NS-CUK Seminar: V.T.Hoang, Review on "Everything is Connected: Graph Neural Networks," Current Opinion in Structural Biology, Mar 6th, 2023
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The 2nd NS-CUK Weekly Seminar Presenter: Van Thuy Hoang Date: Mar 6th, 2023 Topic: Review on "Everything is Connected: Graph Neural Networks," Current Opinion in Structural Biology Schedule: https://nslab-cuk.github.io/seminar/
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Time permitting I also plan on having a few slides on each of the following topics:
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https://www.linkedin.com/in/pierre-de-lacaze-b11026b/
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Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
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Technical Report on "Lecture Quality Prediction using Graph Neural Networks"
Technical Report on "Lecture Quality Prediction using Graph Neural Networks"Network Science Lab, The Catholic University of Korea
A technical report authored by Jooho Lee, Ho Beom Kim, and Hyo Eun Lee as a project outcome exhibited on the 2023 CUK Competition on Data Analytics.Presentation for "Lecture Quality Prediction using Graph Neural Networks"
Presentation for "Lecture Quality Prediction using Graph Neural Networks"Network Science Lab, The Catholic University of Korea
Presentation slides authored by Jooho Lee, Ho Beom Kim, and Hyo Eun Lee as a project outcome exhibited on the 2023 CUK Competition on Data Analytics.NS-CUK Seminar: J.H.Lee, Review on "Graph Neural Networks with convolutional ...
NS-CUK Seminar: J.H.Lee, Review on "Graph Neural Networks with convolutional ...Network Science Lab, The Catholic University of Korea
This document presents a novel graph neural network (GNN) convolutional layer based on Auto-Regressive Moving Average (ARMA) filters. The ARMA layer aims to address limitations of existing GNN layers that use polynomial filters by providing a more flexible frequency response with fewer parameters. It models graph signals using parallel stacks of recurrent operations to approximate high-order neighborhoods efficiently. Experimental results show the ARMA layer outperforms other GNN architectures on tasks like node classification, graph signal classification, and graph regression. Future work could explore incorporating text and content metadata into graph convolutional models.NS-CUK Seminar: V.T.Hoang, Review on "Are More Layers Beneficial to Graph Tra...
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The document proposes a novel graph transformer model called DeepGraph. DeepGraph uses substructure sampling to encode local graph information and add substructure tokens. It applies localized self-attention on substructures using a mask. The document experiments with DeepGraph on various graph datasets and analyzes its performance as its depth increases. Deeper models show diminishing returns, indicating a limitation of increasing depth in graph transformers.NS-CUK Seminar: S.T.Nguyen Review on "Accurate learning of graph representati...
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This document summarizes a research paper on Graph Multiset Pooling, a new method for graph pooling using a Graph Multiset Transformer (GMT). The GMT treats graph pooling as a multiset encoding problem and uses multi-head attention to capture relationships among nodes. It satisfies the injectiveness and permutation invariance properties needed to be as powerful as the Weisfeiler-Lehman graph isomorphism test. Experimental results show the GMT outperforms other pooling methods on tasks like graph classification, reconstruction, and generation. The GMT provides a powerful and efficient way to learn meaningful representations of entire graphs.Joo-Ho Lee: Topographic-aware wind forecasting system using multi-modal spati...
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NS-CUK Seminar: S.T.Nguyen, Review on "Do Transformers Really Perform Bad for...
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The 2nd NS-CUK Weekly Seminar
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The 1st AI-CUK Weekly Joint Journal Club
Presenter: Van Thuy Hoang
Date: Jan 3rd, 2023
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S.M.Lee, Invited Talk on "Machine Learning-based Anomaly Detection"
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Technical Report on "Lecture Quality Prediction using Graph Neural Networks"
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- 1. Hoang Van Thuy Network Science Lab E-mail: hoangvanthuy90@gmail.com
- 2. 1 • GFF, graph neural networks are trained greedily layer by layer, using both positive and negative samples • Solve the noise from neighbours
- 3. 2 The fundamentals: Permutation equivariance and invariance ➢ node feature matrix. ➢ adjacency matrix, A ➢ Neighbours of nodes ➢ Features ➢ Hidden state
- 4. 3 Graph Neural Networks ➢ Main Idea: Pass massages between pairs of nodes and agglomerate ➢ Alternative Interpretation: Pass massages between nodes to refine node (and possibly edge) representations
- 5. 4 Graph Neural Networks ➢ Convolutional
- 6. 5 Graph Neural Networks ➢ Attentional
- 7. 6 Graph Neural Networks ➢ Message-passing
- 8. 7 Graph Neural Networks ➢ Graph Convolutional Networks (GCNs) Kipf & Welling (ICLR 2017), related previous works by D uvenaud et al. (NIPS 2015) and Li et al. (ICLR 2016)
- 9. 8 Tasks ➢ Node classification. ➢ Graph classification. ➢ Link prediction
- 10. 9 GNNs without a graph: Deep Sets and Transformers ➢ Deep Sets
- 11. 10 GNNs without a graph: Deep Sets and Transformers ➢ To reverse-engineer why Transformers appear here, let us consider the NLP perspective.
- 12. 11 GNNs beyond permutation equivariance: Geometric Graphs ➢ We have assumed our graphs to be a discrete, unordered, collection of nodes and edges—hence, only susceptible to permutation symmetries. ➢ But in many cases, this is not the entire story!
- 13. 12