In previous articles, I gave an introduction to graph representation learning and highlighted several shallow methods for learning homogeneous graph embeddings. This article focuses on shallow representation learning methods for heterogeneous graphs.

While homogeneous networks have only one type of nodes and edges, heterogeneous networks contain different types of nodes or edges. So, a homogeneous network can also be considered as a special case of a heterogeneous network. Heterogeneous networks, also called heterogeneous information networks (HIN), are ubiquitous in real-world scenarios. For example, social media websites, like Facebook, contain a set of node types, such as users, posts, groups and, tags. By learning heterogeneous graph embeddings, we learn low-dimensional representations of the graph while preserving the heterogeneous structures and semantics for the downstream tasks (such as node classification, link prediction, etc.).

With its outstanding performance, ChatGPT has become a hot topic of discussion in the NLP community and beyond. This article delves into recent efforts to harness the power of ChatGPT for recommendation tasks.

The previous article “A Guide to Graph Representation Learning” provided a comprehensive introduction to the state of graph representation learning, along with a review of the basic terminologies, techniques, and applications. If you are new to the graph learning domain, I’d highly recommend you read the previous article first. This article takes a closer look at different types of shallow graph embedding models of homogeneous graphs. It also highlights a few real-world applications that build upon some of these ideas.