Information Flow Prediction by Modeling Dynamic Probabilistic Social Network

A driving control apparatus for an engine of a vehicle comprises a first control unit 21 for calculating an engine control parameter, a second control unit 22 for calculating a throttle control amount, and first and second communication lines L1 and L2 for transmitting data between the first and the second control units. The second control unit includes an instrument for judging an abnormality of the first communication line L1. The first control unit includes an instrument for judging an abnormality of the second communication line L2, and second control unit monitor for monitoring an operation of the second control unit based on a first throttle opening degree signal T1 when an abnormality of each of the communication lines is judged. With the above arrangement, an abnormality of the second control unit can be judged at the side of the first control unit with a simple logic, and a reliable driving property can be secured at low costs.

[1]  Stéphane H. Maes,et al.  Multigrained modeling with pattern specific maximum likelihood transformations for text-independent speaker recognition , 2003, IEEE Trans. Speech Audio Process..

[2]  Lada A. Adamic,et al.  Tracking information epidemics in blogspace , 2005, The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05).

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

[4]  Fan Chung Graham,et al.  A Random Graph Model for Power Law Graphs , 2001, Exp. Math..

[5]  T.R. Coffman,et al.  Sensitivity of social network analysis metrics to observation noise , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[6]  Gueorgi Kossinets,et al.  Empirical Analysis of an Evolving Social Network , 2006, Science.

[7]  A. Moore,et al.  Dynamic social network analysis using latent space models , 2005, SKDD.

[8]  Jeff A. Johnson,et al.  ContactMap : Integrating Communication and Information Through Visualizing Personal Social Networks , 2001 .

[9]  David R. Hunter,et al.  Curved Exponential Family Models for Networks , 2005 .

[10]  Peter D. Hoff,et al.  Latent Space Approaches to Social Network Analysis , 2002 .

[11]  Yiming Yang,et al.  Stochastic link and group detection , 2002, AAAI/IAAI.

[12]  A. Barabasi,et al.  Halting viruses in scale-free networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  R. Pastor-Satorras,et al.  Epidemic spreading in correlated complex networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[15]  Ching-Yung Lin,et al.  Modeling and predicting personal information dissemination behavior , 2005, KDD '05.

[16]  Sherry Marcus,et al.  Graph-based technologies for intelligence analysis , 2004, CACM.

[17]  Tom A. B. Snijders,et al.  Markov Chain Monte Carlo Estimation of Exponential Random Graph Models , 2002, J. Soc. Struct..

[18]  S. Strogatz Exploring complex networks , 2001, Nature.

[19]  R. Milo,et al.  Subgraphs in random networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Ching-Yung Lin,et al.  Personalized recommendation driven by information flow , 2006, SIGIR.

[21]  M. Newman Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  N. Demiris Bayesian Inference for Stochastic Epidemic Models using Markov chain Monte Carlo Methods , 2004 .

[23]  Gueorgi Kossinets Effects of missing data in social networks , 2006, Soc. Networks.

[24]  T. Snijders Models for longitudinal network datain , 2005 .

[25]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[26]  S H Strogatz,et al.  Random graph models of social networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.