@inproceedings{52755d6789f14a899da260591d722e09,
title = "Graph Neural Networks-Based Multilabel Classification of Citation Network",
abstract = "There is an increasing number of applications where data can be represented as graphs. Besides, it is well-known that artificial intelligence approaches have become a very active and promising research field, mostly due to deep learning technologies. However popular deep learning architectures were designed to treat mostly image and text data. Graph Neural Network is the branch of machine learning which builds neural networks for graph data. In this context, many authors have recently proposed to adapt existing approaches to graphs and networks. In this paper we train three models of Graph Neural Networks on an academic citation network of Computer Science papers, and we explore the advantages of turning the problem into a multilabel classification problem.",
keywords = "Citation network, Graph neural networks, Multilabel classification",
author = "Guillaume Lachaud and Patricia Conde-Cespedes and Maria Trocan",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 14th Asian Conference on Intelligent Information and Database Systems , ACIIDS 2022 ; Conference date: 28-11-2022 Through 30-11-2022",
year = "2022",
month = jan,
day = "1",
doi = "10.1007/978-3-031-21967-2\_11",
language = "English",
isbn = "9783031219665",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "128--140",
editor = "Nguyen, \{Ngoc Thanh\} and Bogdan Trawi{\'n}ski and Nguyen, \{Ngoc Thanh\} and Tran, \{Tien Khoa\} and Ualsher Tukayev and Tzung-Pei Hong and Edward Szczerbicki",
booktitle = "Intelligent Information and Database Systems - 14th Asian Conference, ACIIDS 2022, Proceedings",
}