The 3rd International Workshop on Machine Learning on Graphs (MLoG)
Document Type
Conference Proceeding
Publication Date
2-27-2023
Abstract
Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today's world, we are generating and gathering data in a much faster and more diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way. Thus, we organize The 3rd International Workshop on Machine Learning on Graphs (MLoG), held in conjunction with the 16th ACM Conference on Web Search and Data Mining (WSDM), which provides a venue to gather academia researchers and industry researchers/practitioners to present the recent progress on machine learning on graphs.
Identifier
85149646166 (Scopus)
ISBN
[9781450394079]
Publication Title
Wsdm 2023 Proceedings of the 16th ACM International Conference on Web Search and Data Mining
External Full Text Location
https://doi.org/10.1145/3539597.3572706
First Page
1271
Last Page
1272
Fund Ref
National Science Foundation
Recommended Citation
Derr, Tyler; Ma, Yao; Rozemberczki, Benedek; Shah, Neil; and Pan, Shirui, "The 3rd International Workshop on Machine Learning on Graphs (MLoG)" (2023). Faculty Publications. 1902.
https://digitalcommons.njit.edu/fac_pubs/1902