Deep Kernel Network Embedding

This paper concerns the problem of network embedding (NE), whose aim is to learn a low-dimensional representation for each node in networks. We provide a new train to solve the sparsity problem where most of nodes including the new arrival nodes have little knowledge with respect to the network. A novel paradigm is proposed to integrate the multiple information from the subgraph covering the target node instead of only the target node. Particularly, to epxress the distinctive feature over the vertex domain, a probabiltiy distribution over subgraph space is constructed for each node. The distribution is more effective to express the distinctive characteristic and feature in a higher dimension compared to the latent representation vectors. One of the primary goals of this paradigm is to define the convolution operation over the distributions, which are efficient to evaluate and learn. Experiments on four real-world network datasets demonstrate that our approach significantly outperforms state-of-the-art methods, especially on the representation learning for the nodes newly joining in the network.