Showing papers by "Albert-László Barabási published in 2010"
TL;DR: Analysis of the trajectories of people carrying cell phones reveals that human mobility patterns are highly predictable, and a remarkable lack of variability in predictability is found, which is largely independent of the distance users cover on a regular basis.
Abstract: A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.
3,040 citations
TL;DR: Empirical data is used to show that the predictions of the CTRW models are in systematic conflict with the empirical results, and two principles that govern human trajectories are introduced, allowing for a statistically self-consistent microscopic model for individual human mobility.
Abstract: Individual human trajectories are characterized by fat-tailed distributions of jump sizes and waiting times, suggesting the relevance of continuous-time random-walk (CTRW) models for human mobility. However, human traces are barely random. Given the importance of human mobility, from epidemic modelling to traffic prediction and urban planning, we need quantitative models that can account for the statistical characteristics of individual human trajectories. Here we use empirical data on human mobility, captured by mobile-phone traces, to show that the predictions of the CTRW models are in systematic conflict with the empirical results. We introduce two principles that govern human trajectories, allowing us to build a statistically self-consistent microscopic model for individual human mobility. The model accounts for the empirically observed scaling laws, but also allows us to analytically predict most of the pertinent scaling exponents.
1,174 citations
TL;DR: This work proposes CARE, a Collaborative Assessment and Recommendation Engine, which relies only on patient’s medical history using ICD-9-CM codes in order to predict future disease risks, and describes an Iterative version, ICARE, which incorporates ensemble concepts for improved performance.
Abstract: The monumental cost of health care, especially for chronic disease treatment, is quickly becoming unmanageable. This crisis has motivated the drive towards preventative medicine, where the primary concern is recognizing disease risk and taking action at the earliest signs. However, universal testing is neither time nor cost efficient. We propose CARE, a Collaborative Assessment and Recommendation Engine, which relies only on patient's medical history using ICD-9-CM codes in order to predict future disease risks. CARE uses collaborative filtering methods to predict each patient's greatest disease risks based on their own medical history and that of similar patients. We also describe an Iterative version, ICARE, which incorporates ensemble concepts for improved performance. Also, we apply time-sensitive modifications which make the CARE framework practical for realistic long-term use. These novel systems require no specialized information and provide predictions for medical conditions of all kinds in a single run. We present experimental results on a large Medicare dataset, demonstrating that CARE and ICARE perform well at capturing future disease risks.
154 citations
Journal Article•
TL;DR: In this paper, the authors explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users and find that 93% potential predictability for user mobility across the whole user base.
Abstract: A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.
118 citations
TL;DR: This blueprint deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks and predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability.
Abstract: Advances in genome analysis, network biology, and computational chemistry have the potential to revolutionize drug discovery by combining system-level identification of drug targets with the atomistic modeling of small molecules capable of modulating their activity. To demonstrate the effectiveness of such a discovery pipeline, we deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. This blueprint is applicable for any sequenced organism with high-quality metabolic reconstruction and suggests a general strategy for strain-specific antiinfective therapy.
114 citations
Book•
29 Apr 2010
TL;DR: Barabsi et al. as mentioned in this paper found that human behavior follows predictable laws, such as work and fight and play in short flourishes of activity followed by next to nothing, revealing an astonishing deep order in human actions that makes us far more predictable than we like to think.
Abstract: Can we scientifically predict our future? Scientists and pseudoscientists have been pursuing this mystery for hundreds and perhaps thousands of years. But now, amazing new research is revealing that patterns in human behavior, previously thought to be purely random, follow predictable laws. Albert-Lszl Barabsi, already the world's preeminent researcher on the science of networks, describes his work on this profound mystery in Bursts, a stunningly original investigation into human behavior. His approach relies on the way our lives have become digital. Mobile phones, the Internet, and e-mail have made human activities more accessible to quantitative analysis, turning our society into a huge research laboratory. All those electronic trails of time- stamped texts, voice mails, and searches add up to a previously unavailable massive data set that tracks our movements, our decisions, our lives. Analysis of these trails is offering deep insights into the rhythm of how we do everything. His finding? We work and fight and play in short flourishes of activity followed by next to nothing. Our daily pattern isn't random, it's "bursty." Bursts uncovers an astonishing deep order in our actions that makes us far more predictable than we like to think. Illustrating this revolutionary science, Barabsi artfully weaves together the story of a sixteenth-century burst of human activity-a bloody medieval crusade launched in his homeland, Transylvania-with the modern tale of a contemporary artist hunted by the FBI through our post-9/11 surveillance society. These narratives illustrate how predicting human behavior has long been the obsession, sometimes the duty, of those in power. Barabsi's wide range of examples from seemingly unrelated areas includes how dollar bills move around the United States, the pattern everyone follows in writing e-mail, the spread of epidemics, and even the flight patterns of albatross. In all these phenomena a virtually identical bursty pattern emerges, a reflection of the universality of human behavior. Bursts reveals where individual spontaneity ends and predictability in human behavior begins. The way you think about your own potential to do something truly extraordinary will never be the same.
111 citations
TL;DR: When it comes to the actions of our fellow humans, the sequence of events we witness on a daily basis appears to be just as mysterious and confusing as the motion of the stars seemed in the 15th century as discussed by the authors.
Abstract: When it comes to the actions of our fellow humans, the sequence of events we witness on a daily basis appears to be just as mysterious and confusing as the motion of the stars seemed in the 15th century.
6 citations
Posted Content•
TL;DR: It is found that hybrid MMS viruses including some level of scanning are more dangerous to the mobile community than their standard topological counterparts.
Abstract: The fast growing market for smart phones coupled with their almost continuous online presence makes these devices the new targets of virus writers. It has been recently found that the topological spread of MMS (Multimedia Message Services) viruses is highly restricted by the underlying fragmentation of the call graph. In this paper, we study MMS viruses under another type of spreading behavior: scanning. We find that hybrid MMS viruses including some level of scanning are more dangerous to the mobile community than their standard topological counterparts. However, the effectiveness of both scanning and topological behaviors in MMS viruses can generally be limited by two controlling methods: (i) decreasing susceptible handsets' market share (OS it runs) and (ii) improving monitoring capacity to limit the frequency in which MMS messages can be sent by the mobile viruses.
6 citations
01 Jan 2010
6 citations
TL;DR: The recent Leonardo satellite symposium Arts | Humanities | Complex Networks as discussed by the authors, which was organized at NetSci2010, aimed to expand and foster cross-disciplinary research on complex networks within, or with the help of, arts and humanities.
Abstract: 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.
2 citations