SNAP: A General Purpose Network Analysis and Graph Mining Library

By Jure Leskovec, Rok Sosic. 2016

830 citations recorded by the source corpus.

Large networks are becoming a widely used abstraction for studying complex systems in a broad set of disciplines, ranging from social-network analysis to molecular biology and neuroscience. Despite an increasing need to analyze and manipulate large networks, only a limited number of tools are available for this task. Here, we describe the Stanford Network Analysis Platform (SNAP), a general-purpose, high-performance system that provides easy-to-use, high-level operations for analysis and manipulation of large networks. We present SNAP functionality, describe its implementational details, and give performance benchmarks. SNAP has been developed for single big-memory machines, and it balances the trade-off between maximum performance, compact in-memory graph representation, and the ability to handle dynamic graphs in which nodes and edges are being added or removed over time. SNAP can process massive networks with hundreds of millions of nodes and billions of edges. SNAP offers over 140 different graph algorithms that can efficiently manipulate large graphs, calculate structural properties, generate regular and random graphs, and handle attributes and metadata on nodes and edges. Besides being able to handle large graphs, an additional strength of SNAP is that networks and their attributes are fully dynamic; they can be modified during the computation at low cost. SNAP is provided as an open-source library in C++ as well as a module in Python. We also describe the Stanford Large Network Dataset, a set of social and information real-world networks and datasets, which we make publicly available. The collection is a complementary resource to our SNAP software and is widely used for development and benchmarking of graph analytics algorithms.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jure Leskovec
2. Marinka Zitnik
3. Yuxiao Dong
4. Weihua Hu
5. Matthias Fey
6. Hongyu Ren
7. Jie Tang
8. Bowen Liu

In Computer Vision and Pattern Recognition

1. Yuxiao Dong
2. Jure Leskovec
3. Marinka Zitnik
4. Hongyu Ren
5. Weihua Hu
6. Jie Tang
7. Bowen Liu
8. Qian Xu

In Statistical and Nonlinear Physics

1. Marinka Zitnik
2. Matthias Fey
3. Jie Tang
4. Yuxiao Dong
5. Jure Leskovec
6. Weihua Hu
7. Bowen Liu
8. Hongyu Ren

Citations in the corpus

• Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time (2017, positive): “Pixie is written in C++ and is built on top of the Stanford Network Analysis Platform (SNAP) [20].”
• Introduction to MAchine Learning & Knowledge Extraction (MAKE) (2017, neutral): “Abbreviations The following abbreviations are used in this manuscript: aML Automatic Machine Learning ANN Artificial Neural Network CNN Convolutional Neural Network DL Deep Learning EWC Elastic Weight Consolitation FMA Foundational Model of Anatomy FB Facebook GO Gene Ontology GP Gaussian Processes ”
• Graph Random Neural Networks for Semi-Supervised Learning on Graphs (2020, background): “Graphs serve as a common language for modeling structured and relational data [22], such as social networks, knowledge graphs, and the World Wide Web.”
• EmptyHeaded: A Relational Engine for Graph Processing (2015, neutral): “H.2 [Information Systems]: Database Management System Engines”
• Open Graph Benchmark: Datasets for Machine Learning on Graphs (2020, positive): “The SNAP library [53] is then used to compute the graph statistics, where the graph diameter is approximated by performing BFS from 1,000 randomlysampled nodes.”