Influence Prediction for ContinuousTime Information Propagation on Networks
Abstract
We consider the problem of predicting the time evolution of influence, the expected number of activated nodes, given a set of initially active nodes on a propagation network. To address the significant computational challenges of this problem on largescale heterogeneous networks, we establish a system of differential equations governing the dynamics of probability mass functions on the state graph where the nodes each lumps a number of activation states of the network, which can be considered as an analogue to the FokkerPlanck equation in continuous space. We provides several methods to estimate the system parameters which depend on the identities of the initially active nodes, network topology, and activation rates etc. The influence is then estimated by the solution of such a system of differential equations. This approach gives rise to a class of novel and scalable algorithms that work effectively for largescale and dense networks. Numerical results are provided to show the very promising performance in terms of prediction accuracy and computational efficiency of this approach.
 Publication:

arXiv eprints
 Pub Date:
 December 2015
 arXiv:
 arXiv:1512.05417
 Bibcode:
 2015arXiv151205417C
 Keywords:

 Computer Science  Social and Information Networks;
 Mathematics  Numerical Analysis;
 Physics  Physics and Society;
 65C40;
 65Y10;
 68U35
 EPrint:
 14 pages, 17 figures