Non-parametric Link Prediction

Details Non-parametric Link Prediction Non-parametric Link Prediction

2011-09-06

arxiv.org/abs/1109.1077v1

Statistics

Machine Learning

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Non-parametric Link Prediction does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Non-parametric Link Prediction, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Non-parametric Link Prediction will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-92909