List of Resources for Complex Network Analysis

Network Data

• KONECT contains over a hundred network datasets of various types, including directed, undirected, bipartite, weighted, unweighted, signed and rating networks. The networks of KONECT are collected from many diverse areas such as social networks, hyperlink networks, authorship networks, physical networks, interaction networks and communication networks.
  
• NetWiki contains links to a large collection of network data
• Stanford Large Network Dataset Collection contains a collection of large networks mainly from social and web-based domains
• Mark Round's map of data formats and software tools 
• easyN allows the creation of gene interaction networks (either physical or genetic) but also the creation of Petri net models. The tool also allow the users to save their networks online and, if they want, publish them. 

Network Analysis Software Tools

• Graphia is a network analysis environment providing dynamic layouts, graph analytics and more. It works well for large graphs with millions of nodes and has an open source version (Graphia Pro)
• igraph is a free software package for creating and manipulating undirected and directed graphs. It includes implementations for classic graph theory problems like minimum spanning trees and network flow, and also implements algorithms for some recent network analysis methods, like community structure search. It can be downloaded as a C library, an R package or a Python extension.
• Brain Connectivity Toolbox contains a large selection of complex network measures in Matlab.
• MatlabBGL is a Matlab package for working with graphs.  It uses the Boost Graph Library to efficiently implement the graph algorithms.  MatlabBGL is designed to work with large sparse graphs with hundreds of thousands of nodes. 
• Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks.  The core SNAP library is written in C++ and optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. 

• Osaka University has released a method called PAFit to estimate the preferential attachment function of a growing complex network. Based on a rigorous statistical framework, PAFit can estimate the preferential attachment function non-parametrically. The related paper is at dx.doi.org/10.1371/journal.pone.0137796 . There is also an R package at cran.r-project.org/package=PAFit

Visualization Software Tools

A variety of tools have been developed to generate efficient and pleasing graphical representations of networks. Some of these are fairly general platforms that also allow some simple analyses to be run on the network:

• Gephi is an interactive visualization and exploration platform for all kinds of networks and complex systems, dynamic and hierarchical graphs. Runs on Windows, Linux and Mac OS X. Gephi is open-source and free.
• GUESS is an exploratory data analysis and visualization tool for graphs and networks. The system contains a domain-specific embedded language called Gython (an extension of Python, or more specifically Jython) which supports the operators and syntactic sugar necessary for working on graph structures in an intuitive manner. An interactive interpreter binds the text that you type in the interpreter to the objects being visualized for more useful integration. GUESS also offers a visualization front end that supports the export of static images and dynamic movies.
• PAJEK is a program for large network analysis.

• MapEquation (by Martin Rosvall and Daniel Edler) can be used to visualize and highlight important structures in large networks such as communities and how they evolve over time (alluvial diagrams) and more.
• Circos visualizes data in a pretty circular layout.
• SpaTo Visual Explorer can be used to reduce a network to the shortest-path tree of a selected root node, yielding a local but simpler view of the network that can be easily visualized. With the ability to quickly change the root node, the program allows us to explore the network from different perspectives.

Network Blogs and Websites

• Complexity and Social Networks Blog (David Lazer)
• Three-Toed Sloth (Shalizi Cosma)
• Visual Complexity (a unified resource space for anyone interested in the visualization of complex networks)

International Network Science Conferences

We don't include links to these conferences as the url tends to change each year. To learn more, simply google the name of each conference (and the year of interest)

• NetSci
• European Conference on Complex Systems (ECCS)
• CompleNet
• Network Frontier Workshop

Network Science Talks and Tutorials

The speakers of our launch event on 27th September 2011 have kindly agreed to make their slides available:

• Madan Babu (Systems Biology group, MRC Laboratory of Molecular Biology) - talk
• Ed Bullmore (Brain Mapping Unit, Department of Psychiatry) - talk
• Sanjeev Goyal (Faculty of Economics) - talk
• Cecilia Mascolo (Networks and Operating Systems Group, Computer Laboratory) - talk
• Franco M Neri (Epidemiology and Modelling Group, Department of Plant Sciences ) - talk