Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.

https://arxiv.org/pdf/cond-mat/9910332.pdf

Emergence of Scaling in Random Networks

Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

/pdf/data-mining-concepts-and-techniques-4dtvdfkvmi.pdf

Data Mining: Concepts and Techniques

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. 
Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. 
The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. 
The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

/pdf/statistical-mechanics-of-complex-networks-1zfjpvxipu.pdf

Statistical mechanics of complex networks

Abstract Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret “hairball” layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm’s ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics. 

/pdf/accelerating-network-layouts-using-graph-neural-networks-2c37e3jc.pdf

Accelerating network layouts using graph neural networks

A number of recent experiments have shown that surfactants can modify the growth mode of an epitaxial film, suppressing islanding and promoting layer-by-layer growth. Here a set of coupled equations are introduced to describe the coupling between a growing interface and a thin surfactant layer deposited on the top of the non-equilibrium surface. The equations are derived using the main experimentally backed characteristics of the system and basic symmetry principles. The system is studied using a dynamic-renormalization-group scheme, which provides scaling relations between the roughness exponents. It is found that the surfactant may drive the system into a novel phase, in which the surface roughness is negative, corresponding to a flat surface.

Surfactant-mediated surface growth: nonequilibrium theory

Self-assembled growth of II-VI quantum dots

In the last decade, the science of complex networks [1] experienced a remarkable success story, driven by never-before-seen amounts of data and an ever-increasing interest in understanding complex properties and dynamics. More and more physicists, computer scientists, engineers, mathematicians, biologists, economists and social scientists are tackling similar problems with methods borrowed from each other or, increasingly, developed by teams working across disciplines. The Leonardo satellite symposium Arts | Humanities | Complex Networks, at NetSci2010 (Boston, 10 May 2010), strives to expand and foster cross-disciplinary research on complex networks within, or with the help of, arts and humanities. Up to this point arts and humanities have usually not been included in the list of relevant disciplines featured in the standard network science literature. Given the wealth of arts and humanities data, as well as the growing role of visualization and other perceptualizations in network science, we are sure that lessons can be learned by network scientists as well as specialists in arts and humanities. The study of networks and network visualizations complement each other, as studying the represented always presupposes the study of representation. Network science can help to explore complex structures and dynamics in areas ranging from literature, art and archaeology to music, film and image science. At the same time, specialists from the arts and humanities can help to develop visualizations and other perceptualizations using expertise that draws on a broad historical corpus of works. Beyond that, other collaborative intersections can certainly be identified and explored. The convergence of arts, humanities and network science has the potential to bring new insights and foster knowledge that none of these fields can achieve on their own. We find complex network structure wherever we look in the arts and humanities, including bibliographies, museum inventories and research databases. Every conceivable link relation in these datasets forms a complex network in a larger “network of networks” between objects, people, places, times, events and concepts [2]. Interesting sub-networks in the arts and humanities include multimodal networks of features and metadata in art, film and literature; implicit citation and the transmission of motifs (including Aby Warburg’s Mnemosyne); as well as networks of cultural exchange and trade, from the Neolithic to modern supply chains. Relevant network dynamics include the emergence and evolution of canons in art, music, literature and film, as well as the evolution of communities of practice in art and science. Dealing with the growing role of data visualization, network researchers benefit from cross-disciplinary collaboration. Ten years ago, leading protagonists of network science and information visualization pointed out that, visualizing a complex network, one should be able to reduce it to a simple tree or one should not try it at all, pointing to the alternative of pure numerical measures. Since then, an impressive number of examples, driven by increasing processing power and new layout methods, continue to disprove this opinion. Scientists develop new ways of visualization, such as “edge bundling,” that bring more clarity to complex network structure. Artists have developed convenient visualization tools, such as the Processing programming language, benefitting not only fellow artists but also increasingly the sciences. And humanities researchers are using tools from cell biology such as Cytoscape, visualizing their own complex network data. As a part of NetSci2010, Arts | Humanities | Complex Networks will foster interdisciplinary communication and collaboration, effectively adding arts and humanities to the expanding list of fields associated with complex network research. We will be happy to present the results in a forthcoming issue of Leonardo.

/pdf/arts-humanities-complex-networks-12m533clc6.pdf

Arts | Humanities | Complex Networks

Networks with complex topology describe systems as diverse as the cell, the World Wide Web or the society. The emergence of most networks is driven by self-organizing processes that are governed by simple but generic laws. The analysis of the cellular network of various organisms shows that cells and complex man-made networks, such as the Internet or the world wide web, and many social and collaboration networks share the same large-scale topology. I will show that the scale-free topology of these complex webs have important consequences on their robustness against failures and attacks, with implications on drug design, the Internet's ability to survive attacks and failures, and the ability of ideas and innovations to spread on the network.

Albert-László Barabási

Papers

Accelerating network layouts using graph neural networks

Surfactant-mediated surface growth: nonequilibrium theory

Self-assembled growth of II-VI quantum dots

Arts | Humanities | Complex Networks

The architecture of complexity: the structure and the dynamics of networks, from the web to the cell