Graph mining

Our mission is to build the most scalable library for graph algorithms and analysis and apply it to a multitude of Google products.

graphs

Our mission is to build the most scalable library for graph algorithms and analysis and apply it to a multitude of Google products.

About the team

We formalize data mining and machine learning challenges as graph problems and perform fundamental research in those fields leading to publications in top venues. Our algorithms and systems are used in a wide array of Google products such as Search, YouTube, AdWords, Play, Maps, and Social.

Team focus summaries

Featured publications

Optimal Distributed Submodular Optimization via Sketching
Hossein Esfandiari
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2018), pp. 1138-1147
Affinity Clustering: Hierarchical Clustering at Scale
Soheil Behnezhad
Mahsa Derakhshan
MohammadTaghi Hajiaghayi
Raimondas Kiveris
NIPS 2017, pp. 6867-6877
Distributed Balanced Partitioning via Linear Embedding
Ninth ACM International Conference on Web Search and Data Mining (WSDM), ACM (2016), pp. 387-396
Grale: Designing Networks for Graph Learning
Alexandru Moșoi
Sam Ruth
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Association for Computing Machinery (2020), 2523–2532

Highlighted work

Some of our people