TY - GEN
T1 - Sparse approximations of directed information graphs
AU - Quinn, Christopher J.
AU - Pinar, Ali
AU - Gao, Jing
AU - Su, Lu
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/10
Y1 - 2016/8/10
N2 - Given a network of agents interacting over time, which few interactions best characterize the dynamics of the whole network? We propose an algorithm that finds the optimal sparse approximation of a network. The user controls the level of sparsity by specifying the total number of edges. The networks are represented using directed information graphs, a graphical model that depicts causal influences between agents in a network. Goodness of approximation is measured with Kullback-Leibler divergence. The algorithm finds the best approximation with no assumptions on the topology or the class of the joint distribution.
AB - Given a network of agents interacting over time, which few interactions best characterize the dynamics of the whole network? We propose an algorithm that finds the optimal sparse approximation of a network. The user controls the level of sparsity by specifying the total number of edges. The networks are represented using directed information graphs, a graphical model that depicts causal influences between agents in a network. Goodness of approximation is measured with Kullback-Leibler divergence. The algorithm finds the best approximation with no assumptions on the topology or the class of the joint distribution.
UR - https://www.scopus.com/pages/publications/84985905495
U2 - 10.1109/ISIT.2016.7541596
DO - 10.1109/ISIT.2016.7541596
M3 - Conference contribution
AN - SCOPUS:84985905495
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 1735
EP - 1739
BT - Proceedings - 2016 IEEE International Symposium on Information Theory, ISIT 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Symposium on Information Theory, ISIT 2016
Y2 - 10 July 2016 through 15 July 2016
ER -