Albert-László Barabási

Bio: Albert-László Barabási is an academic researcher from Northeastern University. The author has contributed to research in topics: Complex network & Network science. The author has an hindex of 152, co-authored 438 publications receiving 200119 citations. Previous affiliations of Albert-László Barabási include Budapest University of Technology and Economics & Lawrence Livermore National Laboratory.

Viva Europa, a Land of Excellence in Research and Innovation for Health and Wellbeing

Abstract: Charles Auffraya; Michael Sagnerb; Sonia Abdelhakc; Ian Adcockd; Alvar Agustie; Margarida Amaralf; Stylianos Antonarakisg; Ross Arenah; Françoise Argouli; Rudi Ballingj; Albert-Laszlo Barabasik; Jacques Beckmannl; Anders Bjartellm; Niklas Blombergn; Thomas Bourgerono; Bertrand Boutronp; Samir Brahmachariq; Christian Bréchotr; Christopher Brightlings; Marta Cascantet; Alfredo Cesariou; Dominique Charronv; Sai-Juan Chenw; Zhu Chenx; Fan Chungy; Karine Clémentz; Ana Conesaaa; Alain Cozzoneab; Menno de Jongac; Jean-François Deleuzead; Jacques Demotesae; Alberto di Meglioaf; Ratko Djukanovicag; Ugur Dogrusozah; Elissa Epelai; Alain Fischeraj; Andrea Gelemanovicak; Carole Gobleal; Takashi Gojoboriam; Michel Goldmanan; Herman Goossensao; François Grosap; Yi-Ke Guoaq; Pierre Hainautar; David Harrisonas; Hans Hoffmannat; Leroy Hoodau; Peter Hunterav; Yves Jacobaw; Hiroaki Kitanoax; Ursula Klingmülleray; Bartha Knoppersaz; Walter Kolchba; Marion Koopmansbb; Doron Lancetbc; Martine Lavillebd; Jean-Marie Lehnbe; Francis Lévibf; Andrey Lisistsabg; Vincent Lotteaubh; Antoine Magnanbi; Bongani Mayosibj; Andres Metspalubk; Yves Moreaubl; James N’Dowbm; Laurent Nicodbn; Denis Noblebo; Maria Manuela Nogueirabp; Anna Norrby-Teglundbq; Laurent Nottalebr; Peter Openshawbs; Mehmet Oztürkbt; Susanna Palkonenbu; Silvio Parodibv; Johann Pelletbw; Ozren Polasekbx; Nathan Priceby; Christian Pristipinobz; Timothy Radstakeca; Martine Raescb; Josep Rocacc; Damjana Rozmancd; Philippe Sabatierce; Shlomo Sassoncf; Bernd Schmeckcg; Ismaïl Serageldinch; Anita Simondsci; Bento Soarescj; Peter Sterkck; Giulio Superti-Furgacl; David Supplecm; Jesper Tegnercn; Mathias Uhlenco; Sylvie van der Werfcp; Pablo Villosladacq; Manlio Vinciguerracr; Vitaly Volpertcs; Steve Webbct; Emiel Wouterscu; Ferran Sanzcv; Francisco Nobregacw

Deterministic Scale-Free Networks

Abstract: Scale-free networks are abundant in nature and society, describing such diverse systems as the world wide web, the web of human sexual contacts, or the chemical network of a cell. All models used to generate a scale-free topology are stochastic, that is they create networks in which the nodes appear to be randomly connected to each other. Here we propose a simple model that generates scale-free networks in a deterministic fashion. We solve exactly the model, showing that the tail of the degree distribution follows a power law.

Network-medicine framework for studying disease trajectories in U.S. veterans

Abstract: A better understanding of the sequential and temporal aspects in which diseases occur in patient's lives is essential for developing improved intervention strategies that reduce burden and increase the quality of health services. Here we present a network-based framework to study disease relationships using Electronic Health Records from > 9 million patients in the United States Veterans Health Administration (VHA) system. We create the Temporal Disease Network, which maps the sequential aspects of disease co-occurrence among patients and demonstrate that network properties reflect clinical aspects of the respective diseases. We use the Temporal Disease Network to identify disease groups that reflect patterns of disease co-occurrence and the flow of patients among diagnoses. Finally, we define a strategy for the identification of trajectories that lead from one disease to another. The framework presented here has the potential to offer new insights for disease treatment and prevention in large health care systems.

Emergence of Scaling in Random Networks

Abstract: 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.

Fast parallel algorithms for short-range molecular dynamics

Abstract: 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.

Data Mining: Concepts and Techniques

Abstract: 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

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

Abstract: 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。

Statistical mechanics of complex networks

Abstract: 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.