The value of temporal data for learning of influence networks: A characterization via Kullback-Leibler divergence

We infer local influence relations between networked entities from data on outcomes and assess the value of temporal data by formulating relevant binary hypothesis testing problems and characterizing the speed of learning of the correct hypothesis via the Kullback-Leibler divergence, under three different types of available data: knowing the set of entities who take a particular action; knowing the order that the entities take an action; and knowing the times of the actions.

[1]  M. Argyle.  Social interactions. , 1976, Science.

[2]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[3]  C. Manski.  Identification of Endogenous Social Effects: The Reflection Problem , 1993 .

[4]  Jeffrey O. Kephart,et al.  Directed-graph epidemiological models of computer viruses , 1991, Proceedings. 1991 IEEE Computer Society Symposium on Research in Security and Privacy.

[5]  Bernard Fortin,et al.  Identification of Peer Effects through Social Networks , 2007, SSRN Electronic Journal.

[6]  Le Song,et al.  Estimating time-varying networks , 2008, ISMB 2008.

[7]  Vwani P. Roychowdhury,et al.  Information resonance on Twitter: watching Iran , 2010, SOMA '10.

[8]  Kristina Lerman,et al.  Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks , 2010, ICWSM.

[9]  Yannis M. Ioannides,et al.  Identification of Social Interactions , 2010 .

[10]  A. Willsky,et al.  Latent variable graphical model selection via convex optimization , 2010 .

[11]  Vikas Sindhwani,et al.  Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference , 2010, NIPS.

[12]  Jure Leskovec,et al.  On the Convexity of Latent Social Network Inference , 2010, NIPS.

[13]  Bernhard Schölkopf,et al.  Uncovering the Temporal Dynamics of Diffusion Networks , 2011, ICML.

[14]  Alex Pentland,et al.  Composite Social Network for Predicting Mobile Apps Installation , 2011, AAAI.

[15]  Devavrat Shah,et al.  Rumors in a Network: Who's the Culprit? , 2009, IEEE Transactions on Information Theory.

[16]  Vincent Y. F. Tan,et al.  Learning Latent Tree Graphical Models , 2010, J. Mach. Learn. Res..

[17]  Sujay Sanghavi,et al.  Learning the graph of epidemic cascades , 2012, SIGMETRICS '12.

[18]  Murti V. Salapaka,et al.  On the Problem of Reconstructing an Unknown Topology via Locality Properties of the Wiener Filter , 2010, IEEE Transactions on Automatic Control.

[19]  Vincent Y. F. Tan,et al.  High-dimensional structure estimation in Ising models: Local separation criterion , 2011, 1107.1736.

[20]  Le Song,et al.  Learning Networks of Heterogeneous Influence , 2012, NIPS.

[21]  Sujay Sanghavi,et al.  Finding the Graph of Epidemic Cascades , 2012, ArXiv.

[22]  Jure Leskovec,et al.  Inferring networks of diffusion and influence , 2010, KDD.

[23]  Vincent Gripon,et al.  Reconstructing a graph from path traces , 2013, 2013 IEEE International Symposium on Information Theory.

[24]  Alessandro Panconesi,et al.  Trace complexity of network inference , 2013, KDD.

[25]  Le Song,et al.  Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm , 2014, ICML.

[26]  John N. Tsitsiklis,et al.  The Value of Temporally Richer Data for Learning of Influence Networks , 2014, WINE.

[27]  Michael Kearns,et al.  Learning from Contagion (Without Timestamps) , 2014, ICML.

[28]  Spyridon Ilias Zoumpoulis,et al.  Networks, decisions, and outcomes: coordination with local information and the value of temporal data for learning influence networks , 2014 .

[29]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..