Deep Inductive Graph Representation Learning

This paper presents a general inductive graph representation learning framework called
$\text{DeepGL}$
for learning deep node
and
edge features that generalize across-networks. In particular,
$\text{DeepGL}$
begins by deriving a set of base features from the graph (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,
$\text{DeepGL}$
learns
relational functions
(each representing a feature) that naturally generalize across-networks and are therefore useful for graph-based transfer learning tasks. Moreover,
$\text{DeepGL}$
naturally supports attributed graphs, learns interpretable inductive graph representations, and is space-efficient (by learning sparse feature vectors). In addition,
$\text{DeepGL}$
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 recent methods,
$\text{DeepGL}$
is (1)
effective
for across-network transfer learning tasks
and
large (attributed) graphs, (2)
space-efficient
requiring up to 6x less memory, (3)
fast
with up to 106x speedup in runtime performance, and (4)
accurate
with an average improvement in AUC of 20 percent or more on many learning tasks and across a wide variety of networks.