This software is a translation into C++ of the excellent Webgraph library by P. Boldi and S. Vigna. The original library, written in Java, is easy to use but hampered by some requirements of the Java virtual machine. This C++ translation attempts to preserve much of the ease of use (through integration with the Boost Graph Library), but bypass requirements imposed by a virtual machine.
This page provides two large hyperlink graph for public download. The graphs have been extracted from the 2012 and 2014 versions of the Common Crawl web corpera. The 2012 graph covers 3.5 billion web pages and 128 billion hyperlinks between these pages. To the best of our knowledge, the graph is the largest hyperlink graph that is available to the public outside companies such as Google, Yahoo, and Microsoft. The2014 graph covers 1.7 billion web pages connected by 64 billion hyperlinks. Below we provide instructions on how to download the graphs as well as basic statistics about their topology.
This page provides a large hyperlink graph for public download. The graph has been extracted from the Common Crawl 2012 web corpus and covers 3.5 billion web pages and 128 billion hyperlinks between these pages. To the best of our knowledge, this graph is the largest hyperlink graph that is available to the public outside companies such as Google, Yahoo, and Microsoft. Below we provide instructions on how to download the graph as well as basic statistics about its topology.
This graph illustrates the distribution of relationships in Twitter—specifically the number of people folks follow as well as how many people are following them.
TrueSkill™ Ranking System
TrueSkill™ Ranking System
The TrueSkill™ ranking system is a skill based ranking system for Xbox Live developed at Microsoft Research.
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.
A simple particle system physics engine for processing. I've designed this to be application / domain agnostic. All this is supposed to do is let you make particles, apply forces and calculate the positions of particles over time in real-time. Anything else you need to handle yourself.
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...
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I. Klapaftis, and S. Manandhar. Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence, page 298--302. Amsterdam, The Netherlands, The Netherlands, IOS Press, (2008)
M. Bonato. Europäische Hochschulschriften. Reihe VI, Psychologie ; Bd. 297 Publications
universitaires europÈennes. SÈrie VI, Psychologie ; vol. 297 P. Lang, Frankfurt am Main ;New York, (1990)Marcellus Bonato. ill 21 cm. Originally presented as the author's
thesis (doctoral--Universität Mäünster, 1989). $5GBV Includes bibliographical
references (p. 147-159). $5GBV.
E. Yeh, D. Ramage, C. Manning, E. Agirre, and A. Soroa. Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing, page 41--49. Stroudsburg, PA, USA, Association for Computational Linguistics, (2009)
J. Leskovec, S. Dumais, and E. Horvitz. WWW '07: Proceedings of the 16th international conference on World Wide Web, page 471--480. New York, NY, USA, ACM, (2007)