87 citations · 242 across the 13 of their papers we have counts for
13 papers
GraphPL: Leveraging GNN for Efficient and Robust Modalities Imputation in Patchwork Learning
Xingjian Hu, Zuoyu Yan, Jianhua Zhu +3
Current research on distributed multi-modal learning typically assumes that clients can access complete information across all modalities, which may not hold in practice. In this p…
HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering
Yuyu Liu, Sarang Rajendra Patil, Mengjia Xu +1
Electronic health record (EHR) question answering is often handled by LLM-based pipelines that are costly to deploy and do not explicitly leverage the hierarchical structure of cli…
IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research
Arpandeep Khatua, Vikram Sharma Mailthody, Bhagyashree Taleka +3
Graph neural networks (GNNs) have shown high potential for a variety of real-world, challenging applications, but one of the major obstacles in GNN research is the lack of large-sc…
When Does A Spectral Graph Neural Network Fail in Node Classification?
Zhixian Chen, Tengfei Ma, Yang Wang
Spectral Graph Neural Networks (GNNs) with various graph filters have received extensive affirmation due to their promising performance in graph learning problems. However, it is k…
Neural Approximation of Graph Topological Features
Zuoyu Yan, Tengfei Ma, Liangcai Gao +3
Topological features based on persistent homology capture high-order structural information so as to augment graph neural network methods. However, computing extended persistent ho…
Cycle Representation Learning for Inductive Relation Prediction
Zuoyu Yan, Tengfei Ma, Liangcai Gao +2
In recent years, algebraic topology and its modern development, the theory of persistent homology, has shown great potential in graph representation learning. In this paper, based…
Link Prediction with Persistent Homology: An Interactive View
Zuoyu Yan, Tengfei Ma, Liangcai Gao +2
Link prediction is an important learning task for graph-structured data. In this paper, we propose a novel topological approach to characterize interactions between two nodes. Our…
Wasserstein Graph Neural Networks for Graphs with Missing Attributes
Zhixian Chen, Tengfei Ma, Yangqiu Song +1
Missing node attributes is a common problem in real-world graphs. Graph neural networks have been demonstrated power in graph representation learning while their performance is aff…
BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
Zhixian Chen, Tengfei Ma, Zhihua Jin +2
Graph convolutional networks (GCNs) have achieved great success on graph-structured data. Many graph convolutional networks can be thought of as low-pass filters for graph signals.…
GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks
Zhihua Jin, Yong Wang, Qianwen Wang +3
Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in rece…
Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
Hanlu Wu, Tengfei Ma, Lingfei Wu +1
Crowdsourcing has attracted much attention for its convenience to collect labels from non-expert workers instead of experts. However, due to the high level of noise from the non-ex…
Deep Graph Matching and Searching for Semantic Code Retrieval
Xiang Ling, Lingfei Wu, Saizhuo Wang +6
Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description. Recent work mainly uses na…
Multilevel Graph Matching Networks for Deep Graph Similarity Learning
Xiang Ling, Lingfei Wu, Saizhuo Wang +5
While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph…