Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library. It is written in C++ and 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. SNAP is also available through the NodeXL which is a graphical front-end that integrates network analysis into Microsoft Office and Excel.
Snap.py is a Python interface for SNAP. It provides performance benefits of SNAP, combined with flexibility of Python. Most of the SNAP C++ functionality is available via Snap.py in Python.
A collection of more than 50 large network datasets from tens of thousands of nodes and edges to tens of millions of nodes and edges. In includes social networks, web graphs, road networks, internet networks, citation networks, collaboration networks, and communication networks.
Tutorials on using SNAP, on methods to analyze large network data, on ways how to think about networks and how to model them at the level of network structure, and on methods to study evolution and dynamics of diffusion and cascading behavior in networks.
Papers on the structure and evolution of large networks, models to think about them and algorithms to computationally analyze the network structure.
Workshop on Frontiers of Network Analysis: Methods, Models, and Applications held in conjunction with Neural Information Processing Systems conference (NIPS 2013).
3rd Stanford Conference on Computational Social Science.
Workshop on Social Network and Social Media Analysis: Methods, Models and Applications held in conjunction with Neural Information Processing Systems conference (NIPS 2012).
Workshop on Networks Across Disciplines in Theory and Applications held in conjunction with Neural Information Processing Systems conference (NIPS 2010).
Workshop on Social Media Analytics held in conjunction with the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010).
Analyzing Networks and Learning with Graphs held in conjunction with Neural Information Processing Systems conference (NIPS 2009).