GNN Applied to Ego-nets for Friend Suggestions

TL;DR

This paper introduces a scalable GNN framework for friend suggestion in large social networks by reducing full graph link prediction to ego-net tasks, and demonstrates its effectiveness through experiments and live testing.

Contribution

The paper presents the Generalized Ego-network Friendship Score framework and the WalkGNN model, enabling scalable, supervised link prediction on large social graphs.

Findings

WalkGNN outperforms baseline methods in accuracy.

The framework improves business metrics in live A/B tests.

The Ego-VK dataset accurately represents real-world social network link prediction.

Abstract

A major problem of making friend suggestions in social networks is the large size of social graphs, which can have hundreds of millions of people and tens of billions of connections. Classic methods based on heuristics or factorizations are often used to address the difficulties of scaling more complex models. However, the unsupervised nature of these methods can lead to suboptimal results. In this work, we introduce the Generalized Ego-network Friendship Score framework, which makes it possible to use complex supervised models without sacrificing scalability. The main principle of the framework is to reduce the problem of link prediction on a full graph to a series of low-scale tasks on ego-nets with subsequent aggregation of their results. Here, the underlying model takes an ego-net as input and produces a pairwise relevance matrix for its nodes. In addition, we develop the WalkGNN model which is capable of working effectively in the social network domain, where these graph-level link prediction tasks are heterogeneous, dynamic and featureless. To measure the accuracy of this model, we introduce the Ego-VK dataset that serves as an exact representation of the real-world problem that we are addressing. Offline experiments on the dataset show that our model outperforms all baseline methods, and a live A/B test demonstrates the growth of business metrics as a result of utilizing our approach.