23 citations · 47 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
Enhancing Graph Representation Learning with Localized Topological Features
Zuoyu Yan, Qi Zhao, Ze Ye +5
Representation learning on graphs is a fundamental problem that can be crucial in various tasks. Graph neural networks, the dominant approach for graph representation learning, are…
Cycle Invariant Positional Encoding for Graph Representation Learning
Zuoyu Yan, Tengfei Ma, Liangcai Gao +3
Cycles are fundamental elements in graph-structured data and have demonstrated their effectiveness in enhancing graph learning models. To encode such information into a graph learn…
An Efficient Subgraph GNN with Provable Substructure Counting Power
Zuoyu Yan, Junru Zhou, Liangcai Gao +2
We investigate the enhancement of graph neural networks' (GNNs) representation power through their ability in substructure counting. Recent advances have seen the adoption of subgr…
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…