{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T04:05:15Z","timestamp":1749960315030,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819682973","type":"print"},{"value":"9789819682980","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-8298-0_3","type":"book-chapter","created":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T18:21:51Z","timestamp":1749925311000},"page":"29-40","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FOG: Interpretable Feature-Oriented Graph Neural Networks for\u00a0Tabular Data Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3857-6565","authenticated-orcid":false,"given":"Teng Yuan","family":"Tsou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6014-4191","authenticated-orcid":false,"given":"Pei-Xuan","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7024-2476","authenticated-orcid":false,"given":"Fandel","family":"Lin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6924-1337","authenticated-orcid":false,"given":"Hsun-Ping","family":"Hsieh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,15]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","unstructured":"Alkhatib, A., Ennadir, S., Bostr\u00f6m, H., Vazirgiannis, M.: Interpretable graph neural networks for tabular data (2024). https:\/\/doi.org\/10.48550\/arXiv.2308.08945","DOI":"10.48550\/arXiv.2308.08945"},{"key":"3_CR2","doi-asserted-by":"publisher","unstructured":"Arik, S.O., Pfister, T.: TabNet: attentive interpretable tabular learning (2020). https:\/\/doi.org\/10.48550\/arXiv.1908.07442","DOI":"10.48550\/arXiv.1908.07442"},{"issue":"6","key":"3_CR3","doi-asserted-by":"publisher","first-page":"7499","DOI":"10.1109\/TNNLS.2022.3229161","volume":"35","author":"V Borisov","year":"2024","unstructured":"Borisov, V., Leemann, T., Se\u00dfler, K., Haug, J., Pawelczyk, M., Kasneci, G.: Deep neural networks and tabular data: a survey. IEEE Trans. Neural Networks Learn. Syst. 35(6), 7499\u20137519 (2024). https:\/\/doi.org\/10.1109\/TNNLS.2022.3229161","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"1","key":"3_CR4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"key":"3_CR5","unstructured":"Chen, J., Song, L., Wainwright, M., Jordan, M.: Learning to explain: an information-theoretic perspective on model interpretation. In: Proceedings of the 35th International Conference on Machine Learning, pp. 883\u2013892. PMLR (2018)"},{"key":"3_CR6","unstructured":"Chen, K.Y., Chiang, P.H., Chou, H.R., Chen, T.W., Chang, D.T.H.: Trompt: towards a better deep neural network for tabular data. In: Proceedings of the 40th International Conference on Machine Learning. ICML\u201923, vol.\u00a0202, pp. 4392\u20134434. JMLR.org (2023)"},{"key":"3_CR7","doi-asserted-by":"publisher","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794. KDD \u201916, Association for Computing Machinery, New York, NY, USA (2016). https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"3_CR8","first-page":"16373","volume":"35","author":"K Du","year":"2022","unstructured":"Du, K., et al.: Learning enhanced representation for tabular data via neighborhood propagation. Adv. Neural. Inf. Process. Syst. 35, 16373\u201316384 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3_CR9","unstructured":"Fatemi, B., El\u00a0Asri, L., Kazemi, S.M.: SLAPS: self-supervision improves structure learning for graph neural networks. In: Advances in Neural Information Processing Systems, vol.\u00a034, pp. 22667\u201322681. Curran Associates, Inc. (2021)"},{"issue":"5","key":"3_CR10","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189\u20131232 (2001). https:\/\/doi.org\/10.1214\/aos\/1013203451","journal-title":"Ann. Stat."},{"key":"3_CR11","doi-asserted-by":"publisher","unstructured":"Gorishniy, Y., Rubachev, I., Khrulkov, V., Babenko, A.: Revisiting Deep Learning Models for Tabular Data (2023). https:\/\/doi.org\/10.48550\/arXiv.2106.11959","DOI":"10.48550\/arXiv.2106.11959"},{"key":"3_CR12","doi-asserted-by":"publisher","unstructured":"Grinsztajn, L., Oyallon, E., Varoquaux, G.: Why do tree-based models still outperform deep learning on tabular data? (2022). https:\/\/doi.org\/10.48550\/arXiv.2207.08815","DOI":"10.48550\/arXiv.2207.08815"},{"key":"3_CR13","doi-asserted-by":"publisher","unstructured":"Guo, X., Quan, Y., Zhao, H., Yao, Q., Li, Y., Tu, W.: TabGNN: multiplex graph neural network for tabular data prediction (2021). https:\/\/doi.org\/10.48550\/arXiv.2108.09127","DOI":"10.48550\/arXiv.2108.09127"},{"key":"3_CR14","unstructured":"Kipf, T., Welling, M.: Semi-supervised classification with graph convolutional networks. ArXiv (2016)"},{"key":"3_CR15","doi-asserted-by":"publisher","unstructured":"Liao, J.C., Li, C.T.: TabGSL: graph structure learning for tabular data prediction (2023). https:\/\/doi.org\/10.48550\/arXiv.2305.15843","DOI":"10.48550\/arXiv.2305.15843"},{"key":"3_CR16","unstructured":"Louppe, G., Wehenkel, L., Sutera, A., Geurts, P.: Understanding variable importances in forests of randomized trees. In: Advances in Neural Information Processing Systems, vol.\u00a026. Curran Associates, Inc. (2013)"},{"key":"3_CR17","doi-asserted-by":"publisher","unstructured":"Somepalli, G., Goldblum, M., Schwarzschild, A., Bruss, C.B., Goldstein, T.: SAINT: improved neural networks for tabular data via row attention and contrastive pre-training (2021). https:\/\/doi.org\/10.48550\/arXiv.2106.01342","DOI":"10.48550\/arXiv.2106.01342"},{"key":"3_CR18","unstructured":"Telyatnikov, L., Scardapane, S.: EGG-GAE: scalable graph neural networks for tabular data imputation. In: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, pp. 2661\u20132676. PMLR (2023)"},{"issue":"4","key":"3_CR19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0301541","volume":"19","author":"S Uddin","year":"2024","unstructured":"Uddin, S., Lu, H.: Confirming the statistically significant superiority of tree-based machine learning algorithms over their counterparts for tabular data. PLoS ONE 19(4), e0301541 (2024). https:\/\/doi.org\/10.1371\/journal.pone.0301541","journal-title":"PLoS ONE"},{"key":"3_CR20","doi-asserted-by":"publisher","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph Attention Networks (2018). https:\/\/doi.org\/10.48550\/arXiv.1710.10903","DOI":"10.48550\/arXiv.1710.10903"},{"key":"3_CR21","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1007\/978-3-031-20984-0_44","volume-title":"Service-Oriented Computing","author":"J Wu","year":"2022","unstructured":"Wu, J., Hu, R., Li, D., Ren, L., Hu, W., Zang, Y.: IDGL: an imbalanced disassortative graph learning framework for fraud detection. In: Troya, J., Medjahed, B., Piattini, M., Yao, L., Fern\u00e1ndez, P., Ruiz-Cort\u00e9s, A. (eds.) Service-Oriented Computing, pp. 616\u2013631. Springer Nature Switzerland, Cham (2022)"},{"issue":"5","key":"3_CR22","doi-asserted-by":"publisher","first-page":"1746","DOI":"10.4208\/cicp.OA-2020-0085","volume":"28","author":"Z Xu","year":"2020","unstructured":"Xu, Z.: Frequency principle: fourier analysis sheds light on deep neural networks. Commun. Comput. Phys. 28(5), 1746\u20131767 (2020). https:\/\/doi.org\/10.4208\/cicp.OA-2020-0085","journal-title":"Commun. Comput. Phys."},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Yan, J., Chen, J., Wu, Y., Chen, D.Z., Wu, J.: T2G-FORMER: organizing tabular features into relation graphs promotes heterogeneous feature interaction. In: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI\u201923, vol.\u00a037, pp. 10720\u201310728 (2023)","DOI":"10.1609\/aaai.v37i9.26272"},{"key":"3_CR24","doi-asserted-by":"publisher","unstructured":"Ye, A., Wang, Z.: Tree-based deep learning approaches. In: Ye, A., Wang, Z. (eds.) Modern Deep Learning for Tabular Data: Novel Approaches to Common Modeling Problems, pp. 549\u2013598. Apress, Berkeley, CA (2023). https:\/\/doi.org\/10.1007\/978-1-4842-8692-0_7","DOI":"10.1007\/978-1-4842-8692-0_7"}],"container-title":["Lecture Notes in Computer Science","Data Science: Foundations and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-8298-0_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T19:02:05Z","timestamp":1749927725000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-8298-0_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819682973","9789819682980"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-8298-0_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"15 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}