Efficient Link Prediction via GNN Layers Induced by Negative Sampling

Yuxin Wang, Xiannian Hu, Quan Gan, Xuanjing Huang, Xipeng Qiu, David Wipf

Keywords: Link prediction, Inference speed

TL;DR: This paper introduces a novel Graph Neural Network offering comparable or superior performance to subgraph-aided models in link prediction while retaining the efficient inference speed of traditional GAEs.

Abstract: Graph neural networks (GNNs) for link prediction can loosely be divided into two broad categories. First, \emph{node-wise} architectures pre-compute individual embeddings for each node that are later combined by a simple decoder to make predictions. While extremely efficient at inference time (since node embeddings are only computed once and repeatedly reused), model expressiveness is limited such that isomorphic nodes contributing to candidate edges may not be distinguishable, compromising accuracy. In contrast, \emph{edge-wise} methods rely on the formation of edge-specific subgraph embeddings to enrich the representation of pair-wise relationships, disambiguating isomorphic nodes to improve accuracy, but with the cost of increased model complexity. To better navigate this trade-off, we propose a novel GNN architecture whereby the \emph{forward pass} explicitly depends on \emph{both} positive (as is typical) and negative (unique to our approach) edges to inform more flexible, yet still cheap node-wise embeddings. This is achieved by recasting the embeddings themselves as minimizers of a forward-pass-specific energy function (distinct from the actual training loss) that favors separation of positive and negative samples. As demonstrated by extensive empirical evaluations, the resulting architecture retains the inference speed of node-wise models, while producing competitive accuracy with edge-wise alternatives.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 2336