能再同步。那么如Rossler等方便定义相位的系统，其在一定情况下出现的相同步能不能用MSF方法来讨论？ 7.因为最大李指数与单一动力系统的动力学、normalized参数有关，那在动力系统与耦合强度都确定的情况下，是否存在不同类型的拓扑结构，会使得处于临界状态的网络由可同步切换到不可同步，或相反呢？ 8. 第四部分的讨论， 是否可以用数值模拟进行验证？
Complex09 Call For Participation Welcome to Complex2009 The First International Conference on Complex Sciences: Theory and Applications 发件人Xiao Shi XiaoShi@ntu.edu.sg 回复Xiao Shi XiaoShi@ntu.edu.sg 发送至 COMPLEX_2009@mlist.ntu.edu.sg 日期2009年2月17日 下午7:03 主题Complex09 Call For Participation 邮送域mlist.ntu.edu.sg 隐藏详细信息 2月17日 (3天前) 回复 The interdisciplinary studies on complex systems have gained extensive research interests. Significant impacts have been made by such studies on a wide range of different areas including physics, biology, economics, social sciences, etc., and penetrating into various engineering applications. In the long-term future, the way we understand and cope with the world may all be revolutionized by such studies. Complex'2009, the First International Conference on Complex Sciences: Theory and Applications, aims to provide a unique and convenient platform for people working on theory and applications of complex systems to exchange their ideas and their latest research results. Topics of interests address, but not limited to, the following areas: - Structure and Dynamics of Complex Networks - Complex Biological Systems - Econophysics - Sociophysics - Complex Systems Methods - Complex Systems in Engineering We look forward to your participation in COMPLEX'2009 to make this conference a success. CONFERENCE DATE AND LOCATION February 23-25, 2009, Shanghai, China CONFERENCE PROGRAM The detailed conference program is available at http://www.complex-sys.org/ . The conference features: - Seventeen technical sessions consisting of more than 190 high-quality technical papers - Five collated workshops. - Four Distinguished Keynote Speakers and Fifteen Invited Speakers. COLLATED WORKSHOPS Causality in Complex Systems (ComplexCCS) http://www.complexsystems.net.au/wiki/Complex_%2709_Workshop_on_Causalit y_in_Complex_Systems Complex Engineering Networks (ComplexEN) http://complexen.icstweb.org/ Complexity Theory of Arts and Music (COART) http://coart.icstweb.org/ Modelling and Analysis of Human Dynamics (MANDYN) http://pil.phys.uniroma1.it/~gcalda/Complex2009Satellite/ Social Physics and its Applications (SPA) http://socialphysics.ac.cn/NewsView.Asp?id=46 For further information and registration: http://www.complex-sys.org/ 回复 全部回复 转发
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