Link prediction finds missing links (in static networks) or predicts the likelihood of future links
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …

With the widespread use of information technologies, information networks are becoming
increasingly popular to capture complex relationships across various disciplines, such as …

Graph embedding is an effective method to represent graph data in a low dimensional
space for graph analytics. Most existing embedding algorithms typically focus on preserving …

We study the problem of representation learning in heterogeneous networks. Its unique
challenges come from the existence of multiple types of nodes and links, which limit the …

We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised
learning on graph-structured data based on the variational auto-encoder (VAE). This model …

Currently, the network traffic control systems are mainly composed of the Internet core and
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …

Prediction tasks over nodes and edges in networks require careful effort in engineering
features used by learning algorithms. Recent research in the broader field of representation …

In this paper, we propose a novel model for learning graph representations, which
generates a low-dimensional vector representation for each vertex by capturing the graph …

Abstract Representation learning has shown its effectiveness in many tasks such as image
classification and text mining. Network representation learning aims at learning distributed …

In this paper, we study an important yet largely under-explored setting of graph embedding,
ie, embedding communities instead of each individual nodes. We find that community …