NS-CUK Seminar: V.T.Hoang, Review on "GOAT: A Global Transformer on Large-sca...ssuser4b1f48
ย
This document presents GOAT, a scalable global transformer model for graph-structured data. GOAT uses a novel local attention module to absorb rich local information from node neighborhoods, in addition to a global attention mechanism that allows each node to attend to all other nodes. The document reports that GOAT achieves strong performance on large-scale homophilous and heterophilous node classification benchmarks, demonstrating its ability to leverage both local and global graph information for prediction tasks. Ablation studies on codebook size further indicate GOAT's effectiveness at modeling long-range interactions through its global attention.
NS-CUK Seminar: H.B.Kim, Review on "Cluster-GCN: An Efficient Algorithm for ...ssuser4b1f48
ย
This document summarizes the Cluster-GCN method for training graph convolutional networks (GCNs) in a memory-efficient and scalable way. The key contributions of Cluster-GCN are that it achieves the best memory usage for training GCNs on large graphs, especially deep GCNs, while maintaining training speed comparable to or faster than existing methods. Experimental results demonstrate that Cluster-GCN can efficiently train very deep GCNs on large graphs and achieve state-of-the-art performance.
This document summarizes a research paper on Gate Graph Sequence Neural Networks (GGSNN). GGSNN is a model that incorporates time dependencies and higher-order relationships in graphs using GRU-based methods. It generates an output sequence to allow for graph-level analysis. The model can be used for a wide range of tasks involving logical formulas. It uses GRU to compute slopes via backpropagation over time, allowing it to capture long-term dependencies between output time steps. Node representations in GGSNN can be updated over time using label data, unlike previous graph neural networks.
NS-CUK Journal club: H.E.Lee, Review on " A biomedical knowledge graph-based ...ssuser4b1f48
ย
1) The document proposes a deep learning framework called DeepLGF to predict drug-drug interactions by combining local and global feature extraction from biomedical knowledge graphs.
2) DeepLGF uses graph neural networks and knowledge graph embedding methods to extract local drug features from chemical structures and biological functions, and global features from the relationships between drugs and other biological entities.
3) Experimental results on prediction tasks using several drug interaction datasets demonstrate that DeepLGF outperforms other state-of-the-art models and has promising applications in drug development and clinical use.
NS-CUK Seminar: H.B.Kim, Review on "Inductive Representation Learning on Lar...ssuser4b1f48
ย
1. The document summarizes the GraphSAGE framework for inductive node embedding proposed by Hamilton et al.
2. GraphSAGE leverages node features to learn an embedding function that generalizes to unseen nodes using a sample and aggregate approach.
3. Across citation, Reddit, and other datasets, GraphSAGE improves classification F1-scores by 51% on average compared to using node features alone and outperforms strong baselines.
NS-CUK Seminar: J.H.Lee, Review on "Relational Self-Supervised Learning on Gr...ssuser4b1f48
ย
This document proposes a new self-supervised learning framework called Relational Graph Representation Learning (RGRL). RGRL aims to learn node representations that preserve relationships between nodes even after augmentation. It does this by focusing training on low-degree nodes and using both global and local contexts to sample anchor nodes. Experiments on 14 real-world datasets show RGRL outperforms previous methods on tasks like node classification and link prediction.
NS-CUK Seminar: V.T.Hoang, Review on "GOAT: A Global Transformer on Large-sca...ssuser4b1f48
ย
This document presents GOAT, a scalable global transformer model for graph-structured data. GOAT uses a novel local attention module to absorb rich local information from node neighborhoods, in addition to a global attention mechanism that allows each node to attend to all other nodes. The document reports that GOAT achieves strong performance on large-scale homophilous and heterophilous node classification benchmarks, demonstrating its ability to leverage both local and global graph information for prediction tasks. Ablation studies on codebook size further indicate GOAT's effectiveness at modeling long-range interactions through its global attention.
NS-CUK Seminar: H.B.Kim, Review on "Cluster-GCN: An Efficient Algorithm for ...ssuser4b1f48
ย
This document summarizes the Cluster-GCN method for training graph convolutional networks (GCNs) in a memory-efficient and scalable way. The key contributions of Cluster-GCN are that it achieves the best memory usage for training GCNs on large graphs, especially deep GCNs, while maintaining training speed comparable to or faster than existing methods. Experimental results demonstrate that Cluster-GCN can efficiently train very deep GCNs on large graphs and achieve state-of-the-art performance.
This document summarizes a research paper on Gate Graph Sequence Neural Networks (GGSNN). GGSNN is a model that incorporates time dependencies and higher-order relationships in graphs using GRU-based methods. It generates an output sequence to allow for graph-level analysis. The model can be used for a wide range of tasks involving logical formulas. It uses GRU to compute slopes via backpropagation over time, allowing it to capture long-term dependencies between output time steps. Node representations in GGSNN can be updated over time using label data, unlike previous graph neural networks.
NS-CUK Journal club: H.E.Lee, Review on " A biomedical knowledge graph-based ...ssuser4b1f48
ย
1) The document proposes a deep learning framework called DeepLGF to predict drug-drug interactions by combining local and global feature extraction from biomedical knowledge graphs.
2) DeepLGF uses graph neural networks and knowledge graph embedding methods to extract local drug features from chemical structures and biological functions, and global features from the relationships between drugs and other biological entities.
3) Experimental results on prediction tasks using several drug interaction datasets demonstrate that DeepLGF outperforms other state-of-the-art models and has promising applications in drug development and clinical use.
NS-CUK Seminar: H.B.Kim, Review on "Inductive Representation Learning on Lar...ssuser4b1f48
ย
1. The document summarizes the GraphSAGE framework for inductive node embedding proposed by Hamilton et al.
2. GraphSAGE leverages node features to learn an embedding function that generalizes to unseen nodes using a sample and aggregate approach.
3. Across citation, Reddit, and other datasets, GraphSAGE improves classification F1-scores by 51% on average compared to using node features alone and outperforms strong baselines.
NS-CUK Seminar: J.H.Lee, Review on "Relational Self-Supervised Learning on Gr...ssuser4b1f48
ย
This document proposes a new self-supervised learning framework called Relational Graph Representation Learning (RGRL). RGRL aims to learn node representations that preserve relationships between nodes even after augmentation. It does this by focusing training on low-degree nodes and using both global and local contexts to sample anchor nodes. Experiments on 14 real-world datasets show RGRL outperforms previous methods on tasks like node classification and link prediction.