A fundamental challenge of bipartite graph representation learning is how to extract
informative node embeddings. Self-Supervised Learning (SSL) is a promising paradigm to …

Learnersourcing offers great potential for scalable education through student content
creation. However, predicting student performance on learnersourced questions, which is …

Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns in real-
world signed graphs containing positive and negative links. However, three key challenges …

Signed graphs are valuable for modeling complex relationships with positive and negative
connections, and Signed Graph Neural Networks (SGNNs) have become crucial tools for …

In online education platforms, accurately predicting student performance is essential for
timely dropout prevention and interventions for at-risk students. This task is made difficult by …

The graph-based recommendation has achieved great success in recent years. However,
most existing graph-based recommendations focus on capturing user preference based on …

In recommender systems, most graph-based methods focus on positive user feedback, while
overlooking the valuable negative feedback. Integrating both positive and negative feedback …

Recently, sign-aware graph recommendation has drawn much attention as it will learn users'
negative preferences besides positive ones from both positive and negative interactions (ie …

With the prevalence of social media, the connectedness between people has been greatly
enhanced. Real-world relations between users on social media are often not limited to …

Node classification on heterogeneous graphs with multiple node types and relationships
has been widely used in commodity classification, friend recommendation, and so on. Most …