Selected Research Projects

Deep Graph Contrastive Learning

Existing deep graph neural networks (GNNs) typically require a large amount of labeled data in order to be trained effectively. This can be a significant challenge, as collecting and labeling data can be time-consuming and labor-intensive, especially for large and complex datasets. Fortunately, unlabeled data is often more readily available in practice, and can provide valuable information about the structure and patterns of the data. The question, then, is whether it is possible to learn from unlabeled data using GNNs, and if so, how can we do it effectively? Our research addresses this question using the technique of graph contrastive learning, which involves augmenting the input graphs in various ways and then maximizing the consistency between the augmented versions of the graphs. Our approach significantly improves previous methods for unsupervised graph learning, and in some cases even outperforms supervised approaches that use labeled data.

Graph-Based Recommender Systems

Nowadays, recommender systems have become an indispensable tool for helping users navigate the overwhelming amount of information available online. Considering that users, items, and contexts within recommender systems are tightly connected, a natural question is whether graph structures can be helpful for mining users’ complex behaviors to enhance recommendations. Our work in this rigorous field provides affirmative answers to this question. Particularly, we develop the very first graph-based recommender systems for sequential session data, which captures the temporal dynamics of users’ interactions with items. In addition, we also apply our graph-based approach to other recommendation settings, such as multimedia recommendation, where users’ interactions with multimedia content can be modeled as a graph and used to improve the quality of recommendations.