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 * SNAP for C++
    * SNAP C++ Main Page
    * SNAP C++ Download
    * SNAP C++ Documentation

 * SNAP for Python
    * Snap.py Python Main Page
    * Snap.py Python Download
    * Snap.py Python Documentation

 * SNAP Datasets
    * Large networks
    * Web datasets
    * Other resources

 * BIOSNAP Datasets
 * What's new
 * People
 * Papers
 * Projects
    * Activity Inequality
    * AGM
    * BetaE
    * CAW
    * COMET
    * ConE
    * Conflict
    * ConNIe
    * Counseling
    * CRank
    * Distance-encoding
    * Decagon
    * F-FADE
    * GIB
    * GNN-Design
    * GNN-Explainer
    * GNN-pretrain
    * GRAPE
    * GraphSAGE
    * GraphWave
    * G2SAT
    * HGCN
    * Higher-order
    * ID-GNN
    * Disinformation
    * InfoPath
    * JODIE
    * LIM
    * MAPPR
    * MAMBO
    * MARS
    * Memetracker
    * NCP
    * NE
    * NETINF
    * NIFTY
    * node2vec
    * Ocean
    * OhmNet
    * ORCA
    * NeuroMatch
    * Pathways
    * P-GNN
    * Query2box
    * QUOTUS
    * Ringo
    * SEISMIC
    * SNAP
    * Snap.py
    * SnapVX
    * SPMiner
    * STELLAR
    * Temporal Motifs
    * TICC
    * TIPAS
    * Tree of Life
    * TVGL

 * Citing SNAP
 * Links
 * About
 * Contact us

Open positions
We are inviting applications for postdoctoral positions in Foundation Models for
Biomedicine. We have open positions for undergraduate and graduate research
assistants. The application form and project descriptions can be found here.
Stanford Network Analysis Project



SNAP FOR C++: STANFORD NETWORK ANALYSIS PLATFORM

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: SNAP FOR PYTHON

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.


STANFORD LARGE NETWORK DATASET COLLECTION

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.


RECENT EVENTS

We gave a tutorial on Deep Learning for Network Biology at the annual
international conference on Intelligent Systems for Molecular Biology (ISMB) in
Chicago, on July 6, 2018.

We gave a tutorial on Representation Learning on Networks at The Web Conference
in Lyon, France, on April 24, 2018.

We organized Wiki Workshop at The Web Conference in Lyon, France, on April 24,
2018.


PUBLICATIONS

Papers on the structure and evolution of large networks, models to think about
them and algorithms to computationally analyze the network structure.


TUTORIALS

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.



 * Tutorial on Meta-learning for Bridging Labeled and Unlabeled Data in
   Biomedicine will be held at ISMB/ECCB conference in July, 2021. More info.
 * Tutorial on Deep Learning for Network Biology was held at the annual
   international conference on Intelligent Systems for Molecular Biology (ISMB)
   in Chicago, on July 6, 2018.
 * Tutorial on Representation Learning on Networks was held at The Web
   Conference in Lyon, France, on April 24, 2018. More info.
 * Tutorial on Malicious Behavior on the Web: Characterization and Detection was
   held at WWW2017 conference, Perth, Australia, April 3, 2017. More info.
 * Tutorial on Large Scale Network Analytics with SNAP was held at WWW-15
   conference, Florence, Italy, May 18, 2015. More info.
 * Tutorial on Large Scale Network Analytics with SNAP was held at ICWSM-14
   conference, June 2014. More info.
 * Tutorial on Social Media Analytics was held at ACM SIGKDD conference, August
   2011. More info.
 * Tutorial on Analytics & Predictive Models for Social Media was held at the
   ACM WWW '11 conference. More info.
   
   




EVENTS

Workshop on MIS2: Misinformation and Misbehavior Mining on the Web was held at
WSDM 2018.

Wiki Workshop was held at WWW 2017.

Wiki Workshop was held at WWW 2016 and ICWSM 2016.

Workshop on Wikipedia, a Social Pedia: Research Challenges and Opportunities was
held in conjunction with ICWSM 2015.

Workshop on Frontiers of Network Analysis: Methods, Models, and Applications was
held in conjunction with Neural Information Processing Systems conference (NIPS
2013).

3rd Stanford Conference on Computational Social Science.

Eleventh Workshop on Mining and Learning with Graphs was co-located with KDD
2013.

Workshop on Social Network and Social Media Analysis: Methods, Models and
Applications was held in conjunction with Neural Information Processing Systems
conference (NIPS 2012).

Workshop on Networks Across Disciplines in Theory and Applications was held in
conjunction with Neural Information Processing Systems conference (NIPS 2010).

Workshop on Social Media Analytics was held in conjunction with the 16th ACM
SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD
2010).

Analyzing Networks and Learning with Graphs was held in conjunction with Neural
Information Processing Systems conference (NIPS 2009).