1 research outputs found
motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks
Recent years have witnessed a surge of interest in machine learning on graphs
and networks with applications ranging from vehicular network design to IoT
traffic management to social network recommendations. Supervised machine
learning tasks in networks such as node classification and link prediction
require us to perform feature engineering that is known and agreed to be the
key to success in applied machine learning. Research efforts dedicated to
representation learning, especially representation learning using deep
learning, has shown us ways to automatically learn relevant features from vast
amounts of potentially noisy, raw data. However, most of the methods are not
adequate to handle heterogeneous information networks which pretty much
represents most real-world data today. The methods cannot preserve the
structure and semantic of multiple types of nodes and links well enough,
capture higher-order heterogeneous connectivity patterns, and ensure coverage
of nodes for which representations are generated. We propose a novel efficient
algorithm, motif2vec that learns node representations or embeddings for
heterogeneous networks. Specifically, we leverage higher-order, recurring, and
statistically significant network connectivity patterns in the form of motifs
to transform the original graph to motif graph(s), conduct biased random walk
to efficiently explore higher order neighborhoods, and then employ
heterogeneous skip-gram model to generate the embeddings. Unlike previous
efforts that uses different graph meta-structures to guide the random walk, we
use graph motifs to transform the original network and preserve the
heterogeneity. We evaluate the proposed algorithm on multiple real-world
networks from diverse domains and against existing state-of-the-art methods on
multi-class node classification and link prediction tasks, and demonstrate its
consistent superiority over prior work