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.
Visualization of graph data is incredibly challenging, particularly when it comes to extremely large, scale-free graphs and social networks. A few simple searches on the Web and you will find some mesmerizing and very cool images. Perhaps the most cited...
[updated with Tagommenders paper – thanks Shilad.] The organizers of the World Wide Web conference recently announced the list of accepted papers for this year’s event. In the Social Networks and Web 2.0 track (chaired by Elisa Bertino and Lada...
F. Otto, M. Ring, D. Landes, и A. Hotho. ECCWS2016-Proceedings fo the 15th European Conference on Cyber Warfare and Security, стр. 437. Academic Conferences and publishing limited, (2016)