What Webgraph does

WebGraph is an open-source software framework for compressing and analyzing very large web graphs, maintained as a personal GitHub repository (github.com/vigna/webgraph) by a developer affiliated with the University of Milan (Università degli Studi di Milano), as inferred from the di.unimi.it domain references. The framework is written primarily in Java (97.2% of the codebase) with a Rust implementation, and provides algorithms for compressing web graphs using ζ (zeta) codes, gap compression, referentiation, intervalization, and lazy decompression. It includes the HyperBall algorithm for approximating neighbourhood functions, reachable nodes, and geometric centralities on massive graphs; the algorithm was notably used to determine that the Facebook social graph has an average of 3.74 degrees of separation. The project also distributes pre-collected large graph datasets gathered from WebBase, UbiCrawler, and BUbiNG, hosted at law.di.unimi.it.

The product serves academic researchers studying web and social network graphs, data scientists working with large-scale graph data via Python bindings, and organizations (Facebook, Software Heritage, Common Crawl, JGraphT) that have applied the framework to specific graph analysis problems. Distribution is entirely free and self-serve via Maven Central, GitHub, crates.io, and PyPI, with no commercial pricing, no licensing fees, and no direct monetization. Adoption is driven by academic citations, conference papers (notably WWW 2004), and organic discovery. Partnerships are limited to community-driven integrations such as JGraphT adapters and third-party Python bindings.

The business model is effectively a community-supported academic software project rather than a commercial enterprise. There is no disclosed revenue, no funding rounds, no parent company, no management team structure, and no M&A activity. The latest stable release is version 3.6.12 (2024), with the framework operating under LGPL-2.1+ and Apache Software License 2.0. Note: firmographic inputs (year_founded: 1924; num_employees: 1,001-5,000) appear inconsistent with the nature of an open-source academic GitHub repository and should be treated as unreliable.

Name

Webgraph

Website

https://webgraph.di.unimi.it

Company type

Private

Founded year

1924

Operating status

Operating

Headcount range

1,001–5,000 employees

Short description

WebGraph is an open-source Java/Rust framework for compressing and analyzing very large web and social graphs, maintained by a University of Milan researcher and used by academic institutions, Facebook research, Software Heritage, and Common Crawl. It is distributed free under LGPL-2.1+ and Apache 2.0.

Ownership category

akta.pro rank

Product category

Graph Compression Software

NAICS

Web Search Portals and All Other Information Services (519290), Web Search Portals, Libraries, Archives, and Other Information Services (5192)

SIC

Services-Computer Programming, Data Processing, Etc. (7370)

akta.pro primary industry

Graph Databases (HDAEAAAE)

akta.pro secondary industry

GraphQL, gRPC & Modern API Protocols (BPAMAOAK)

Keywords

• Graph compression framework
• Web graph analysis
• Graph algorithms library
• Social network graphs
• Large-scale graph processing

Headquarters

HQ city

Milano

HQ country

Italy

HQ region

Europe

Markets served

GTM type

B2B

Offering type

Software

Cost components

Technology or R&D, Personnel, Infrastructure

Revenue model

1. Open Source Distribution: Free open-source framework distributed under LGPL-2.1+ and Apache Software License 2.0. No commercial revenue model disclosed; project appears to be academic/research-oriented with no direct monetization.

Go-to-market motion1 record

Distribution channels4 records

Marketing channels3 records

Core offering

Webgraph is an open-source software framework for compressing web graphs and large social graphs, enabling efficient storage and on-the-fly analysis of massive graph datasets. It provides a Java core (with a Rust port and Python bindings) and ships companion tools such as the HyperBall algorithm for hyperbolic-distance-based graph analytics. The framework also publishes downloadable web graph datasets and is licensed under LGPL-2.1+ and Apache 2.0.

Product overview

WebGraph is a single unified framework for graph compression and analysis, consisting of core compression algorithms (ζ codes, gap compression, referentiation, intervalization), the HyperBall algorithm for analyzing massive graphs, and supporting datasets. The framework is implemented in Java (97.2% of codebase) with Rust bindings, distributed under LGPL 2.1+ or Apache 2.0 licenses. It also includes Python bindings and JGraphT adapters for interoperability.

Differentiator

Problem solved

Functional benefit

Products and services

• Webgraph Framework An open-source Java (with Rust and Python ports) framework for compressing web graphs and large social graphs, providing lazy decompression, ζ codes, gap coding, referentiation, and intervalization for in-memory and on-the-fly graph analysis. Targeted at researchers, data scientists, and large-scale web platform engineering teams.
• HyperBall A distributed, in-compression algorithm distributed with Webgraph that computes proximity centrality (e.g., closeness) and hyperbolic-distance-based statistics on very large graphs without fully decompressing them. Useful for analyzing billion-scale social and web graphs.
• Webgraph Datasets Downloadable, large-scale web graph datasets published by the Webgraph project (e.g., derived from Web Data Commons / Common Crawl) that serve as standard inputs for benchmarking compression and graph-traversal algorithms.

Quantifiable outcome

• Compression enables studying web graphs with billions of nodes using standard hardware
• +2 more outcomes

Named customers4 records

Segments3 records

Ideal customer profiles3 records

Technology focussed Yes

API detail

Has API

Yes

API docs

API detail

Core technology

AI maturity

App detail

Integration2 records

Feature5 records

Scale indicators5 records

Recent moves5 records

Expansion highlights4 records

Broad incumbents

• TigerGraph: Enterprise graph database optimized for massive-scale analytics. TigerGraph is a broad incumbent competing for the same large-graph analytical workloads where WebGraph's compression technology is most relevant.
• Neo4j: Leading commercial graph database platform. Neo4j is a broad incumbent in the same overarching graph technology space, but offers a full property-graph database product rather than specialized compression algorithms.
• Apache TinkerPop / Gremlin: Graph computing framework providing a standard traversal language (Gremlin) over various graph backends. It is a broad incumbent in the graph tooling ecosystem, comparable to WebGraph in serving developers building graph applications, though with a more standardized API focus.

Direct peers

• igraph: Cross-language graph library (Python, R, C) for network analysis. igraph is one of the closest functional peers to WebGraph for graph algorithms, with both serving academic and data science communities analyzing large network datasets.
• SNAP (Stanford Network Analysis Project): Stanford's open-source graph analysis framework targeting very large networks. SNAP is a near-direct peer in the academic large-graph analysis space, with similar customer base of researchers and data scientists working on web and social network data.
• JGraphT: Java graph library that already maintains official WebGraph adapters. JGraphT is both a technology partner and a direct peer, providing general-purpose graph data structures and algorithms in Java that overlap with WebGraph's capabilities.
• NetworkX: Python library for graph analysis. WebGraph's PyPI bindings directly overlap with NetworkX's user base, and both target developers working with large graph datasets, though NetworkX focuses on in-memory analysis while WebGraph emphasizes compression.

Emerging players

• Memgraph: In-memory graph database built for streaming and real-time analytics. Memgraph is an emerging player in the graph database market, partially overlapping with WebGraph in serving customers who need to work with very large dynamic graphs.
• GraphBLAS: Standard for graph algorithms expressed via linear algebra, with implementations including SuiteSparse:GraphBLAS. It is an emerging player in the academic/standards community for graph algorithm specification, overlapping with WebGraph's research-driven user base.
• Gephi: Open-source interactive graph visualization and exploration platform. Gephi serves a similar academic and data science audience analyzing large networks, complementing rather than directly competing with WebGraph's compression focus.

Market position

Strengths5 records

Weaknesses5 records

Competitive moat4 records

Key risks6 records

Key highlights6 records

Customer concentration

• GitHubgithub.com/vigna/webgraph
• Xx.com/lastatale
• Facebookfacebook.com/lastatale
• LinkedInlinkedin.com/company/university-of-milan

Revenue estimate

Valuation estimate

Number of profiles

Funding overview

Funding rounds

Investors

Funding detail is available on the Subscription and Enterprise plan.Contact sales →

M&A

Investments

M&A and investment is available on the Subscription and Enterprise plan.Contact sales →

Frequently asked questions about Webgraph