Born to be big: Data, graphs, and their entangled complexity

Big Data and Big Graphs have become landmarks of current cross-border research, destined to remain so for long time. While we try to optimize the ability of assimilating both, novel methods continue to inspire new applications, and vice versa.Clearly these two big things, data and graphs, are connected, but can we ensure management of their complexities, computational efficiency, robust inference? Critical bridging features are addressed here to identify grand challenges and bottlenecks.

[1]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[2]  Gitta Kutyniok,et al.  1 . 2 Sparsity : A Reasonable Assumption ? , 2012 .

[3]  Jure Leskovec,et al.  Statistical properties of community structure in large social and information networks , 2008, WWW.

[4]  Enrico Capobianco,et al.  Aliasing in Gene Feature Detection by Projective Methods , 2009, J. Bioinform. Comput. Biol..

[5]  Ricardo López-Ruiz,et al.  A Statistical Measure of Complexity , 1995, ArXiv.

[6]  H. Stanley,et al.  Anomalous conductance and diffusion in complex networks , 2016 .

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

[8]  Andrea Montanari,et al.  Computational Implications of Reducing Data to Sufficient Statistics , 2014, ArXiv.

[9]  B. Lindsay.  Mixture models : theory, geometry, and applications , 1995 .

[10]  Euan A Ashley,et al.  Using "big data" to dissect clinical heterogeneity. , 2015, Circulation.

[11]  Kwang-Il Goh,et al.  Towards real-world complexity: an introduction to multiplex networks , 2015, ArXiv.

[12]  Pedro M. Domingos,et al.  Deep Symmetry Networks , 2014, NIPS.

[13]  A. Yashin,et al.  Heterogeneity's ruses: some surprising effects of selection on population dynamics. , 1985, The American statistician.

[14]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Thomas Manke,et al.  Robustness and network evolution--an entropic principle , 2005 .

[16]  E A Leicht,et al.  Mixture models and exploratory analysis in networks , 2006, Proceedings of the National Academy of Sciences.

[17]  Enrico Capobianco,et al.  Sub-Modular Resolution Analysis by Network Mixture Models , 2010, Statistical applications in genetics and molecular biology.

[18]  J. Ramasco,et al.  Inversion method for content-based networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Jean-Jacques E. Slotine,et al.  The missing link , 2012, Nature Physics.

[20]  Bruno A. Olshausen,et al.  Sparse Codes and Spikes , 2001 .

[21]  Paolo Zanardi,et al.  Bipartite quantum states and random complex networks , 2011, 1103.5989.

[22]  U. Grenander,et al.  Probability and Statistics: The Harald Cramer Volume. , 1960 .

[23]  C. Lynch.  Big data: How do your data grow? , 2008, Nature.

[24]  Nitesh V. Chawla,et al.  Data Mining for Imbalanced Datasets: An Overview , 2005, The Data Mining and Knowledge Discovery Handbook.

[25]  Vito Latora,et al.  Remote synchronization reveals network symmetries and functional modules. , 2012, Physical review letters.

[26]  Han Liu,et al.  Challenges of Big Data Analysis. , 2013, National science review.

[27]  Roman Orus,et al.  A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States , 2013, 1306.2164.