Network representation learning (NRL) advances the conventional graph mining of social
networks, knowledge graphs, and complex biomedical and physics information networks …

Network embedding methods aim at learning low-dimensional latent representation of
nodes in a network. These representations can be used as features for a wide range of tasks …

There has been a surge of recent interest in graph representation learning (GRL). GRL
methods have generally fallen into three main categories, based on the availability of …

Since real-world objects and their interactions are often multi-modal and multi-typed,
heterogeneous networks have been widely used as a more powerful, realistic, and generic …

We present HARP, a novel method for learning low dimensional embeddings of a graph's
nodes which preserves higher-order structural features. Our proposed method achieves this …

Embedding a web-scale information network into a low-dimensional vector space facilitates
tasks such as link prediction, classification, and visualization. Past research has addressed …

Graph embedding methods represent nodes in a continuous vector space, preserving
different types of relational information from the graph. There are many hyper-parameters to …

We present WALKLETS, a novel approach for learning multiscale representations of vertices
in a network. In contrast to previous works, these representations explicitly encode multi …

Heterogeneous information networks (HINs) are ubiquitous in real-world applications. In the
meantime, network embedding has emerged as a convenient tool to mine and learn from …

Recent interest in graph embedding methods has focused on learning a single
representation for each node in the graph. But can nodes really be best described by a …