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One of the important applications of GNN is in the semi-supervised node classification task. The accuracy of the node classification using GNN depends on (i) the number and (ii) the choice of the training nodes. In this article, we demonstrate that increasing the training nodes by selecting nodes from the same class that are spread out across non-contiguous subgraphs, can significantly improve the accuracy. We accomplish this by presenting a novel input intervention technique that can be used in conjunction with different GNN classification methods to increase the non-contiguous training nodes and, thereby, improve the accuracy. We also present an output intervention technique to identify misclassified nodes and relabel them with their potentially correct labels. We demonstrate on real-world networks that our proposed methods, both individually and collectively, significantly improve the accuracy in comparison to the baseline GNN algorithms. Both our methods are agnostic. Apart from the initial set of training nodes generated by the baseline GNN methods, our techniques do not need any other extra knowledge about the classes of the nodes. Thus, our methods are modular and can be used as pre-and post-processing steps with many of the currently available GNN methods to improve their accuracy.<\/jats:p>","DOI":"10.1145\/3610535","type":"journal-article","created":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T08:46:09Z","timestamp":1690015569000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Improving Node Classification Accuracy of GNN through Input and Output Intervention"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1056-1568","authenticated-orcid":false,"given":"Anjan","family":"Chowdhury","sequence":"first","affiliation":[{"name":"Indian Statistical Institute, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0085-309X","authenticated-orcid":false,"given":"Sriram","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"Bowie State University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4534-0044","authenticated-orcid":false,"given":"Animesh","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"IIT Kharagpur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8550-5371","authenticated-orcid":false,"given":"Sanjukta","family":"Bhowmick","sequence":"additional","affiliation":[{"name":"University of North Texas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4431-1404","authenticated-orcid":false,"given":"Kuntal","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Indian Statistical Institute, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,9,6]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"21","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Abu-El-Haija Sami","year":"2019","unstructured":"Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019. 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