  Recent works on representation learning for graph structured data
predominantly focus on learning distributed representations of graph
substructures such as nodes and subgraphs. However, many graph analytics tasks
such as graph classification and clustering require representing entire graphs
as fixed length feature vectors. While the aforementioned approaches are
naturally unequipped to learn such representations, graph kernels remain as the
most effective way of obtaining them. However, these graph kernels use
handcrafted features (e.g., shortest paths, graphlets, etc.) and hence are
hampered by problems such as poor generalization. To address this limitation,
in this work, we propose a neural embedding framework named graph2vec to learn
data-driven distributed representations of arbitrary sized graphs. graph2vec's
embeddings are learnt in an unsupervised manner and are task agnostic. Hence,
they could be used for any downstream task such as graph classification,
clustering and even seeding supervised representation learning approaches. Our
experiments on several benchmark and large real-world datasets show that
graph2vec achieves significant improvements in classification and clustering
accuracies over substructure representation learning approaches and are
competitive with state-of-the-art graph kernels.
