Title: gl2vec: Learning Feature Representation Using Graphlets for Directed Networks,
Abstract: Learning network representations has a variety of applications, such as
network classification. Most existing work in this area focuses on static
undirected networks and do not account for presence of directed edges or
temporarily changes. Furthermore, most work focuses on node representations
that do poorly on tasks like network classification. In this paper, we propose
a novel, flexible and scalable network embedding methodology, \emph{gl2vec},
for network classification in both static and temporal directed networks.
\emph{gl2vec} constructs vectors for feature representation using static or
temporal network graphlet distributions and a null model for comparing them
against random graphs. We argue that \emph{gl2vec} can be used to classify and
compare networks of varying sizes and time period with high accuracy. We
demonstrate the efficacy and usability of \emph{gl2vec} over existing
state-of-the-art methods on network classification tasks such as network type
classification and subgraph identification in several real-world static and
temporal directed networks. Experimental results further show that
\emph{gl2vec}, concatenated with a wide range of state-of-the-art methods,
improves classification accuracy by up to $10\%$ in real-world applications
such as detecting departments for subgraphs in an email network or identifying
mobile users given their app switching behaviors represented as static or
temporal directed networks.