Deep Feature Learning for Graphs

TL;DR

DeepGL introduces a scalable, interpretable, and transferable deep graph representation learning framework that outperforms existing methods in efficiency and accuracy, especially for attributed and large-scale graphs.

Contribution

DeepGL is a novel hierarchical graph learning framework that generalizes relational functions across networks, supporting attributed graphs and improving transfer learning capabilities.

Findings

Outperforms state-of-the-art in transfer learning and attributed graph tasks

Requires up to 6x less memory than previous methods

Achieves up to 182x faster runtime and 20%+ accuracy improvements

Abstract

This paper presents a general graph representation learning framework called DeepGL for learning deep node and edge representations from large (attributed) graphs. In particular, DeepGL begins by deriving a set of base features (e.g., graphlet features) and automatically learns a multi-layered hierarchical graph representation where each successive layer leverages the output from the previous layer to learn features of a higher-order. Contrary to previous work, DeepGL learns relational functions (each representing a feature) that generalize across-networks and therefore useful for graph-based transfer learning tasks. Moreover, DeepGL naturally supports attributed graphs, learns interpretable features, and is space-efficient (by learning sparse feature vectors). In addition, DeepGL is expressive, flexible with many interchangeable components, efficient with a time complexity of $\mathcal{O}(|E|)$, and scalable for large networks via an efficient parallel implementation. Compared with the state-of-the-art method, DeepGL is (1) effective for across-network transfer learning tasks and attributed graph representation learning, (2) space-efficient requiring up to 6x less memory, (3) fast with up to 182x speedup in runtime performance, and (4) accurate with an average improvement of 20% or more on many learning tasks.