The Boost Graph Library Python bindings (which we refer to as "BGL-Python") expose the functionality of the Boost Graph Library and Parallel Boost Graph Library as a Python package, allowing one to perform computation-intensive tasks on graphs (or network
The video is a screencast of RhNav - Rhizome Navigation visualizing the Blogosphere as a 3D graph using the technorati API. www.rafelsberger.at is used as a starting point.
Graph-based NLP
From Language and Information Technologies
Jump to: navigation, search
The goal of this research project is to investigate efficient graph-based representations of text, and explore the application of ranking models based on such graph structures to natural language processing tasks. We bring together methods from computational linguistics and graph-theory, and combine them into a suite of innovative approaches that will improve and ultimately solve difficult problems in natural language processing. Specifically, we are currently working on the application of graph centrality algorithms to problems such as word sense disambiguation, text summarization and keyword extraction.
So, a while ago, I’ve decided to code a library to plot some information I had. The idea was to create simple graphics in a way they would be easy to create, beautiful and good to present to people with no or few backgrounds on math and computers.
Graph mining refers to extracting knowledge from massive graphs. The data sets of telephone calls we see at AT&T can be viewed as a single graph, with several hundred million phone numbers as nodes, and calls between phone numbers as edges. It is a giant social network, like an internet connections graph or a rich citation network.
M. Rattigan, M. Maier, and D. Jensen. ICML '07: Proceedings of the 24th international conference on Machine learning, page 783--790. New York, NY, USA, ACM, (2007)
P. Ravindra, V. Deshpande, and K. Anyanwu. MDAC '10: Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud, page 1--6. New York, NY, USA, ACM, (2010)
K. Rohloff, and R. Schantz. Proceedings of the fourth international workshop on Data-intensive distributed computing, page 35--44. New York, NY, USA, ACM, (2011)