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Large Scale Network Analytics with SNAP: Tutorial at the World Wide Web 2015 Conference

Authors:

Rok Sosic,

Jure LeskovecAuthors Info & Claims

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

Pages 1537 - 1538

https://doi.org/10.1145/2740908.2744708

Published: 18 May 2015 Publication History

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Abstract

Many techniques for the modeling, analysis and optimization of Web related datasets are based on studies of large scale networks, where a network can contain hundreds of millions of nodes and billions of edges. Network analysis tools must provide not only extensive functionality, but also high performance in processing these large networks. The tutorial will present Stanford Network Analysis Platform (SNAP), a general purpose, high performance system for analysis and manipulation of large networks. SNAP is being used widely in studies of the Web datasets. SNAP consists of open source software, which provides a rich set of functions for performing network analytics, and a popular repository of publicly available real world network datasets. SNAP software APIs are available in Python and C++.

The tutorial will cover all aspects of SNAP, including APIs and datasets. The tutorial will include a hands-on component, where the participants will have the opportunity to use SNAP on their computers.

References

[1]

Jure Leskovec and Andrej Krevl. SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data, June 201

Google Scholar

[2]

Jure Leskovec and Rok Sosic. SNAP: A general purpose network analysis and graph mining library in C++. http://snap.stanford.edu/snap, June 2014.

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Cited By

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Ullah AMeng Y(2025)Finding influential nodes via graph embedding and hybrid centrality in complex networksChaos, Solitons & Fractals10.1016/j.chaos.2025.116151194(116151)Online publication date: May-2025
https://doi.org/10.1016/j.chaos.2025.116151
Lovera FCardinale YHomsi M(2021)Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning ClassificationElectronics10.3390/electronics1022273910:22(2739)Online publication date: 10-Nov-2021
https://doi.org/10.3390/electronics10222739
Dong YJohnson RXu JChawla NMatwin SYu SFarooq F(2017)Structural Diversity and HomophilyProceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/3097983.3098116(807-816)Online publication date: 13-Aug-2017
https://dl.acm.org/doi/10.1145/3097983.3098116

Index Terms

Large Scale Network Analytics with SNAP: Tutorial at the World Wide Web 2015 Conference
1. Information systems
  1. Information systems applications

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Published In

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

May 2015

1602 pages

ISBN:9781450334730

DOI:10.1145/2740908

General Chairs:
Aldo Gangemi
National Research Council, Italy & Paris 13 University-CNRS, France
,
Stefano Leonardi
Sapienza University of Rome, Italy
,
Alessandro Panconesi
Sapienza University of Rome, Italy

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

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Published: 18 May 2015

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WWW '15

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IW3C2

WWW '15: 24th International World Wide Web Conference

May 18 - 22, 2015

Florence, Italy

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Ullah AMeng Y(2025)Finding influential nodes via graph embedding and hybrid centrality in complex networksChaos, Solitons & Fractals10.1016/j.chaos.2025.116151194(116151)Online publication date: May-2025
https://doi.org/10.1016/j.chaos.2025.116151
Lovera FCardinale YHomsi M(2021)Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning ClassificationElectronics10.3390/electronics1022273910:22(2739)Online publication date: 10-Nov-2021
https://doi.org/10.3390/electronics10222739
Dong YJohnson RXu JChawla NMatwin SYu SFarooq F(2017)Structural Diversity and HomophilyProceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/3097983.3098116(807-816)Online publication date: 13-Aug-2017
https://dl.acm.org/doi/10.1145/3097983.3098116

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

Dynamic Network Analytics: Tutorial Outline

A Large Scale Study of Data Center Network Reliability

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