Abstract: Many practical problems can be formulated as graph-based semi-supervised classification problems. For example, online finance anti-fraud. Recently, many researchers attempt using deep ...
Abstract: In 3D human pose estimation, human joints typically exhibit continuity and interdependence during motion, suggesting that joint velocities and their positional relationships can offer ...
Quantum Variational Graph Auto-Encoders (QVGAE) represent an integration of graph-based machine learning and quantum computing. In this work, we propose a first-of-its-kind quantum implementation of ...
\textit{Graph neural networks} (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and ...
You will need to use a Python version between 3.9 (inclusive) and 3.13 (exclusive) to run the scripts and notebooks. We use Poetry to set up our Python environment as ...
Implementation of Graph Encoder Embedding in the Ligra framework. This speeds up the original implementation by 10-100x, making Graph Encoder Embedding a very useful tool for learning on Graphs. It ...
In this paper, we introduce the concept of principal communities and propose a principal graph encoder embedding method that concurrently detects these communities and achieves vertex embedding. Given ...
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