Showing papers by "Albert-László Barabási published in 2006"

Modeling bursts and heavy tails in human dynamics.

Abstract: terized by bursts of rapidly occurring events separated by long periods of inactivity. We show that the bursty nature of human behavior is a consequence of a decision based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, most tasks being rapidly executed, while a few experiencing very long waiting times. In contrast, priority blind execution is well approximated by uniform interevent statistics. We discuss two queuing models that capture human activity. The first model assumes that there are no limitations on the number of tasks an individual can hadle at any time, predicting that the waiting time of the individual tasks follow a heavy tailed distribution Pw w with =3/2. The second model imposes limitations on the queue length, resulting in a heavy tailed waiting time distribution characterized by = 1. We provide empirical evidence supporting the relevance of these two models to human activity patterns, showing that while emails, web browsing and library visitation display = 1, the surface mail based communication belongs to the =3/2 universality class. Finally, we discuss possible extension of the proposed queuing models and outline some future challenges in exploring the statistical mechanics of human dynamics.

Burstiness and Memory in Complex Systems

Abstract: The dynamics of a wide range of real systems, from email patterns to earthquakes, display a bursty, intermittent nature, characterized by short timeframes of intensive activity followed by long times of no or reduced activity. The understanding of the origin of such bursty patterns is hindered by the lack of tools to compare different systems using a common framework. We introduce two measures to distinguish the mechanisms responsible for the bursty nature of real signals, changes in the interevent times and memory. We find that while the burstiness of natural phenomena is rooted in both the interevent time distribution and memory, for human dynamics memory is weak, and the bursty character is due to changes in the interevent time distribution. Finally, we show that current models lack in their ability to reproduce the activity pattern observed in real systems, opening up new avenues for future work.

Dynamics of information access on the web.

Abstract: different, the skeleton acquiring visits at a constant rate, while a news document’s visitation peaks after a few hours. We find that the visitation pattern of a news document decays as a power law, in contrast with the exponential prediction provided by simple models of site visitation. This is rooted in the inhomogeneous nature of the browsing pattern characterizing individual users: the time interval between consecutive visits by the same user to the site follows a power-law distribution, in contrast to the exponential expected for Poisson processes. We show that the exponent characterizing the individual user’s browsing patterns determines the power-law decay in a document’s visitation. Finally, our results document the fleeting quality of news and events: while fifteen minutes of fame is still an exaggeration in the online media, we find that access to most news items significantly decays after 36 hours of posting.

The Architecture of Biological Networks

Abstract: An ambitious goal of contemporary biological research is the elucidation of the structure and functions of networks that constitute cells and organisms. In biological systems, networks appear in many different disguises, ranging from protein interactions to metabolic networks. The emergence of these networks is driven by self-organizing processes that are governed by simple but generic laws. While unraveling the complex and interwoven systems of different interacting units, it has become clear that the topology of networks of different biological origin share the same characteristics on the large scale. In this chapter, we survey the most prominent characteristics of biological networks, focusing on the emergence of the scale-free architecture and the hierarchical arrangement of modules.

Stable evolutionary signal in a Yeast protein interaction network

Abstract: The recently emerged protein interaction network paradigm can provide novel and important insights into the innerworkings of a cell. Yet, the heavy burden of both false positive and false negative protein-protein interaction data casts doubt on the broader usefulness of these interaction sets. Approaches focusing on one-protein-at-a-time have been powerfully employed to demonstrate the high degree of conservation of proteins participating in numerous interactions; here, we expand his 'node' focused paradigm to investigate the relative persistence of 'link' based evolutionary signals in a protein interaction network of S. cerevisiae and point out the value of this relatively untapped source of information. The trend for highly connected proteins to be preferably conserved in evolution is stable, even in the context of tremendous noise in the underlying protein interactions as well as in the assignment of orthology among five higher eukaryotes. We find that local clustering around interactions correlates with preferred evolutionary conservation of the participating proteins; furthermore the correlation between high local clustering and evolutionary conservation is accompanied by a stable elevated degree of coexpression of the interacting proteins. We use this conserved interaction data, combined with P. falciparum /Yeast orthologs, as proof-of-principle that high-order network topology can be used comparatively to deduce local network structure in non-model organisms. High local clustering is a criterion for the reliability of an interaction and coincides with preferred evolutionary conservation and significant coexpression. These strong and stable correlations indicate that evolutionary units go beyond a single protein to include the interactions among them. In particular, the stability of these signals in the face of extreme noise suggests that empirical protein interaction data can be integrated with orthologous clustering around these protein interactions to reliably infer local network structures in non-model organisms.

WIPER: A Multi-Agent System for Emergency Response

Abstract: This paper describes the proposed WIPER system. WIPER is intended to provide emergency planners and responders with an integrated system that will help to detect possible emergencies, as well as to suggest and evaluate possible courses of action to deal with the emergency. The system is designed as a multi-agent system using web services and the service oriented architecture. Components of the system for detecting and mitigating emergency situations can be added and removed from the system as the need arises. WIPER is designed to evaluate potential plans of action using a series of GIS enabled Agent-Based simulations that are grounded on realtime data from cell phone network providers. The system relies on the DDDAS concept, the interactive use of partial aggregate and detailed realtime data to continuously update the system and allow emergency planners to stay updated on the situation. The interaction with the system is done using a web-based interface and is composed of several overlaid layers of information, allowing users rich detail and flexibility.

WIPER: the integrated wireless phone based emergency response system

Abstract: We describe a prototype emergency response system. This dynamic data driven application system (DDDAS) uses wireless call data, including call volume, who calls whom, call duration, services in use, and cell phone location information. Since all cell phones (that are powered on) maintain contact with one or more local cell towers, location data about each phone is updated periodically and available throughout the cellular phone network. This permits the cell phones of a city to serve as an ad hoc mobile sensor net, measuring the movement and calling patterns of the population. Social network theory and statistical analysis on normal call activity and call locations establish a baseline. A detection and alert system monitors streaming summary cell phone call data. Abnormal call patterns or population movements trigger a simulation and prediction system. Hypotheses about the anomaly are generated by a rule-based system, each initiating an agent-based simulation. Automated dynamic validation of the simulations against incoming streaming data is used to test each hypothesis. A validated simulation is used to predict the evolution of the anomaly and made available to an emergency response decision support system.

Power Laws in Biological Networks

Abstract: The rapidly developing theory of complex networks indicates that real networks are not random, but have a highly robust large-scale architecture, governed by strict organizational principles. Here, we focus on the properties of biological networks, discussing their scale-free and hierarchical features. We illustrate the major network characteristics using examples from the metabolic network of the bacterium Escherichia coli. We also discuss the principles of network utilization, acknowledging that the interactions in a real network have unequal strengths. We study the interplay between topology and reaction fluxes provided by flux-balance analysis. We find that the cellular utilization of the metabolic network is both globally and locally highly inhomogeneous, dominated by “hot-spots”, rep-resenting connected high-flux pathways.

The architecture of complexity: from www to cellular metabolism

Abstract: Recent studies of complex systems indicate that real networks are far from random, instead having a highly robust, large-scale architecture that is governed by strict organizational principles. Here, we will focus on cellular networks, discussing their scale-free and hierarchical features. We will first discuss a few central network models, before illustrating the major network characteristics using examples primarily from bacterial metabolic networks. Additionally, as the interactions in real networks have unequal strengths, we discuss the interplay between network topology and reaction fluxes in cellular metabolic networks, as provided by the flux balance method. We find that the utilization of the metabolic networks is both globally and locally highly inhomogeneous, dominated by “hot-spots” that represent connected set of high-flux pathways.

Correspondence patterns: Mechanisms and models of human dynamics (Reply)

Abstract: Kentsis notes that the response time to an e-mail or a letter depends on the semantic content of the correspondence, as well as the social context in which the communication arises1. We would add that it also depends on deadlines, the time dependence of priorities and the dropping of past-deadline messages2, making human response dynamics sufficiently complicated that no simple model could fully account for it3,4,5,6. However, the advantage of the proposed modelling framework is that most of these effects can be incorporated into it, and their impact on the queuing process can be systematically evaluated. Addressing some of these additional mechanisms, including those suggested by Kentsis, requires information that is beyond reach for most researchers at this point.