Multi-Label Graph Convolutional Network Representation Learning

--Knowledge representation of graph-based systems is fundamental across many disciplines. T o date, most existing methods for representation learning primarily focus on networks with simplex labels, yet real-world objects (nodes) are inherently complex in nature and often contain rich semantics or labels, e . The multi-label network nodes not only have multiple labels for each node, such labels are often highly correlated making existing methods ineffective or fail to handle such correlation for node representation learning. In this paper, we propose a novel multi-label graph convolutional network (ML-GCN) for learning node representation for multi-label networks. T o fully explore label-label correlation and network topology structures, we propose to model a multi-label network as two Siamese GCNs: a node-node-label graph and a label-label-node graph. The two GCNs each handle one aspect of representation learning for nodes and labels, respectively, and they are seamlessly integrated under one objective function. The learned label representations can effectively preserve the inner-label interaction and node label properties, and are then aggregated to enhance the node representation learning under a unified training framework. Experiments and comparisons on multi-label node classification validate the effectiveness of our proposed approach. Graphs have become increasingly common structures for organizing data in many complex systems such as sensor networks, citation networks, social networks and many more [1]. Such a development raised new requirement of efficient network representation or embedding learning algorithms for various real-world applications, which seeks to learn low-dimensional vector representations of all nodes with preserved graph topology structures, such as edge links, degrees, and communities etc.