No abstract

Our ability to uncover complex network structure and dynamics from data is fundamental to understanding and controlling collective dynamics in complex systems. Despite recent progress in this area, reconstructing networks with stochastic dynamical processes from limited time series remains to be an outstanding problem. Here we develop a framework based on compressed sensing to reconstruct complex networks on which stochastic spreading dynamics take place. We apply the methodology to a large number of model and real networks, finding that a full reconstruction of inhomogeneous interactions can be achieved from small amounts of polarized (binary) data, a virtue of compressed sensing. Further, we demonstrate that a hidden source that triggers the spreading process but is externally inaccessible can be ascertained and located with high confidence in the absence of direct routes of propagation from it. Our approach thus establishes a paradigm for tracing and controlling epidemic invasion and information diffusion in complex networked systems.

Reconstructing complex networks from measurable data is a fundamental problem for understanding and controlling collective dynamics of complex networked systems. However, a significant challenge arises when we attempt to decode structural information hidden in limited amounts of data accompanied by noise and in the presence of inaccessible nodes. Here, we develop a general framework for robust reconstruction of complex networks from sparse and noisy data. Specifically, we decompose the task of reconstructing the whole network into recovering local structures centered at each node. Thus, the natural sparsity of complex networks ensures a conversion from the local structure reconstruction into a sparse signal reconstruction problem that can be addressed by using the lasso, a convex optimization method. We apply our method to evolutionary games, transportation and communication processes taking place in a variety of model and real complex networks, finding that universal high reconstruction accuracy can be achieved from sparse data in spite of noise in time series and missing data of partial nodes. Our approach opens new routes to the network reconstruction problem and has potential applications in a wide range of fields. 89.75.Fb, 05.45.Tp Complex networked systems are common in many fields [1][2][3]. The need to ascertain collective dynamics of such systems to control them is shared among different scientific communities [4][5][6]. Much evidence has demonstrated that interaction patterns among dynamical elements captured by a complex network play deterministic roles in collective dynamics [7]. It is thus imperative to study a complex networked system as a whole rather than study each component separately to offer a comprehensive understanding of the whole system [8]. However, we are often incapable of directly accessing network structures; instead, only limited observable data are available [9], raising the need for network reconstruction approaches to uncovering network structures from data. Network reconstruction, the inverse problem, is challenging because structural information is hidden in measurable data in an unknown manner and the solution space of all possible structural configurations is of extremely high dimension. So far a number of approaches have been proposed to address the inverse problem [4,5,[9][10][11][12][13][14][15][16][17]. However, accurate and robust reconstruction of large complex networks is still a challenging problem, especially given limited measurements disturbed by noise and unexpected factors.In this letter, we develop a general framework to reconcile the contradiction between the robustness of reconstructing complex networks and limits on our ability to access sufficient amounts of data required by conventional approaches. The key lies in converting the network reconstruction problem into a sparse signal reconstruction problem that can be addressed by exploiting the lasso, a convex optimization algorithm [18,19]. In particular, reconstructing the whole network structure can be...

Despite persistent efforts in understanding the creativity of scientists over different career stages, little is known about the underlying dynamics of research topic switching that drives innovation. Here, we analyze the publication records of individual scientists, aiming to quantify their topic switching dynamics and its influence. We find that the co-citing network of papers of a scientist exhibits a clear community structure where each major community represents a research topic. Our analysis suggests that scientists have a narrow distribution of number of topics. However, researchers nowadays switch more frequently between topics than those in the early days. We also find that high switching probability in early career is associated with low overall productivity, yet with high overall productivity in latter career. Interestingly, the average citation per paper, however, is in all career stages negatively correlated with the switching probability. We propose a model that can explain the main observed features.