# SNAP: A General Purpose Network Analysis and Graph Mining Library

arXiv: https://arxiv.org/abs/1606.07550 · HTML: https://mlsanity.com/paper/1606.07550/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2016.

## Built on by (most cited first, 30 of 56)

- [OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs](https://mlsanity.com/paper/2103.09430/) (2021)
- [GREED: A Neural Framework for Learning Graph Distance Functions](https://mlsanity.com/paper/2112.13143/) (2021)
- [Learning Individual Treatment Effects under Heterogeneous Interference in Networks](https://mlsanity.com/paper/2210.14080/) (2022)
- [FairGen: Towards Fair Graph Generation](https://mlsanity.com/paper/2303.17743/) (2023)
- [GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding](https://mlsanity.com/paper/2110.06196/) (2021)
- [GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware](https://mlsanity.com/paper/2108.11571/) (2021)
- [A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning](https://mlsanity.com/paper/2402.01440/) (2024)
- [When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph Benchmark](https://mlsanity.com/paper/2407.10916/) (2024)
- [The KL3M Data Project: Copyright-Clean Training Resources for Large Language Models](https://mlsanity.com/paper/2504.07854/) (2025)
- [SEAM: Semantically Equivalent Across Modalities Benchmark for Vision-Language Models](https://mlsanity.com/paper/2508.18179/) (2025)
- [Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs](https://mlsanity.com/paper/2210.00032/) (2022)
- [Mamba Hawkes Process](https://mlsanity.com/paper/2407.05302/) (2024)
- [Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning](https://mlsanity.com/paper/2210.10643/) (2022)
- [A Fast Algorithm for Moderating Critical Nodes via Edge Removal](https://mlsanity.com/paper/2309.06392/) (2023)
- [RoTHP: Rotary Position Embedding-based Transformer Hawkes Process](https://mlsanity.com/paper/2405.06985/) (2024)
- [Practicable Black-box Evasion Attacks on Link Prediction in Dynamic Graphs -- A Graph Sequential Embedding Method](https://mlsanity.com/paper/2412.13134/) (2024)
- [HeroFilter: Adaptive Spectral Graph Filter for Varying Heterophilic Relations](https://mlsanity.com/paper/2510.10864/) (2025)
- [Modality-free Graph In-context Alignment](https://mlsanity.com/paper/2603.13434/) (2026)
- [On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance](https://mlsanity.com/paper/2204.06122/) (2022)
- [All the World's a (Hyper)Graph: A Data Drama](https://mlsanity.com/paper/2206.08225/) (2022)
- [Differential Privacy Analysis of Decentralized Gossip Averaging under Varying Threat Models](https://mlsanity.com/paper/2505.19969/) (2025)
- [Inferential SIR-GN: Scalable Graph Representation Learning](https://mlsanity.com/paper/2111.04826/) (2021)
- [Hardware/Software Co-Programmable Framework for Computational SSDs to Accelerate Deep Learning Service on Large-Scale Graphs](https://mlsanity.com/paper/2201.09189/) (2022)
- [Interactive Visualization of Protein RINs using NetworKit in the Cloud](https://mlsanity.com/paper/2203.01263/) (2022)
- [High-order Order Proximity-Incorporated, Symmetry and Graph-Regularized Nonnegative Matrix Factorization for Community Detection](https://mlsanity.com/paper/2203.03876/) (2022)
- [Strain-Minimizing Hyperbolic Network Embeddings with Landmarks](https://mlsanity.com/paper/2207.06775/) (2022)
- [Rethinking Efficiency and Redundancy in Training Large-scale Graphs](https://mlsanity.com/paper/2209.00800/) (2022)
- [Knowledge Graphs are not Created Equal: Exploring the Properties and Structure of Real KGs](https://mlsanity.com/paper/2311.06414/) (2023)
- [Viral Marketing in Social Networks with Competing Products](https://mlsanity.com/paper/2312.15819/) (2023)
- [SoK: Data Minimization in Machine Learning](https://mlsanity.com/paper/2508.10836/) (2025)

## Abstract (arXiv)

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 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 where 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 meta-data 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.