Global and partitioned reconstructions of undirected complex networks

AbstractIt is a significant challenge to predict the network topology from a small amount of dynamical observations. Different from the usual framework of the node-based reconstruction, two optimization approaches (i.e., the global and partitioned reconstructions) are proposed to infer the structure of undirected networks from dynamics. These approaches are applied to evolutionary games occurring on both homogeneous and heterogeneous networks via compressed sensing, which can more efficiently achieve higher reconstruction accuracy with relatively small amounts of data. Our approaches provide different perspectives on effectively reconstructing complex networks.

[1]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[2]  Wen-Xu Wang,et al.  Efficient Reconstruction of Heterogeneous Networks from Time Series via Compressed Sensing , 2015, PloS one.

[3]  D. Lusseau,et al.  The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations , 2003, Behavioral Ecology and Sociobiology.

[4]  Pik-Yin Lai,et al.  Reconstructing weighted networks from dynamics. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Roger Guimerà,et al.  Missing and spurious interactions and the reconstruction of complex networks , 2009, Proceedings of the National Academy of Sciences.

[6]  Marc Timme,et al.  Inferring network topology from complex dynamics , 2010, 1007.1640.

[7]  Arne Traulsen,et al.  Coevolution of strategy and structure in complex networks with dynamical linking. , 2006, Physical review letters.

[8]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[9]  J Kurths,et al.  Inner composition alignment for inferring directed networks from short time series. , 2011, Physical review letters.

[10]  Zhen Wang,et al.  If players are sparse social dilemmas are too: Importance of percolation for evolution of cooperation , 2012, Scientific Reports.

[11]  Attila Szolnoki,et al.  Restricted connections among distinguished players support cooperation. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  G. Szabó,et al.  Evolutionary games on graphs , 2006, cond-mat/0607344.

[13]  M. Nowak,et al.  Evolutionary games and spatial chaos , 1992, Nature.

[14]  Eric H Davidson,et al.  A Gene Regulatory Network Subcircuit Drives a Dynamic Pattern of Gene Expression , 2007, Science.

[15]  Albert-László Barabási,et al.  Controllability of complex networks , 2011, Nature.

[16]  Wen-Xu Wang,et al.  Reconstructing propagation networks with natural diversity and identifying hidden sources , 2014, Nature Communications.

[17]  T. Sauer,et al.  Reconstructing the topology of sparsely connected dynamical networks. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  A. Barabasi,et al.  Network link prediction by global silencing of indirect correlations , 2013, Nature Biotechnology.

[19]  Wen-Xu Wang,et al.  Time-series–based prediction of complex oscillator networks via compressive sensing , 2011 .

[20]  Ming Xu,et al.  Analytical controllability of deterministic scale-free networks and Cayley trees , 2014, 1411.1175.

[21]  M. Timme,et al.  Revealing networks from dynamics: an introduction , 2014, 1408.2963.

[22]  Wen-Xu Wang,et al.  Exact controllability of complex networks , 2013, Nature Communications.

[23]  M. Perc,et al.  Towards effective payoffs in the prisoner’s dilemma game on scale-free networks , 2007, 0711.4028.

[24]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Attila Szolnoki,et al.  Coevolutionary Games - A Mini Review , 2009, Biosyst..

[26]  Tao Zhou,et al.  Epidemic spreading on heterogeneous networks with identical infectivity , 2007 .

[27]  G. Szabó,et al.  Evolutionary prisoner's dilemma game on a square lattice , 1997, cond-mat/9710096.

[28]  H. Stanley,et al.  Spontaneous recovery in dynamical networks , 2013, Nature Physics.

[29]  Diogo M. Camacho,et al.  Wisdom of crowds for robust gene network inference , 2012, Nature Methods.

[30]  Xiao Han,et al.  Robust Reconstruction of Complex Networks from Sparse Data , 2015, Physical review letters.

[31]  Michael Doebeli,et al.  Spatial structure often inhibits the evolution of cooperation in the snowdrift game , 2004, Nature.

[32]  Kazuyuki Aihara,et al.  Unambiguous reconstruction of network structure using avalanche dynamics. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  G. Szabó,et al.  Phase diagrams for an evolutionary prisoner's dilemma game on two-dimensional lattices. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  B. Kerner.  Criticism of generally accepted fundamentals and methodologies of traffic and transportation theory: A brief review , 2013 .

[35]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[36]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.