Nonparametric Link Prediction in Large Scale Dynamic Networks

TL;DR

This paper introduces a scalable nonparametric method for link prediction in large dynamic networks, leveraging local graph features and locality-sensitive hashing, outperforming existing methods especially in complex scenarios.

Contribution

It presents a novel scalable nonparametric model using local features and hashing, with theoretical guarantees and superior empirical performance.

Findings

Outperforms state-of-the-art methods on real-world data

Handles nonlinear and abrupt network changes effectively

Provides theoretical consistency and weak convergence results

Abstract

We propose a nonparametric approach to link prediction in large-scale dynamic networks. Our model uses graph-based features of pairs of nodes as well as those of their local neighborhoods to predict whether those nodes will be linked at each time step. The model allows for different types of evolution in different parts of the graph (e.g, growing or shrinking communities). We focus on large-scale graphs and present an implementation of our model that makes use of locality-sensitive hashing to allow it to be scaled to large problems. Experiments with simulated data as well as five real-world dynamic graphs show that we outperform the state of the art, especially when sharp fluctuations or nonlinearities are present. We also establish theoretical properties of our estimator, in particular consistency and weak convergence, the latter making use of an elaboration of Stein's method for dependency graphs.