Title: Inductive Representation Learning in Large Attributed Graphs,
Abstract: Graphs (networks) are ubiquitous and allow us to model entities (nodes) and
the dependencies (edges) between them. Learning a useful feature representation
from graph data lies at the heart and success of many machine learning tasks
such as classification, anomaly detection, link prediction, among many others.
Many existing techniques use random walks as a basis for learning features or
estimating the parameters of a graph model for a downstream prediction task.
Examples include recent node embedding methods such as DeepWalk, node2vec, as
well as graph-based deep learning algorithms. However, the simple random walk
used by these methods is fundamentally tied to the identity of the node. This
has three main disadvantages. First, these approaches are inherently
transductive and do not generalize to unseen nodes and other graphs. Second,
they are not space-efficient as a feature vector is learned for each node which
is impractical for large graphs. Third, most of these approaches lack support
for attributed graphs.
To make these methods more generally applicable, we propose a framework for
inductive network representation learning based on the notion of attributed
random walk that is not tied to node identity and is instead based on learning
a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute
vector $\mathrm{\rm \bf x}$ to a type $w$. This framework serves as a basis for
generalizing existing methods such as DeepWalk, node2vec, and many other
previous methods that leverage traditional random walks.