bookmarks (hide)64
- 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.
- Indiana University Professor specializing in scientometrics, informetrics, science of science studies, network science, cyberinfrastructure
- 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.
- This all began with an introductory presentation about social network analysis to a group of medical students. What better way to grab their attention than with attractive, fake doctors having sex on television? Naturally this led to the dense network … Continue reading →
- 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.
publications (hide)65
- IEEE Transactions on Network and Service Management, (2024)
- (2021)cite arxiv:2111.07671.
- Computers & Security, (2019)
- PLOS ONE, 13 (9): 1-18 (September 2018)
- CoRR, (2018)
- PLOS ONE, 13 (7): 1-22 (July 2018)
- Big Data and Software Defined Networks, IET, (2018)
- Nature, 538 (7626): 471--476 (October 2016)
- page 3--31. Springer International Publishing, Cham, (2017)
- Proceedings of the 21st International Conference on World Wide Web, page 619--628. New York, NY, USA, ACM, (2012)
- ECCWS2016-Proceedings fo the 15th European Conference on Cyber Warfare and Security, page 437. Academic Conferences and publishing limited, (2016)
- (2015)
- Science Advances, (May 2016)
- (2016)
- (2015)cite arxiv:1506.05869Comment: ICML Deep Learning Workshop 2015.
- IEEE Intelligent Systems, 24 (3): 16-25 (2009)
- (2014)cite arxiv:1410.4120Comment: 4 pages, 3 figures.
- Phys. Rev. E, 64 (1): 016131 (June 2001)
- (2009)cite arxiv:0912.5410Comment: 96 pages, 14 figures, 333 references.
- (2011)cite arxiv:1111.4503Comment: 17 pages, 9 figures, 1 table.
