Ziyan Zhang

RobustGCNs: Robust Norm Graph Convolutional Networks in the Presence of Node Missing Data and Large Noises

2020 · arXiv (Cornell University)

Graph Convolutional Networks (GCNs) have been widely studied for attribute graph data representation and learning. In many applications, graph node attribute/feature may contain various kinds of noises, such as gross corruption, outliers and missing values. …

Study on Reconstruction and Feature Tracking of Silicone Heart 3D Surface

2021 · Sensors

At present, feature-based 3D reconstruction and tracking technology is widely applied in the medical field. In minimally invasive surgery, the surgeon can achieve three-dimensional reconstruction through the images obtained by the endoscope in the human …

Robust Graph Data Learning via Latent Graph Convolutional Representation

2019 · arXiv (Cornell University)

Graph Convolutional Representation (GCR) has achieved impressive performance for graph data representation. However, existing GCR is generally defined on the input fixed graph which may restrict the representation capacity and also be vulnerable to the …

Graph Edge Representation via Tensor Product Graph Convolutional Representation

2024 · arXiv (Cornell University)

Graph Convolutional Networks (GCNs) have been widely studied. The core of GCNs is the definition of convolution operators on graphs. However, existing Graph Convolution (GC) operators are mainly defined on adjacency matrix and node features …

Leveraging SDN Control for Time-Precise Quantum Position Verification

2025

With ongoing improvements in quantum network hardware, the demand for practical applications continues to rise. Quantum Position Verification (QPV), which leverages geographic location as an authentication token, has been identified as a critical application. However, …