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Recent years have witnessed a surge of interest in machine learning on graphsand networks with applications ranging from vehicular network design to IoTtraffic management to social network recommendations. Supervised machinelearning tasks in networks such as node classification and link predictionrequire us to perform feature engineering that is known and agreed to be thekey to success in applied machine learning. Research efforts dedicated torepresentation learning, especially representation learning using deeplearning, has shown us ways to automatically learn relevant features from vastamounts of potentially noisy, raw data. However, most of the methods are notadequate to handle heterogeneous information networks which pretty muchrepresents most real-world data today. The methods cannot preserve thestructure and semantic of multiple types of nodes and links well enough,capture higher-order heterogeneous connectivity patterns, and ensure coverageof nodes for which representations are generated. We propose a novel efficientalgorithm, motif2vec that learns node representations or embeddings forheterogeneous networks. Specifically, we leverage higher-order, recurring, andstatistically significant network connectivity patterns in the form of motifsto transform the original graph to motif graph(s), conduct biased random walkto efficiently explore higher order neighborhoods, and then employheterogeneous skip-gram model to generate the embeddings. Unlike previousefforts that uses different graph meta-structures to guide the random walk, weuse graph motifs to transform the original network and preserve theheterogeneity. We evaluate the proposed algorithm on multiple real-worldnetworks from diverse domains and against existing state-of-the-art methods onmulti-class node classification and link prediction tasks, and demonstrate itsconsistent superiority over prior work.

motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks