@inproceedings{3f860a981b454a83a54f1c915cd892a9,
title = "E2EG: End-to-End Node Classification Using Graph Topology and Text-based Node Attributes",
abstract = "Node classification utilizing text-based node attributes has many real-world applications, ranging from prediction of paper topics in academic citation graphs to classification of user characteristics in social media networks. State-of-the-art node classification frameworks, such as GIANT, use a two-stage pipeline: first embedding the text attributes of graph nodes then feeding the resulting embeddings into a node classification model. In this paper, we eliminate these two stages and develop an end-to-end node classification model that builds upon GIANT, called End-to-End-GIANT (E2EG). The tandem utilization of a main and an auxiliary classification objectives in our approach results in a more robust model, enabling the BERT backbone to be switched out for a distilled encoder with a 25% - 40% reduction in the number of parameters. Moreover, the model's end-to-end nature increases ease of use, as it avoids the need of chaining multiple models for node classification. Compared to a GIANT+MLP baseline on the ogbn-arxiv and ogbn-products datasets, E2EG obtains slightly better accuracy in the transductive setting (+0.5%), while reducing model training time by up to 40%. Our model is also applicable in the inductive setting, outperforming GIANT+MLP by up to +2.23%.",
keywords = "end-to-end, multi-task learning, node classification, transfer learning",
author = "Dinh, {Tu Anh} and {Den Boef}, Jeroen and Joran Cornelisse and Paul Groth",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023 ; Conference date: 01-12-2023 Through 04-12-2023",
year = "2023",
doi = "10.1109/ICDMW60847.2023.00142",
language = "Ingl{\'e}s",
series = "IEEE International Conference on Data Mining Workshops, ICDMW",
publisher = "IEEE Computer Society",
pages = "1084--1091",
editor = "Jihe Wang and Yi He and Dinh, {Thang N.} and Christan Grant and Meikang Qiu and Witold Pedrycz",
booktitle = "Proceedings - 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023",
address = "Estados Unidos",
}