We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification. We present a densely connected autoencoder architecture capable of learning a joint representation of both local graph structure and available external node features for the multi-task learning of link prediction and node classification. To the best of our knowledge, this is the first architecture that can be efficiently trained end-to-end in a single learning stage to simultaneously perform link prediction and node classification. We provide comprehensive empirical evaluation of our models on a range of challenging benchmark graph-structured datasets, and demonstrate significant improvement in accuracy over related methods for graph representation learning. Code implementation is available at this https URL
The field of graph representation learning is seeing a resurgence of research interest in recent years, driven in part by the latest advances in deep learning. The aim is to learn a mapping that encodes the input graph into low-dimensional feature embeddings while preserving its original global structure. @cite_14 succinctly articulate the diverse set of previously proposed approaches for graph representation learning, or graph embedding, as belonging within a unified encoder-decoder framework. In this section, we summarize three classes of encoder-decoder models most related to our work: matrix factorization (MF), autoencoders, and graph convolutional networks (GCNs).
@cite_14
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We propose a novel subgraph image representation for classification of network fragments with the target being their parent networks. The graph image representation is based on 2D image embeddings of adjacency matrices. We use this image representation in two modes. First, as the input to a machine learning algorithm. Second, as the input to a pure transfer learner. Our conclusions from multiple datasets are that 1. deep learning using structured image features performs the best compared to graph kernel and classical features based methods; and, 2. pure transfer learning works effectively with minimum interference from the user and is robust against small data.