Gradient boosted graph convolutional network on heterophilic graph
Graph Neural Networks (GNNs) are impressive models that have been highly successful in performing graphical analysis and learning. However, GNNs are known to be outstanding in learning from homophilic graphs but are subpar in learning from heterophilic graphs. On the other hand, Gradient Boosted Dec...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/176770 |
| _version_ | 1826112626394923008 |
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| author | Seah, Ming Yang |
| author2 | Tay Wee Peng |
| author_facet | Tay Wee Peng Seah, Ming Yang |
| author_sort | Seah, Ming Yang |
| collection | NTU |
| description | Graph Neural Networks (GNNs) are impressive models that have been highly successful in performing graphical analysis and learning. However, GNNs are known to be outstanding in learning from homophilic graphs but are subpar in learning from heterophilic graphs. On the other hand, Gradient Boosted Decision Trees (GBDTs) have become the best-performing model in dealing with heterogeneous tabular data. But will GBDTs retain that superiority when working with heterophilic graphs? This project proposes an alternative model to learn from heterophilic graphs, combining both GNNs and GBDTs. GBDTs will only focus on training the node features of the heterophilic graphs, passing the refined node features to the GNN to improve on the graph structure. After experimentation and comparison with GNN models, Graph Convolutional Network (GCN) in the case of this project, the proposed alternative model has shown a reasonable increase in performance in learning from heterophilic graphs. |
| first_indexed | 2024-10-01T03:10:11Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/176770 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:10:11Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1767702024-05-24T15:42:50Z Gradient boosted graph convolutional network on heterophilic graph Seah, Ming Yang Tay Wee Peng School of Electrical and Electronic Engineering wptay@ntu.edu.sg Engineering Gradient Boosting Convolutional Graph Neural Networks (GNNs) are impressive models that have been highly successful in performing graphical analysis and learning. However, GNNs are known to be outstanding in learning from homophilic graphs but are subpar in learning from heterophilic graphs. On the other hand, Gradient Boosted Decision Trees (GBDTs) have become the best-performing model in dealing with heterogeneous tabular data. But will GBDTs retain that superiority when working with heterophilic graphs? This project proposes an alternative model to learn from heterophilic graphs, combining both GNNs and GBDTs. GBDTs will only focus on training the node features of the heterophilic graphs, passing the refined node features to the GNN to improve on the graph structure. After experimentation and comparison with GNN models, Graph Convolutional Network (GCN) in the case of this project, the proposed alternative model has shown a reasonable increase in performance in learning from heterophilic graphs. Bachelor's degree 2024-05-20T01:09:27Z 2024-05-20T01:09:27Z 2024 Final Year Project (FYP) Seah, M. Y. (2024). Gradient boosted graph convolutional network on heterophilic graph. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176770 https://hdl.handle.net/10356/176770 en A3205-231 application/pdf Nanyang Technological University |
| spellingShingle | Engineering Gradient Boosting Convolutional Seah, Ming Yang Gradient boosted graph convolutional network on heterophilic graph |
| title | Gradient boosted graph convolutional network on heterophilic graph |
| title_full | Gradient boosted graph convolutional network on heterophilic graph |
| title_fullStr | Gradient boosted graph convolutional network on heterophilic graph |
| title_full_unstemmed | Gradient boosted graph convolutional network on heterophilic graph |
| title_short | Gradient boosted graph convolutional network on heterophilic graph |
| title_sort | gradient boosted graph convolutional network on heterophilic graph |
| topic | Engineering Gradient Boosting Convolutional |
| url | https://hdl.handle.net/10356/176770 |
| work_keys_str_mv | AT seahmingyang gradientboostedgraphconvolutionalnetworkonheterophilicgraph |