[Paper Review] SNAP: A General Purpose Network Analysis and Graph Mining Library

Jure Leskovec, Rok Sosič|arXiv (Cornell University)|Jun 24, 2016

Complex Network Analysis Techniques48 references127 citations

TL;DR

SNAP is a high-performance, open-source C++ (with Python module) library for large-scale network analysis and graph mining on single big-memory machines, supporting dynamic graphs and over 200 functions across 8 graph types, 20 generators, and 20 manipulation methods.

ABSTRACT

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.

Motivation & Objective

• Motivate the need for a scalable, flexible network analysis tool that can handle graphs with hundreds of millions of nodes on a single machine.
• Describe SNAP's architecture, data structures, and implementation details that balance performance, memory footprint, and dynamic graph capability.
• Present a comprehensive set of graph generation, manipulation, and analytics methods.
• Benchmark SNAP against existing single-machine systems to demonstrate speed and memory efficiency benefits.

Proposed method

• Introduce graph and network container abstractions that unify interfaces across multiple graph types (directed, undirected, multigraphs, with attributes).
• Describe a compact in-memory representation that uses a hash table of nodes with sorted adjacency vectors to balance flexibility and speed.
• Provide a uniform iterator-based API so algorithms are container-agnostic and reusable across graph types.
• Detail memory management via reference counting and binary (fast) load/save of graphs to disk.
• Overview of more than 140 graph algorithms and 200 total functions, including generators, manipulations, and analytics.
• Benchmark methodology comparing SNAP to NetworkX and iGraph on performance and memory usage.

Experimental results

Research questions

• RQ1 How does SNAP's data structures and layering enable scalable analysis of very large graphs on a single machine?
• RQ2 What are SNAP's core capabilities (containers, generators, algorithms) and how do they compare to existing tools in terms of performance and memory efficiency?
• RQ3 Can SNAP efficiently support dynamic graphs with online modifications while maintaining high performance?
• RQ4 How does SNAP fare against established libraries (NetworkX, iGraph) in speed and memory usage on large networks?
• RQ5 What is the scope of community detection, link prediction, diffusion, and other graph analytics available within SNAP?

Key findings

• SNAP can process networks with hundreds of millions of nodes and billions of edges on a single machine.
• SNAP offers over 140 graph algorithms and more than 200 functions across 8 graph/network types, 20 generators, and 20 manipulation methods.
• Compared to NetworkX, SNAP is about 1–2 orders of magnitude faster and uses ~50× less memory, enabling larger or faster analyses on the same hardware.
• Compared to iGraph, SNAP uses ~3× less memory and is significantly more flexible for dynamic graph modifications, with some algorithms being much faster or slower depending on use case.
• SNAP’s design enables dynamic modification of networks during computation at low cost, a key strength for online or streaming graph workloads.