Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed in practical applications? Besides the obvious ones–recommendation systems at Pinterest, Alibaba and Twitter–a slightly nuanced success story is the Transformer architecture, which has taken the NLP industry by storm. Through this post, I want to establish links between Graph Neural Networks (GNNs) and Transformers. I’ll talk about the intuitions behind model architectures in the NLP and GNN communities, make connections using equations and figures, and discuss how we could work together to drive progress.
Our mission is to leverage the methods of machine learning and game theory for addressing relevant applications both in recreational games and in abstract decision games played in the real world.
Gephi is an open-source software for visualizing and analyzing large networks graphs. Gephi uses a 3D render engine to display graphs in real-time and speed up the exploration. Use Gephi to explore, analyse, spatialise, filter, cluterize, manipulate and export all types of graphs.
C. Scholz, M. Atzmueller, M. Kibanov, and G. Stumme. Proceedings of the 2013 ACM/IEEE International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, Niagara Falls, Canada, August 25-28, 2013, page 356--363. New York, NY, USA, ACM, (2013)
A. Schmidt, and G. Stumme. Proceedings of the 21th International Conference on Knowledge Engineering and Knowledge Management (EKAW), page 370-385. Springer, (2018)
M. Atzmueller, L. Thiele, G. Stumme, and S. Kauffeld. Proc. Annual Machine Learning Conference of the Benelux (Benelearn 2017), Eindhoven, The Netherlands, Eindhoven University of Technology, (2017)
M. Atzmueller, L. Thiele, G. Stumme, and S. Kauffeld. Proc. ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, New York, NY, USA, ACM Press, (2016)