NOTICE Unless I explicitly say otherwise, all pages and posts on this site are largely speculation based on observations. Where experiments have been done to provide conclusive evidence it is noted. These posts are a brain dump of my thoughts (to capture those elusive moments) at the time of writing, and is therefore likely to change as the results of research proves otherwise. Obviously this does not apply to posts where I describe a technique that has been implemented and tested.

## Posts to be done

• Basically available soft state eventual consistency
• Bloom Filters, caching your way to performance
• CAP theorem
• Components of a database management system
• Designing a graph file system
• Gossip protocols
• Haskell FFI, working with C libraries (see above)
• Imutability, a beautiful future
• Introduction to the fundamentals of graph theory
• Log structured merge trees for the rest of us (and why it doesn't work for graphs)
• Voltage Cluster (Finding communities in linear time: a physics approach)
• Markov cluster (http://micans.org/mcl/) Thesis => http://micans.org/mcl/index.html?sec_thesisetc
• Network modularity - http://en.wikipedia.org/wiki/Modularity_(networks)
• Clique - http://en.wikipedia.org/wiki/Clique(graphtheory)
• Fuzy clustering - http://en.wikipedia.org/wiki/Fuzzy_clustering
• BIRCH - http://en.wikipedia.org/wiki/Birch(dataclustering)

## TODO

Update with list of papers in the repo.

## Problems that can be solved with graphs

Having data represented as graphs is one thing but once you have that graph, what do you want to know about it? What can it tell you? This is a collection of resources with ideas of how to use graphs or graph algorithms.

DIMACS challenge data

Stanford's GraphBase Knuth

Clique Benchmark Instances

Graph colouring instances

Washington state graph dataset

Networks, Crowds and Markets, reasoning about a highly connected world

Introduction to graph theory- Wikibooks

Problem solving using graph traversals

An Introduction to Graph Theory and Complex Networks