Longitudinal Citation Prediction using Temporal Graph Neural Networks

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research

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Longitudinal Citation Prediction using Temporal Graph Neural Networks. / Holm, Andreas Nugaard; Plank, Barbara ; Wright, Dustin; Augenstein, Isabelle.

Proceedings of the Workshop on Scientific Document Understanding co-located with 36th AAAI Conference on Artificial Inteligence (AAAI 2022). CEUR, 2022. (CEUR Workshop Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research

Harvard

Holm, AN, Plank, B, Wright, D & Augenstein, I 2022, Longitudinal Citation Prediction using Temporal Graph Neural Networks. in Proceedings of the Workshop on Scientific Document Understanding co-located with 36th AAAI Conference on Artificial Inteligence (AAAI 2022). CEUR, CEUR Workshop Proceedings, 36th AAAI Conference on Artificial Intelligence (AAAI-22) / The second workshop on advances, resources, tools and challenges of scientific document understanding, Online, 01/03/2022.

APA

Holm, A. N., Plank, B., Wright, D., & Augenstein, I. (2022). Longitudinal Citation Prediction using Temporal Graph Neural Networks. In Proceedings of the Workshop on Scientific Document Understanding co-located with 36th AAAI Conference on Artificial Inteligence (AAAI 2022) CEUR. CEUR Workshop Proceedings

Vancouver

Holm AN, Plank B, Wright D, Augenstein I. Longitudinal Citation Prediction using Temporal Graph Neural Networks. In Proceedings of the Workshop on Scientific Document Understanding co-located with 36th AAAI Conference on Artificial Inteligence (AAAI 2022). CEUR. 2022. (CEUR Workshop Proceedings).

Author

Holm, Andreas Nugaard ; Plank, Barbara ; Wright, Dustin ; Augenstein, Isabelle. / Longitudinal Citation Prediction using Temporal Graph Neural Networks. Proceedings of the Workshop on Scientific Document Understanding co-located with 36th AAAI Conference on Artificial Inteligence (AAAI 2022). CEUR, 2022. (CEUR Workshop Proceedings).

Bibtex

@inproceedings{3218cf0931ae4e42baf37baa299f4d6b,
title = "Longitudinal Citation Prediction using Temporal Graph Neural Networks",
abstract = "Citation count prediction is the task of predicting the number of citations a paper has gained after a period of time. Prior work viewed this as a static prediction task. As papers and their citations evolve over time, considering the dynamics of the number of citations a paper will receive would seem logical. Here, we introduce the task of sequence citation prediction, where the goal is to accurately predict the trajectory of the number of citations a scholarly work receives over time. We propose to view papers as a structured network of citations, allowing us to use topological information as a learning signal. Additionally, we learn how this dynamic citation network changes over time and the impact of paper meta-data such as authors, venues and abstracts. To approach the introduced task, we derive a dynamic citation network from Semantic Scholar which spans over 42 years. We present a model which exploits topological and temporal information using graph convolution networks paired with sequence prediction, and compare it against multiple baselines, testing the importance of topological and temporal information and analyzing model performance. Our experiments show that leveraging both the temporal and topological information greatly increases the performance of predicting citation counts over time. ",
author = "Holm, {Andreas Nugaard} and Barbara Plank and Dustin Wright and Isabelle Augenstein",
year = "2022",
language = "English",
series = "CEUR Workshop Proceedings",
publisher = "CEUR",
booktitle = "Proceedings of the Workshop on Scientific Document Understanding co-located with 36th AAAI Conference on Artificial Inteligence (AAAI 2022)",
note = "36th AAAI Conference on Artificial Intelligence (AAAI-22) / The second workshop on advances, resources, tools and challenges of scientific document understanding ; Conference date: 01-03-2022",

}

RIS

TY - GEN

T1 - Longitudinal Citation Prediction using Temporal Graph Neural Networks

AU - Holm, Andreas Nugaard

AU - Plank, Barbara

AU - Wright, Dustin

AU - Augenstein, Isabelle

PY - 2022

Y1 - 2022

N2 - Citation count prediction is the task of predicting the number of citations a paper has gained after a period of time. Prior work viewed this as a static prediction task. As papers and their citations evolve over time, considering the dynamics of the number of citations a paper will receive would seem logical. Here, we introduce the task of sequence citation prediction, where the goal is to accurately predict the trajectory of the number of citations a scholarly work receives over time. We propose to view papers as a structured network of citations, allowing us to use topological information as a learning signal. Additionally, we learn how this dynamic citation network changes over time and the impact of paper meta-data such as authors, venues and abstracts. To approach the introduced task, we derive a dynamic citation network from Semantic Scholar which spans over 42 years. We present a model which exploits topological and temporal information using graph convolution networks paired with sequence prediction, and compare it against multiple baselines, testing the importance of topological and temporal information and analyzing model performance. Our experiments show that leveraging both the temporal and topological information greatly increases the performance of predicting citation counts over time.

AB - Citation count prediction is the task of predicting the number of citations a paper has gained after a period of time. Prior work viewed this as a static prediction task. As papers and their citations evolve over time, considering the dynamics of the number of citations a paper will receive would seem logical. Here, we introduce the task of sequence citation prediction, where the goal is to accurately predict the trajectory of the number of citations a scholarly work receives over time. We propose to view papers as a structured network of citations, allowing us to use topological information as a learning signal. Additionally, we learn how this dynamic citation network changes over time and the impact of paper meta-data such as authors, venues and abstracts. To approach the introduced task, we derive a dynamic citation network from Semantic Scholar which spans over 42 years. We present a model which exploits topological and temporal information using graph convolution networks paired with sequence prediction, and compare it against multiple baselines, testing the importance of topological and temporal information and analyzing model performance. Our experiments show that leveraging both the temporal and topological information greatly increases the performance of predicting citation counts over time.

M3 - Article in proceedings

T3 - CEUR Workshop Proceedings

BT - Proceedings of the Workshop on Scientific Document Understanding co-located with 36th AAAI Conference on Artificial Inteligence (AAAI 2022)

PB - CEUR

T2 - 36th AAAI Conference on Artificial Intelligence (AAAI-22) / The second workshop on advances, resources, tools and challenges of scientific document understanding

Y2 - 1 March 2022

ER -

ID: 339338759