Statistical mechanics of complex networks
Authors: Reka Albert, Albert-Laszlo Barabasi
Comments: 54 pages, submitted to Reviews of Modern Physics
Subj-class: Statistical Mechanics; Disordered Systems and Neural Networks; Mathematical Physics; Data Analysis, Statistics and Probability; Adaptation and Self-Organizing Systems; Networking and Internet Architecture
Journal-ref: Reviews of Modern Physics 74, 47 (2002)
I posted an updated tech demo of RhNav - Rhizome Navigation visualizing user behavior of this blog. The graph is now centered around the page where most time is spent. Noise created by search engine robots is filtered which should clear things up quite a
p2pfoundation has an interesting post about the importance of time as a dimension in social networking technology. My concept of RhNav - RhizomeNavigation includes some of these thoughts...
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.
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.
J. Zhang, Y. Dong, Y. Wang, J. Tang, und M. Ding. Proceedings of the 28th International Joint Conference on Artificial Intelligence, Seite 4278–4284. AAAI Press, (10.08.2019)