Difference between revisions of "Latent Social Network Influence"
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| − | + | This paper is interesting paper of learning the graph structure which is not a typical Bayesian graph learning. The main problem that the authors try to solve is given a set of nodes and network diffusion, learn the graph over which the contagion spreads. Typically we observe the nodes getting infected and the time they get infected, however we cannot know who infected them. This problem is converted to a maximum likelihood based convex optimization problem and further they introduce an <math> l_1</math> penalty to penalize the graph with larger number of nodes. | |
== Description of the method == | == Description of the method == | ||
Revision as of 19:13, 4 February 2011
Contents
Citation
S. A. Myers & J. Leskovec "On the Convexity of Latent Social Network Inference" In Neural Information Processing Systems (NIPS), 2010.
Online version
Summary
This paper is interesting paper of learning the graph structure which is not a typical Bayesian graph learning. The main problem that the authors try to solve is given a set of nodes and network diffusion, learn the graph over which the contagion spreads. Typically we observe the nodes getting infected and the time they get infected, however we cannot know who infected them. This problem is converted to a maximum likelihood based convex optimization problem and further they introduce an penalty to penalize the graph with larger number of nodes.
