Publications (10)
Optimal Propagation for Graph Neural Networks
Beidi Zhao, Boxin Du, Zhe Xu +2
Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input. However, the initial input graph…
KompaRe: A Knowledge Graph Comparative Reasoning System
Lihui Liu, Boxin Du, Heng Ji +1
Reasoning is a fundamental capability for harnessing valuable insight, knowledge and patterns from knowledge graphs. Existing work has primarily been focusing on point-wise reasoni…
Enhancing Multimodal Large Language Models with Multi-instance Visual Prompt Generator for Visual Representation Enrichment
Wenliang Zhong, Wenyi Wu, Qi Li +6
Multimodal Large Language Models (MLLMs) have achieved SOTA performance in various visual language tasks by fusing the visual representations with LLMs leveraging some visual adapt…
Hypergraph Pre-training with Graph Neural Networks
Boxin Du, Changhe Yuan, Robert Barton +2
Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus…
SUGER: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation
Zhenning Zhang, Boxin Du, Hanghang Tong
Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users. Although Graph Neural Networ…
Graph Sanitation with Application to Node Classification
Zhe Xu, Boxin Du, Hanghang Tong
The past decades have witnessed the prosperity of graph mining, with a multitude of sophisticated models and algorithms designed for various mining tasks, such as ranking, classifi…
Conversational Question Answering with Reformulations over Knowledge Graph
Lihui Liu, Blaine Hill, Boxin Du +2
Conversational question answering (convQA) over knowledge graphs (KGs) involves answering multi-turn natural language questions about information contained in a KG. State-of-the-ar…
Hierarchical Multi-Marginal Optimal Transport for Network Alignment
Zhichen Zeng, Boxin Du, Si Zhang +3
Finding node correspondence across networks, namely multi-network alignment, is an essential prerequisite for joint learning on multiple networks. Despite great success in aligning…
Neural Multi-network Diffusion towards Social Recommendation
Boxin Du, Lihui Liu, Jiejun Xu +2
Graph Neural Networks (GNNs) have been widely applied on a variety of real-world applications, such as social recommendation. However, existing GNN-based models on social recommend…
Geometric Matrix Completion via Sylvester Multi-Graph Neural Network
Boxin Du, Changhe Yuan, Fei Wang +1
Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exist…