Bowen Du

Label-Enhanced Graph Neural Network for Semi-supervised Node Classification

2022 · arXiv (Cornell University)

Graph Neural Networks (GNNs) have been widely applied in the semi-supervised node classification task, where a key point lies in how to sufficiently leverage the limited but valuable label information. Most of the classical GNNs …

Repeat-Aware Neighbor Sampling for Dynamic Graph Learning

2024 · arXiv (Cornell University)

Dynamic graph learning equips the edges with time attributes and allows multiple links between two nodes, which is a crucial technology for understanding evolving data scenarios like traffic prediction and recommendation systems. Existing works obtain …

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 …

S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension

2017 · arXiv (Cornell University)

In this paper, we present a novel approach to machine reading comprehension for the MS-MARCO dataset. Unlike the SQuAD dataset that aims to answer a question with exact text spans in a passage, the MS-MARCO …