Node Classification on The Citation Network Using Graph Neural Network
Research on Graph Neural Networks has influenced various current real-world problems. The graph-based approach is considered capable of effectively representing the actual state of surrounding data by utilizing nodes, edges, and features. Consider the feedforward neural network and the graph neural...
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Universitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian Masyarakat
2023-06-01
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| Series: | Inspiration |
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| Online Access: | https://ojs.unitama.ac.id/index.php/inspiration/article/view/49 |
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| author | Irani Hoeronis Bambang Riyanto Trilaksono |
| author_facet | Irani Hoeronis Bambang Riyanto Trilaksono |
| author_sort | Irani Hoeronis |
| collection | DOAJ |
| description | Research on Graph Neural Networks has influenced various current real-world problems. The graph-based approach is considered capable of effectively representing the actual state of surrounding data by utilizing nodes, edges, and features. Consider the feedforward neural network and the graph neural network approaches, we determine the accuracy of each method. In the baseline experiment, training and testing were performed using the NN approach. The resulting accuracy of FNN was 72.59 % and GNN model has increased by 81.65 %. There is a 9.06 % increase in accuracy between the baseline model and the GNN model. The new data utilized in the model predictions showcases the probabilities of each class through randomly generated examples. |
| format | Article |
| id | doaj-art-88df936434be4c9ebd2df68051d9e9b1 |
| institution | Kabale University |
| issn | 2088-6705 2621-5608 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Universitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian Masyarakat |
| record_format | Article |
| series | Inspiration |
| spelling | doaj-art-88df936434be4c9ebd2df68051d9e9b12025-01-28T05:36:06ZengUniversitas Teknologi Akba Makassar, Lembaga Penelitian dan Pengabdian MasyarakatInspiration2088-67052621-56082023-06-011319610510.35585/inspir.v13i1.4949Node Classification on The Citation Network Using Graph Neural NetworkIrani Hoeronis0Bambang Riyanto Trilaksono1Universitas SiliwangiBandung Institute of TechnologyResearch on Graph Neural Networks has influenced various current real-world problems. The graph-based approach is considered capable of effectively representing the actual state of surrounding data by utilizing nodes, edges, and features. Consider the feedforward neural network and the graph neural network approaches, we determine the accuracy of each method. In the baseline experiment, training and testing were performed using the NN approach. The resulting accuracy of FNN was 72.59 % and GNN model has increased by 81.65 %. There is a 9.06 % increase in accuracy between the baseline model and the GNN model. The new data utilized in the model predictions showcases the probabilities of each class through randomly generated examples.https://ojs.unitama.ac.id/index.php/inspiration/article/view/49graph neural networkfeedforward neural networknode classification |
| spellingShingle | Irani Hoeronis Bambang Riyanto Trilaksono Node Classification on The Citation Network Using Graph Neural Network Inspiration graph neural network feedforward neural network node classification |
| title | Node Classification on The Citation Network Using Graph Neural Network |
| title_full | Node Classification on The Citation Network Using Graph Neural Network |
| title_fullStr | Node Classification on The Citation Network Using Graph Neural Network |
| title_full_unstemmed | Node Classification on The Citation Network Using Graph Neural Network |
| title_short | Node Classification on The Citation Network Using Graph Neural Network |
| title_sort | node classification on the citation network using graph neural network |
| topic | graph neural network feedforward neural network node classification |
| url | https://ojs.unitama.ac.id/index.php/inspiration/article/view/49 |
| work_keys_str_mv | AT iranihoeronis nodeclassificationonthecitationnetworkusinggraphneuralnetwork AT bambangriyantotrilaksono nodeclassificationonthecitationnetworkusinggraphneuralnetwork |