Showing papers by "Albert-László Barabási published in 2013"

Quantifying long-term scientific impact.

Abstract: The lack of predictability of citation-based measures frequently used to gauge impact, from impact factors to short-term citations, raises a fundamental question: Is there long-term predictability in citation patterns? Here, we derive a mechanistic model for the citation dynamics of individual papers, allowing us to collapse the citation histories of papers from different journals and disciplines into a single curve, indicating that all papers tend to follow the same universal temporal pattern. The observed patterns not only help us uncover basic mechanisms that govern scientific impact but also offer reliable measures of influence that may have potential policy implications.

Observability of complex systems

Abstract: A quantitative description of a complex system is inherently limited by our ability to estimate the system’s internal state from experimentally accessible outputs. Although the simultaneous measurement of all internal variables, like all metabolite concentrations in a cell, offers a complete description of a system’s state, in practice experimental access is limited to only a subset of variables, or sensors. A system is called observable if we can reconstruct the system’s complete internal state from its outputs. Here, we adopt a graphical approach derived from the dynamical laws that govern a system to determine the sensors that are necessary to reconstruct the full internal state of a complex system. We apply this approach to biochemical reaction systems, finding that the identified sensors are not only necessary but also sufficient for observability. The developed approach can also identify the optimal sensors for target or partial observability, helping us reconstruct selected state variables from appropriately chosen outputs, a prerequisite for optimal biomarker design. Given the fundamental role observability plays in complex systems, these results offer avenues to systematically explore the dynamics of a wide range of natural, technological and socioeconomic systems.

Universality in network dynamics

Abstract: Models for the topology or dynamics of various networks abound, but until now, there has been no single universal framework for complex networks that can separate factors contributing to the topology and dynamics of networks across biological and social systems.

network link prediction by global silencing of indirect correlations

Abstract: By mathematically 'silencing' spurious, indirect correlations in networks, two groups devise approaches for improving many different types of network analyses.

Emergence of bimodality in controlling complex networks

Abstract: The control of a complex network can be achieved by different combinations of relatively few driver nodes. Tao Jia and colleagues show that this can lead to two distinct control modes—centralized or distributed—that determine the number of nodes that can act as driver node.

Effect of correlations on network controllability

Abstract: A dynamical system is controllable if by imposing appropriate external signals on a subset of its nodes, it can be driven from any initial state to any desired state in finite time. Here we study the impact of various network characteristics on the minimal number of driver nodes required to control a network. We find that clustering and modularity have no discernible impact, but the symmetries of the underlying matching problem can produce linear, quadratic or no dependence on degree correlation coefficients, depending on the nature of the underlying correlations. The results are supported by numerical simulations and help narrow the observed gap between the predicted and the observed number of driver nodes in real networks.

Control Capacity and A Random Sampling Method in Exploring Controllability of Complex Networks

Abstract: Controlling complex systems is a fundamental challenge of network science. Recent advances indicate that control over the system can be achieved through a minimum driver node set (MDS). The existence of multiple MDS's suggests that nodes do not participate in control equally, prompting us to quantify their participations. Here we introduce control capacity quantifying the likelihood that a node is a driver node. To efficiently measure this quantity, we develop a random sampling algorithm. This algorithm not only provides a statistical estimate of the control capacity, but also bridges the gap between multiple microscopic control configurations and macroscopic properties of the network under control. We demonstrate that the possibility of being a driver node decreases with a node's in-degree and is independent of its out-degree. Given the inherent multiplicity of MDS's, our findings offer tools to explore control in various complex systems.

Uncovering the role of elementary processes in network evolution

Abstract: The growth and evolution of networks has elicited considerable interest from the scientific community and a number of mechanistic models have been proposed to explain their observed degree distributions. Various microscopic processes have been incorporated in these models, among them, node and edge addition, vertex fitness and the deletion of nodes and edges. The existing models, however, focus on specific combinations of these processes and parameterize them in a way that makes it difficult to elucidate the role of the individual elementary mechanisms. We therefore formulated and solved a model that incorporates the minimal processes governing network evolution. Some contribute to growth such as the formation of connections between existing pair of vertices, while others capture deletion; the removal of a node with its corresponding edges, or the removal of an edge between a pair of vertices. We distinguish between these elementary mechanisms, identifying their specific role on network evolution.

Understanding the spread of malicious mobile-phone programs and their damage potential

Abstract: The fast growing market for smart phones coupled with their almost constant on-line presence makes these devices the new targets of malicious code (virus) writers. To aggravate the issue, the security level of these devices is far below the state-of-the art of what is used in personal computers. It has been recently found that the topological spread of multimedia message service (MMS) viruses is highly restricted by the underlying fragmentation of the call graph--the term topological here refers to the explicit use of the call graph topology to find vulnerable phones. In this paper, we study MMS viruses under another type of spreading behavior that locates vulnerable phones by generating a random list of numbers to be contacted, generally referred to as scanning. We find that hybrid MMS viruses including some level of scanning are more dangerous to the mobile community than their standard topological counterparts. Interestingly, this paper shows that the topological and scanning behaviors of MMS viruses can be more damaging in high and low market share cases, respectively. The results also show that given sufficient time, sophisticated viruses may infect a large fraction of susceptible phones without being detected. Fortunately, with the improvement of phone providers' monitoring ability and the timely installations of patches on infected phones, one can contain the spread of MMS viruses. Our findings lead to a better understanding on how one could prevent the spread of mobile-phone viruses even in light of new behaviors such as scanning.

Understanding the spread of malicious mobile-phone programs and their damage potential

Abstract: The fast growing market for smart phones coupled with their almost constant on-line presence makes these devices the new targets of malicious code (virus) writers. To aggravate the issue, the security level of these devices is far below the state-of-the art of what is used in personal computers. It has been recently found that the topological spread of multimedia message service (MMS) viruses is highly restricted by the underlying fragmentation of the call graph--the term topological here refers to the explicit use of the call graph topology to find vulnerable phones. In this paper, we study MMS viruses under another type of spreading behavior that locates vulnerable phones by generating a random list of numbers to be contacted, generally referred to as scanning. We find that hybrid MMS viruses including some level of scanning are more dangerous to the mobile community than their standard topological counterparts. Interestingly, this paper shows that the topological and scanning behaviors of MMS viruses can be more damaging in high and low market share cases, respectively. The results also show that given sufficient time, sophisticated viruses may infect a large fraction of susceptible phones without being detected. Fortunately, with the improvement of phone providers' monitoring ability and the timely installations of patches on infected phones, one can contain the spread of MMS viruses. Our findings lead to a better understanding on how one could prevent the spread of mobile-phone viruses even in light of new behaviors such as scanning.

Erratum: Universality in network dynamics

Abstract: Nature Physics 9, 673–681 (2013); published online 8 September 2013; corrected after print 9 October 2013. In the version of this Article originally published, the expression for Gij on page 673 should have included an absolute value sign. In addition, the caption for Fig. 1b1–c4 was missing the final wording: (note that the bounded distributions here and throughout are normalized to have mean zero and variance one).

Graph Theory Properties of Cellular Networks

Abstract: The functionality of the living cell is enabled by an intricate network of biochemical, metabolic and information transporting processes. These processes are carried out by the different network systems that comprise the cell’s activity, among which are the transcriptional regulatory network, the protein–protein interaction network and the metabolic network. To understand the functional design of these complex systems, it is worth referring to their abstract representation as graphs, where the interacting components, be them proteins, metabolites or genes, are designated as nodes, and the interactions between them as edges. Once the graphical description has been established, the tools of graph theory can be utilized to analyze the networks and obtain a better understanding of their overall construction. This approach has led to several groundbreaking discoveries on the nature of networks, crossing fields of research from biology to social science and technology. In this chapter we present the basic tools and concepts brought forth by the graph theoretic approach, and show their application to biological networks. We especially focus on the universal appearance of various features, such as small-world topologies, scale-free degree distributions and hierarchical and modular structures. These recurring patterns in the structure of the cellular networks are key to understanding their evolution, their design principles, and, most importantly, the way they function.