Pengyang Wang

Semi-supervised Drifted Stream Learning with Short Lookback

2022 · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

In many scenarios, 1) data streams are generated in real time; 2) labeled data are expensive and only limited labels are available in the beginning; 3) real-world data is not always i.i.d. and data drift …

Schema-Aware Hyper-Relational Knowledge Graph Embeddings for Link Prediction

2023 · IEEE Transactions on Knowledge and Data Engineering

Knowledge Graph (KG) embeddings have become a powerful paradigm to resolve link prediction tasks for KG completion. The widely adopted triple-based representation, where each triplet$(h,r,t)$links two entities$h$and$t$through a relation$r$, oversimplifies the complex nature of the …

DyGKT: Dynamic Graph Learning for Knowledge Tracing

2024

Knowledge Tracing aims to assess student learning states by predicting their performance in answering questions. Different from the existing research which utilizes fixed-length learning sequence to obtain the student states and regards KT as a …

Deep Cut-informed Graph Embedding and Clustering

2025 · arXiv (Cornell University)

Graph clustering aims to divide the graph into different clusters. The recently emerging deep graph clustering approaches are largely built on graph neural networks (GNN). However, GNN is designed for general graph encoding and there …