Title: Deep Feature Learning for Graphs,
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.