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The Sixth International Workshop on Deep Learning on Graphs - Methods and Applications (DLG-KDD'21)

Published: 14 August 2021 Publication History

Abstract

Deep Learning models are at the core of research in Artificial Intelligence research today. A tide in research for deep learning on graphs or graph neural networks. This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including computer vision, natural language processing, program synthesis and analysis, financial security, Drug Discovery, and so on. However, there are still many challenges regarding a broad range of the topics in deep learning on graphs, from methodologies to applications, and from foundations to the new frontiers of GNNs. This international workshop on "Deep Learning on Graphs: Method and Applications (DLG-KDD'21)" aims to bring together both academic researchers and industrial practitioners from different backgrounds and perspectives to the above challenges.

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  • (2023)LGFat-RGCN: Faster Attention with Heterogeneous RGCN for Medical ICD Coding GenerationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612564(5428-5435)Online publication date: 26-Oct-2023
  • (2023)Anti-Money Laundering by Group-Aware Deep Graph LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327239635:12(12444-12457)Online publication date: 2-May-2023

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Published In

cover image ACM Conferences
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
August 2021
4259 pages
ISBN:9781450383325
DOI:10.1145/3447548
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 August 2021

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Author Tags

  1. deep learning
  2. graph mining
  3. graph neural network
  4. graph representation learning

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KDD '21
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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

View all
  • (2023)LGFat-RGCN: Faster Attention with Heterogeneous RGCN for Medical ICD Coding GenerationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612564(5428-5435)Online publication date: 26-Oct-2023
  • (2023)Anti-Money Laundering by Group-Aware Deep Graph LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327239635:12(12444-12457)Online publication date: 2-May-2023

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