skip to main content
10.1145/3459637.3482490acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article

VPALG: Paper-publication Prediction with Graph Neural Networks

Published: 30 October 2021 Publication History

Abstract

Paper-publication venue prediction aims to predict candidate publication venues that effectively suit given submissions. This technology is developing rapidly with the popularity of machine learning models. However, most previous methods ignore the structure information of papers, while modeling them with graphs can naturally solve this drawback. Meanwhile, they either use hand-crafted or bag-of-word features to represent the papers, ignoring the ones that involve high-level semantics. Moreover, existing methods assume that the venue where a paper is published as a correct venue for the data annotation, which is unrealistic. One paper can be relevant to many venues. In this paper, we attempt to address these problems above and develop a novel prediction model, namelyVenue Prediction with Abstract-Level Graph (Vpalg xspace), which can serve as an effective decision-making tool for venue selections. Specifically, to achieve more discriminative paper abstract representations, we construct each abstract as a semantic graph and perform a dual attention message passing neural network for representation learning. Then, the proposed model can be trained over the learned abstract representations with their labels and generalized via self-training. Empirically, we employ the PubMed dataset and further collect two new datasets from the top journals and conferences in computer science. Experimental results indicate the superior performance of Vpalg xspace, consistently outperforming the existing baseline methods.

Supplementary Material

MP4 File (CIKM21-VPALG.mp4)
Presentation video

References

[1]
Hamed Alhoori and Richard Furuta. 2017. Recommendation of scholarly venues based on dynamic user interests. Journal of Informetrics 11, 2 (2017), 553--563.
[2]
Joeran Beel, Bela Gipp, Stefan Langer, and Corinna Breitinger. 2016. Paper rec-ommender systems: a literature survey. International Journal on Digital Libraries 17, 4 (2016), 305--338.
[3]
David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research 3, Jan (2003), 993--1022.
[4]
Zhen Chen, Feng Xia, Huizhen Jiang, Haifeng Liu, and Jun Zhang. 2015. AVER: Random walk based academic venue recommendation. In International Conference on World Wide Web.
[5]
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolu-tional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems.
[6]
Zhongfen Deng, Hao Peng, Congying Xia, Jianxin Li, Lifang He, and Philip S. Yu. 2020. Hierarchical Bi-Directional Self-Attention Networks for Paper Re-view Rating Recommendation. In International Conference on Computational Linguistics.
[7]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[8]
Xiaoyue Feng, Hao Zhang, Yijie Ren, Penghui Shang, Yi Zhu, Yanchun Liang, Renchu Guan, and Dong Xu. 2019. The Deep Learning--Based Recommender System ?Pubmender" for Choosing a Biomedical Publication Venue: Development and Validation Study. Journal of Medical Internet Research 21, 5 (2019), e12957.
[9]
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural message passing for Quantum chemistry. In International Conference on Machine Learning.
[10]
Takuo Hamaguchi, Hidekazu Oiwa, Masashi Shimbo, and Yuji Matsumoto. 2017. Knowledge transfer for out-of-knowledge-base entities: a graph neural network approach. In International Joint Conference on Artificial Intelligence.
[11]
William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive represen-tation learning on large graphs. In Advances in Neural Information Processing Systems.
[12]
Mikael Henaff, Joan Bruna, and Yann LeCun. 2015. Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163 (2015).
[13]
Mohammad Hossin and MN Sulaiman. 2015. A review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process 5 (2015), 1.
[14]
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017. Bag of Tricks for Efficient Text Classification. In Conference of the European Chapter of the Association for Computational Linguistics.
[15]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Opti-mization. In International Conference on Learning Representations.
[16]
Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Repre-sentations.
[17]
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann. 2019. Pre-dict then Propagate: Graph Neural Networks meet Personalized PageRank. In International Conference on Learning Representations.
[18]
Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In International Conference on Machine Learning.
[19]
Zhouhan Lin, Minwei Feng, Cícero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017. A Structured Self-Attentive Sentence Embedding. In International Conference on Learning Representations.
[20]
Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2016. Recurrent Neural Network for Text Classification with Multi-Task Learning. In International Joint Conference on Artificial Intelligence.
[21]
Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 11 (2008), 2579--2605.
[22]
Eric Medvet, Alberto Bartoli, and Giulio Piccinin. 2014. Publication venue rec-ommendation based on paper abstract. In International Conference on Tools with Artificial Intelligence.
[23]
Michael T Mills and Nikolaos G Bourbakis. 2013. Graph-based methods for natural language processing and understanding-a survey and analysis. IEEE Transactions on Systems, Man, and Cybernetics: Systems 44, 1 (2013), 59--71.
[24]
Giannis Nikolentzos, Antoine J.-P. Tixier, and Michalis Vazirgiannis. 2020. Mes-sage Passing Attention Networks for Document Understanding. In AAAI Confer-ence on Artificial Intelligence.
[25]
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for word representation. In Conference on Empirical Methods in Natural Language Processing.
[26]
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep Contextualized Word Representa-tions. In North American Chapter of the Association for Computational Linguistics.
[27]
Jay M Ponte and W Bruce Croft. 1998. A language modeling approach to in-formation retrieval. In ACM SIGIR Conference on Research and Development in Information Retrieval.
[28]
Tribikram Pradhan, Abhinav Gupta, and Sukomal Pal. 2020. HASVRec: A mod-ularized Hierarchical Attention-based Scholarly Venue Recommender system. Knowledge-Based Systems 204 (2020), 106181.
[29]
Tribikram Pradhan and Sukomal Pal. 2020. CNAVER: A content and network-based academic venue recommender system. Knowledge-Based Systems 189 (2020), 105092.
[30]
Tobias Schnabel, Igor Labutov, David Mimno, and Thorsten Joachims. 2015. Eval-uation methods for unsupervised word embeddings. In Conference on Empirical Methods in Natural Language Processing.
[31]
Weiwei Shi, Yihong Gong, Chris Ding, Zhiheng MaXiaoyu Tao, and Nanning Zheng. 2018. Transductive semi-supervised deep learning using min-max features. In European Conference on Computer Vision (ECCV).
[32]
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In International Con-ference on Learning Representations.
[33]
Donghui Wang, Yanchun Liang, Dong Xu, Xiaoyue Feng, and Renchu Guan. 2018. A content-based recommender system for computer science publications. Knowledge-Based Systems 157 (2018), 1--9.
[34]
Junyuan Xie, Ross Girshick, and Ali Farhadi. 2016. Unsupervised deep embedding for clustering analysis. In International Conference on Machine Learning. 478--487.
[35]
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le. 2020. Self-training with noisy student improves imagenet classification. In IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[36]
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In International Conference on Learning Representa-tions.
[37]
Zaihan Yang and Brian D. Davison. 2012. Venue recommendation: Submitting your paper with style. In International Conference on Machine Learning and Applications.
[38]
Liang Yao, Chengsheng Mao, and Yuan Luo. 2019. Graph Convolutional Networks for Text Classification. In AAAI Conference on Artificial Intelligence.
[39]
David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling super-vised methods. In Annual Meeting of the Association for Computational Linguistics.
[40]
Shuo Yu, Jiaying Liu, Zhuo Yang, Zhen Chen, Huizhen Jiang, Amr Tolba, and Feng Xia. 2018. PAVE: Personalized Academic Venue recommendation Exploiting co-publication networks. Journal of Network and Computer Applications 104 (2018), 38--47.
[41]
Chengxiang Zhai and John Lafferty. 2004. A study of smoothing methods for lan-guage models applied to information retrieval. ACM Transactions on Information Systems (TOIS) 22, 2 (2004), 179--214.
[42]
Yifan Zhu, Qika Lin, Hao Lu, Kaize Shi, Ping Qiu, and Zhendong Niu. 2021. Recommending scientific paper via heterogeneous knowledge embedding based attentive recurrent neural networks. Knowledge-Based Systems 215 (2021), 106744.
[43]
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, and Quoc Le. 2020. Rethinking Pre-training and Self-training. In Advances in Neural Information Processing Systems.

Cited By

View all
  • (2024)A Survey on Graph Representation Learning MethodsACM Transactions on Intelligent Systems and Technology10.1145/363351815:1(1-55)Online publication date: 16-Jan-2024
  • (2023)Graph-based Text Classification by Contrastive Learning with Text-level Graph AugmentationACM Transactions on Knowledge Discovery from Data10.1145/363835318:4(1-21)Online publication date: 22-Dec-2023
  • (2023)Time-Aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question AnsweringICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10095395(1-5)Online publication date: 4-Jun-2023

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
October 2021
4966 pages
ISBN:9781450384469
DOI:10.1145/3459637
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 October 2021

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. graph neural networks
  2. paper-publication prediction
  3. text mining

Qualifiers

  • Research-article

Funding Sources

Conference

CIKM '21
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)42
  • Downloads (Last 6 weeks)2
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)A Survey on Graph Representation Learning MethodsACM Transactions on Intelligent Systems and Technology10.1145/363351815:1(1-55)Online publication date: 16-Jan-2024
  • (2023)Graph-based Text Classification by Contrastive Learning with Text-level Graph AugmentationACM Transactions on Knowledge Discovery from Data10.1145/363835318:4(1-21)Online publication date: 22-Dec-2023
  • (2023)Time-Aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question AnsweringICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10095395(1-5)Online publication date: 4-Jun-2023

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media