A Sample-driven Selection Framework: Towards Graph Contrastive Networks with Reinforcement Learning
Authors: 
Xiangping Zheng, Xiuxin Hao, Bo Wu, Xigang Bao, Xuan Zhang, Wei Li, Xun LiangAuthors Info & Claims
Xiangping Zheng, Xiuxin Hao, Bo Wu, Xigang Bao, Xuan Zhang, Wei Li, Xun LiangAuthors Info & ClaimsPages 10755 - 10764
Abstract
Graph Contrastive Learning (GCL) aims to address the issue of label scarcity by leveraging graph structures to propagate labels from a limited set of labeled data to a broader range of unlabeled data. However, recent GCL methods often rely on uniform negative sample selection schemes, such as random sampling, which results in suboptimal performance. To tackle this challenge, we present GraphSaSe, a tailored approach specifically designed for graph contrastive learning. Our method introduces an innovative reinforcement learning strategy that translates the divergence between positive pairs into a reinforcement reward mechanism. This mechanism generates selection probabilities to dynamically guide the selection of negative samples during training. We explore the impact of negative sample selection at different stages in graph contrastive learning and analyze how the discount factor affects the reward mechanism in reinforcement learning. These studies enhance the overall performance of the model. Comprehensive experimentation across diverse real-world datasets validates the effectiveness of our algorithm, positioning it favorably against contemporary state-of-the-art methodologies.
References
[1]
Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, and Alexander J. Smola. 2006. Integrating structured biological data by Kernel Maximum Mean Discrepancy. In Proceedings 14th International Conference on Intelligent Systems for Molecular Biology 2006, Fortaleza, Brazil, August 6-10, 2006. 49--57. https://doi.org/10.1093/bioinformatics/btl242
[2]
Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 3 (2011), 27:1--27:27. https: //doi.org/10.1145/1961189.1961199
[3]
Guanyi Chu, Xiao Wang, Chuan Shi, and Xunqiang Jiang. 2021. CuCo: Graph Representation with Curriculum Contrastive Learning. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021, Zhi-Hua Zhou (Ed.). ijcai.org, 2300--2306. https://doi.org/10.24963/ijcai.2021/317
[4]
Zicun Cong, Baoxu Shi, Shan Li, Jaewon Yang, Qi He, and Jian Pei. 2024. Fair-Sample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently. IEEE Trans. Knowl. Data Eng. 36, 4 (2024), 1537--1551. https: //doi.org/10.1109/TKDE.2023.3306378
[5]
Gongfan Fang, Jie Song, Xinchao Wang, Chengchao Shen, Xingen Wang, and Mingli Song. 2021. Contrastive Model Inversion for Data-Free Knowledge Distillation. CoRR abs/2105.08584 (2021). arXiv:2105.08584 https://arxiv.org/abs/2105.08584
[6]
Jun Feng, Minlie Huang, Li Zhao, Yang Yang, and Xiaoyan Zhu. 2018. Reinforcement Learning for Relation Classification From Noisy Data. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press, 5779--5786. https://www.aaai.org/ocs/index.php/ AAAI/AAAI18/paper/view/17151
[7]
Alex Fout, Jonathon Byrd, Basir Shariat, and Asa Ben-Hur. 2017. Protein Interface Prediction using Graph Convolutional Networks. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 6530--6539. https://proceedings.neurips.cc/paper/ 2017/hash/f507783927f2ec2737ba40afbd17efb5-Abstract.html
[8]
William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 1024--1034. https://proceedings.neurips.cc/paper/2017/hash/ 5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html
[9]
Kaveh Hassani and Amir Hosein Khas Ahmadi. 2020. Contrastive Multi-View Representation Learning on Graphs. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event (Proceedings of Machine Learning Research), Vol. 119. PMLR, 4116--4126. http://proceedings.mlr.press/v119/hassani20a.html
[10]
Biao Hou and Junxing Zhang. 2021. Real-time Surveillance Video Salient Object Detection Using Collaborative Cloud-Edge Deep Reinforcement Learning. In International Joint Conference on Neural Networks, IJCNN 2021, Shenzhen, China, July 18-22, 2021. IEEE, 1--8. https://doi.org/10.1109/IJCNN52387.2021.9533581
[11]
Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang. 2022. GraphMAE: Self-Supervised Masked Graph Autoencoders. In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022, Aidong Zhang and Huzefa Rangwala (Eds.). ACM, 594--604. https://doi.org/10.1145/3534678.3539321
[12]
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay S. Pande, and Jure Leskovec. 2020. Strategies for Pre-training Graph Neural Networks. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. https://openreview. net/forum?id=HJlWWJSFDH
[13]
Wei Ju, Yiyang Gu, Xiao Luo, Yi-Fan Wang, Haochen Yuan, Huasong Zhong, and Ming Zhang. 2023. Unsupervised graph-level representation learning with hierarchical contrasts. Neural Networks 158 (2023), 359--368. https://doi.org/10. 1016/j.neunet.2022.11.019
[14]
Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=SJU4ayYgl
[15]
Luchen Li and A. Aldo Faisal. 2021. Bayesian Distributional Policy Gradients. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021. AAAI Press, 8429--8437. https://ojs.aaai.org/ index.php/AAAI/article/view/17024
[16]
Qimai Li, Zhichao Han, and Xiao-MingWu. 2018. Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press, 3538--3545. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/ paper/view/16098
[17]
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https://openreview. net/forum?id=SJiHXGWAZ
[18]
Jianhua Lin. 1991. Divergence measures based on the Shannon entropy. IEEE Trans. Inf. Theory 37, 1 (1991), 145--151. https://doi.org/10.1109/18.61115
[19]
Miaofeng Liu, Yan Song, Hongbin Zou, and Tong Zhang. 2019. Reinforced Training Data Selection for Domain Adaptation. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28-August 2, 2019, Volume 1: Long Papers, Anna Korhonen, David R. Traum, and Lluís Màrquez (Eds.). Association for Computational Linguistics, 1957--1968. https://doi.org/10.18653/v1/p19-1189
[20]
Yuanfu Lu, Xunqiang Jiang, Yuan Fang, and Chuan Shi. 2021. Learning to Pre-train Graph Neural Networks. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021. AAAI Press, 4276--4284. https://ojs.aaai.org/index.php/AAAI/article/view/16552
[21]
Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal. 2017. graph2vec: Learning Distributed Representations of Graphs. CoRR abs/1707.05005 (2017). arXiv:1707.05005 http: //arxiv.org/abs/1707.05005
[22]
Shaowen Peng, Kazunari Sugiyama, and Tsunenori Mine. 2024. Less is More: Removing Redundancy of Graph Convolutional Networks for Recommendation. ACM Trans. Inf. Syst. 42, 3 (2024), 85:1--85:26. https://doi.org/10.1145/3632751
[23]
Efrén Rama-Maneiro, Juan Carlos Vidal, and Manuel Lama. 2024. Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring. IEEE Trans. Knowl. Data Eng. 36, 1 (2024), 137--151. https://doi.org/ 10.1109/TKDE.2023.3286017
[24]
Alfréd Rényi. 1961. On measures of entropy and information. In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics. University of California Press, 547--561.
[25]
Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka. 2021. Contrastive Learning with Hard Negative Samples. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. https://openreview.net/forum?id=CR1XOQ0UTh-
[26]
Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, and Jian Tang. 2019. Session-Based Social Recommendation via Dynamic Graph Attention Networks. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, February 11-15, 2019, J. Shane Culpepper, Alistair Moffat, Paul N. Bennett, and Kristina Lerman (Eds.). ACM, 555--563. https://doi.org/10.1145/3289600.3290989
[27]
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2020. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview. net. https://openreview.net/forum?id=r1lfF2NYvH
[28]
Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. 2021. Adversarial Graph Augmentation to Improve Graph Contrastive Learning. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, Marc?Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and JenniferWortman Vaughan (Eds.). 15920--15933. https://proceedings.neurips.cc/paper/2021/hash/ 854f1fb6f65734d9e49f708d6cd84ad6-Abstract.html
[29]
Vincent Van Asch and Walter Daelemans. 2010. Using domain similarity for performance estimation. In Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing. 31--36.
[30]
Aäron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation Learning with Contrastive Predictive Coding. CoRR abs/1807.03748 (2018). arXiv:1807.03748 http://arxiv.org/abs/1807.03748
[31]
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2017. Graph Attention Networks. CoRR abs/1710.10903 (2017). arXiv:1710.10903 http://arxiv.org/abs/1710.10903
[32]
Jun Xia, LirongWu, Jintao Chen, Bozhen Hu, and Stan Z. Li. 2022. SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation. In WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022, Frédérique Laforest, Raphaël Troncy, Elena Simperl, Deepak Agarwal, Aristides Gionis, Ivan Herman, and Lionel Médini (Eds.). ACM, 1070--1079. https: //doi.org/10.1145/3485447.3512156
[33]
Yaochen Xie, Zhao Xu, and Shuiwang Ji. 2022. Self-Supervised Representation Learning via Latent Graph Prediction. In International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, and Sivan Sabato (Eds.), Vol. 162. PMLR, 24460--24477. https://proceedings.mlr.press/v162/xie22e.html
[34]
Keyulu Xu,Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net. https://openreview.net/forum?id=ryGs6iA5Km
[35]
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph Contrastive Learning with Augmentations. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, Hugo Larochelle, Marc Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.). https://proceedings.neurips.cc/paper/2020/hash/ 3fe230348e9a12c13120749e3f9fa4cd-Abstract.html
[36]
Jiaqi Zeng and Pengtao Xie. 2021. Contrastive Self-supervised Learning for Graph Classification. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021. AAAI Press, 10824--10832. https: //ojs.aaai.org/index.php/AAAI/article/view/17293
[37]
Dawei Zhang, Zhonglong Zheng, Riheng Jia, and Minglu Li. 2021. Visual Tracking via Hierarchical Deep Reinforcement Learning. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021. AAAI Press, 3315--3323. https://ojs.aaai.org/index.php/AAAI/article/view/16443
[38]
Han Zhao, Xu Yang, Zhenru Wang, Erkun Yang, and Cheng Deng. 2021. Graph Debiased Contrastive Learning with Joint Representation Clustering. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021, Zhi-Hua Zhou (Ed.). ijcai.org, 3434--3440. https://doi.org/10.24963/ijcai.2021/473
[39]
Chuanpan Zheng, Xiaoliang Fan, Shirui Pan, Haibing Jin, Zhaopeng Peng, Zonghan Wu, Cheng Wang, and Philip S. Yu. 2024. Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting. IEEE Trans. Knowl. Data Eng. 36, 1 (2024), 372--385. https://doi.org/10.1109/TKDE.2023.3284156
[40]
Xiangping Zheng, Xun Liang, and BoWu. 2023. Select The Best: Enhancing Graph Representation with Adaptive Negative Sample Selection. In IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2023, Rhodes Island, Greece, June 4-10, 2023. IEEE, 1--5. https://doi.org/10.1109/ICASSP49357.2023. 10095586
[41]
Xiangping Zheng, Xun Liang, Bo Wu, Yuhui Guo, and Xuan Zhang. 2022. Graph Capsule Network with a Dual Adaptive Mechanism. In SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022, Enrique Amigó, Pablo Castells, Julio Gonzalo, Ben Carterette, J. Shane Culpepper, and Gabriella Kazai (Eds.). ACM, 1859--1864. https://doi.org/10.1145/3477495.3531764
[42]
Xiangping Zheng, Xun Liang, Bo Wu, Jun Wang, Yuhui Guo, Sensen Zhang, and Yuefeng Ma. 2023. Modeling High-Order Relation to Explore User Intent with Parallel Collaboration Views. In Database Systems for Advanced Applications - 28th International Conference, DASFAA 2023, Tianjin, China, April 17-20, 2023, Proceedings, Part II (Lecture Notes in Computer Science), Xin Wang, Maria Luisa Sapino, Wook-Shin Han, Amr El Abbadi, Gill Dobbie, Zhiyong Feng, Yingxiao Shao, and Hongzhi Yin (Eds.), Vol. 13944. Springer, 489--504. https://doi.org/10. 1007/978-3-031-30672-3_33
[43]
Xiangping Zheng, Xun Liang, Bo Wu, Jun Wang, Yuhui Guo, Xuan Zhang, and Yuefeng Ma. 2023. A Multi-scale Interaction Motion Network for Action Recognition Based on Capsule Network. In Proceedings of the 2023 SIAM International Conference on Data Mining, SDM 2023, Minneapolis-St. Paul Twin Cities, MN, USA, April 27-29, 2023, Shashi Shekhar, Zhi-Hua Zhou, Yao-Yi Chiang, and Gregor Stiglic (Eds.). SIAM, 505--513. https://doi.org/10.1137/1.9781611977653.CH57
[44]
Xiangping Zheng, Bo Wu, Xun Liang, and Wei Li. 2024. Friend or Foe? Mining Suspicious Behavior via Graph Capsule Infomax Detector against Fraudsters. In Proceedings of the ACM on Web Conference 2024, WWW 2024, Singapore, May 13-17, 2024, Tat-Seng Chua, Chong-Wah Ngo, Ravi Kumar, Hady W. Lauw, and Roy Ka-Wei Lee (Eds.). ACM, 2684--2693. https://doi.org/10.1145/3589334.3645706
[45]
Xiangping Zheng, Bo Wu, Alex X. Zhang, and Wei Li. 2024. Hypergraph-Based Session Modeling: A Multi-Collaborative Self-Supervised Approach for Enhanced Recommender Systems. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC/COLING 2024, 20-25 May, 2024, Torino, Italy, Nicoletta Calzolari, Min-Yen Kan, Véronique Hoste, Alessandro Lenci, Sakriani Sakti, and Nianwen Xue (Eds.). ELRA and ICCL, 8493--8504. https://aclanthology.org/2024.lrec-main.745
[46]
Xiangping Zheng, BoWu, Alex X. Zhang, andWei Li. 2024. Improving Robustness of GNN-based Anomaly Detection by Graph Adversarial Training. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC/COLING 2024, 20-25 May, 2024, Torino, Italy, Nicoletta Calzolari, Min-Yen Kan, Véronique Hoste, Alessandro Lenci, Sakriani Sakti, and Nianwen Xue (Eds.). ELRA and ICCL, 8902--8912. https://aclanthology. org/2024.lrec-main.779
[47]
Junping Zhong, Zhigang Liu, Hongrui Wang, Wenqiang Liu, Cheng Yang, Zhiwei Han, and Alfredo Núñez. 2021. A Looseness Detection Method for Railway Catenary Fasteners Based on Reinforcement Learning Refined Localization. IEEE Trans. Instrum. Meas. 70 (2021), 1--13. https://doi.org/10.1109/TIM.2021.3086913
[48]
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2021. Graph Contrastive Learning with Adaptive Augmentation. In WWW '21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021, Jure Leskovec, Marko Grobelnik, Marc Najork, Jie Tang, and Leila Zia (Eds.). ACM / IW3C2, 2069--2080. https://doi.org/10.1145/3442381.3449802
Index Terms
- A Sample-driven Selection Framework: Towards Graph Contrastive Networks with Reinforcement Learning
Recommendations
Self-supervised Graph-level Representation Learning with Adversarial Contrastive Learning
The recently developed unsupervised graph representation learning approaches apply contrastive learning into graph-structured data and achieve promising performance. However, these methods mainly focus on graph augmentation for positive samples, while the ...
Graph Self-supervised Learning with Augmentation-aware Contrastive Learning
WWW '23: Proceedings of the ACM Web Conference 2023Graph self-supervised learning aims to mine useful information from unlabeled graph data, and has been successfully applied to pre-train graph representations. Many existing approaches use contrastive learning to learn powerful embeddings by learning ...
GCH: Graph Contrastive Learning with Higher-Order Networks
Web and Big DataAbstractGraph contrastive learning has improved graph representation learning, becoming a successful unsupervised graph representation learning method. This method first generates two or more views of the input graph through data augmentation and learns ...
Comments
Information & Contributors
Information
Published In
October 2024
11719 pages
ISBN:9798400706868
DOI:10.1145/3664647
- General Chairs:
- Jianfei Cai,
- Mohan Kankanhalli,
- Balakrishnan Prabhakaran,
- Susanne Boll,
- Program Chairs:
- Ramanathan Subramanian,
- Liang Zheng,
- Vivek K. Singh,
- Pablo Cesar,
- Lexing Xie,
- Dong Xu
Copyright © 2024 ACM.
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 the author(s) 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: 28 October 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- Talent Program of the School of Computer Science and Technology at Harbin Engineering University
- Natural Science Foundation of Heilongjiang Province
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Stable Supporting Fund of the National Key Laboratory of Underwater Acoustic Technology
- Central University Basic Research Project
Conference
MM '24
Sponsor:
MM '24: The 32nd ACM International Conference on Multimedia
October 28 - November 1, 2024
Melbourne VIC, Australia
Acceptance Rates
MM '24 Paper Acceptance Rate 1,150 of 4,385 submissions, 26%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 113Total Downloads
- Downloads (Last 12 months)113
- Downloads (Last 6 weeks)66
Reflects downloads up to 28 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in