Nonparametric Link Prediction in Dynamic Networks
This addresses link prediction for dynamic networks, offering improved accuracy in scenarios with complex temporal patterns, but it appears incremental as it builds on existing non-parametric and dynamic graph methods.
The paper tackles link prediction in dynamic networks by proposing a non-parametric algorithm that uses endpoint and local neighborhood features, proving its consistency and showing it outperforms state-of-the-art methods on real-world graphs, especially with sharp fluctuations or non-linearities.
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 five real-world dynamic graphs show that we outperform the state of the art, especially when sharp fluctuations or non-linearities are present.