Scalable Deep Metric Learning on Attributed Graphs
This addresses the need for scalable graph embedding methods that work with attributed graphs for researchers and practitioners in graph machine learning.
The paper tackles the problem of constructing embeddings for large attributed graphs to support multiple downstream learning tasks, achieving better consistency across node clustering, node classification, and link prediction compared to any single existing method.
We consider the problem of constructing embeddings of large attributed graphs and supporting multiple downstream learning tasks. We develop a graph embedding method, which is based on extending deep metric and unbiased contrastive learning techniques to 1) work with attributed graphs, 2) enabling a mini-batch based approach, and 3) achieving scalability. Based on a multi-class tuplet loss function, we present two algorithms -- DMT for semi-supervised learning and DMAT-i for the unsupervised case. Analyzing our methods, we provide a generalization bound for the downstream node classification task and for the first time relate tuplet loss to contrastive learning. Through extensive experiments, we show high scalability of representation construction, and in applying the method for three downstream tasks (node clustering, node classification, and link prediction) better consistency over any single existing method.