3 citations · 3 across the 2 of their papers we have counts for
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cs.LG2023
A Graph Encoder-Decoder Network for Unsupervised Anomaly Detection
Mahsa Mesgaran, A. Ben Hamza
A key component of many graph neural networks (GNNs) is the pooling operation, which seeks to reduce the size of a graph while preserving important structural information. However,…
cs.LG2020
Anisotropic Graph Convolutional Network for Semi-supervised Learning
Mahsa Mesgaran, A. Ben Hamza
Graph convolutional networks learn effective node embeddings that have proven to be useful in achieving high-accuracy prediction results in semi-supervised learning tasks, such as…
cs.LG2020★ 3 cited
Graph Fairing Convolutional Networks for Anomaly Detection
Mahsa Mesgaran, A. Ben Hamza
Graph convolution is a fundamental building block for many deep neural networks on graph-structured data. In this paper, we introduce a simple, yet very effective graph convolution…