Non-parametric Link Prediction

Friday, October 28, 2011 - 10:15am - 11:15am
Keller 3-180
Purnamrita Sarkar (University of California, Berkeley)
We propose a non-parametric link prediction algorithm for a sequence of graph snapshots over time. The model predicts links based on the features of its endpoints, as well as those of the local neighborhood around the endpoints. This allows for different types of neighborhoods in a graph, each with its own dynamics (e.g, growing or shrinking communities). We prove the consistency
of our estimator, and give a fast implementation based on locality-sensitive hashing. Experiments with simulated as well as three real-world dynamic graphs show that we outperform the state of the art, especially when sharp fluctuations or non-linearities are present in the graph sequence.