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

Authors:

Rok SosičAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 8, Issue 1

Article No.: 1, Pages 1 - 20

https://doi.org/10.1145/2898361

Published: 15 July 2016 Publication History

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 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.

References

[1]

S. Arifuzzaman, M. Khan, and M. Marathe. 2013. PATRIC: A parallel algorithm for counting triangles in massive networks. In ACM International Conference on Information and Knowledge Management (CIKM). 529--538.

Digital Library

[2]

A.-L. Barabási and R. Albert. 1999. Emergence of scaling in random networks. Science 286, 5439 (1999), 509--512.

[3]

V. Batagelj and A. Mrvar. 1998. Pajek-program for large network analysis. Connections 21, 2 (1998), 47--57.

[4]

V. Batagelj and M. Zaveršnik. 2002. Generalized cores. ArXiv cs.DS/0202039 (Feb 2002).

[5]

A. A. Benczur, K. Csalogany, T. Sarlos, and M. Uher. 2005. Spamrank--fully automatic link spam detection. In International Workshop on Adversarial Information Retrieval on the Web.

[6]

B. Bollobás. 1980. A probabilistic proof of an asymptotic formula for the number of labelled regular graphs. European Journal of Combinatorics 1, 4 (1980), 311--316.

[7]

D. Chakrabarti, Y. Zhan, and C. Faloutsos. 2004. R-MAT: A recursive model for graph mining. In SIAM International Conference on Data Mining (SDM), Vol. 4. SIAM, 442--446.

[8]

G. Csardi and T. Nepusz. 2006. The igraph software package for complex network research. InterJournal, Complex Systems 1695, 5 (2006).

[9]

D. Easley and J. Kleinberg. 2010. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press.

Digital Library

[10]

A. D. Flaxman, A. M. Frieze, and J. Vera. 2006. A geometric preferential attachment model of networks. Internet Mathematics 3, 2 (2006), 187--205.

[11]

M. Gomez-Rodriguez, J. Leskovec, D. Balduzzi, and B. Schölkopf. 2014. Uncovering the structure and temporal dynamics of information propagation. Network Science 2, 01 (2014), 26--65.

[12]

M. Gomez-Rodriguez, J. Leskovec, and A. Krause. 2010. Inferring networks of diffusion and influence. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM, 1019--1028.

Digital Library

[13]

M. Gomez-Rodriguez, J. Leskovec, and A. Krause. 2012. Inferring networks of diffusion and influence. ACM Transactions on Knowledge Discovery from Data 5, 4, Article 21 (Feb. 2012), 37 pages.

Digital Library

[14]

M. Gomez-Rodriguez, J. Leskovec, and B. Schölkopf. 2013. Structure and dynamics of information pathways in online media. In ACM International Conference on Web Search and Data Mining (WSDM). ACM, 23--32.

Digital Library

[15]

J. E. Gonzalez, Y. Low, H. Gu, D. Bickson, and C. Guestrin. 2012. PowerGraph: Distributed graph-parallel computation on natural graphs. In USENIX Symposium on Operating Systems Design and Implementation (OSDI), Vol. 12. 2.

Digital Library

[16]

D. Gregor and A. Lumsdaine. 2005. The parallel BGL: A generic library for distributed graph computations. Parallel Object-Oriented Scientific Computing (POOSC) 2 (2005), 1--18.

[17]

A. Hagberg, P. Swart, and D. S. Chult. 2008. Exploring Network Structure, Dynamics, and Function using NetworkX. Technical Report. Los Alamos National Laboratory (LANL).

[18]

D. Hallac, J. Leskovec, and S. Boyd. 2015. Network lasso: Clustering and optimization in large graphs. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM, 387--396.

Digital Library

[19]

M. O. Jackson. 2008. Social and Economic Networks. Vol. 3. Princeton university press Princeton.

[20]

U. Kang, C. E. Tsourakakis, and C. Faloutsos. 2009. Pegasus: A peta-scale graph mining system implementation and observations. In IEEE International Conference on Data Mining (ICDM). IEEE, 229--238.

Digital Library

[21]

J. Kim, W.-S. Han, S. Lee, K. Park, and H. Yu. 2014. OPT: A new framework for overlapped and parallel triangulation in large-scale graphs. In ACM SIGMOD International Conference on Management of Data (SIGMOD). ACM, 637--648.

Digital Library

[22]

M. Kim and J. Leskovec. 2011a. Modeling social networks with node attributes using the multiplicative attribute graph model. In Conference on Uncertainty in Artificial Intelligence (UAI).

[23]

M. Kim and J. Leskovec. 2011b. The network completion problem: Inferring missing nodes and edges in networks. In SIAM International Conference on Data Mining (SDM). 47--58.

[24]

M. Kim and J. Leskovec. 2012a. Latent multi-group membership graph model. In International Conference on Machine Learning (ICML).

[25]

M. Kim and J. Leskovec. 2012b. Multiplicative attribute graph model of real-world networks. Internet Mathematics 8, 1--2 (2012), 113--160.

[26]

M. Kim and J. Leskovec. 2013. Nonparametric multi-group membership model for dynamic networks. In Advances in Neural Information Processing Systems (NIPS). 1385--1393.

[27]

R. Kumar, P. Raghavan, S. Rajagopalan, D. Sivakumar, A. Tomkins, and E. Upfal. 2000. Stochastic models for the web graph. In Annual Symposium on Foundations of Computer Science. IEEE, 57--65.

Digital Library

[28]

H. Kwak, C. Lee, H. Park, and S. Moon. 2010. What is twitter, a social network or a news media?. In WWW’10.

Digital Library

[29]

A. Kyrola, G. Blelloch, and C. Guestrin. 2012. GraphChi: Large-scale graph computation on just a PC. In USENIX Symposium on Operating Systems Design and Implementation (OSDI). 31--46.

Digital Library

[30]

J. Leskovec, L. Backstrom, and J. Kleinberg. 2009. Meme-tracking and the dynamics of the news cycle. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). 497--506.

Digital Library

[31]

J. Leskovec, D. Chakrabarti, J. Kleinberg, C. Faloutsos, and Z. Ghahramani. 2010. Kronecker graphs: An approach to modeling networks. Journal of Machine Learning Research 11 (2010), 985--1042.

Digital Library

[32]

J. Leskovec and E. Horvitz. 2014. Geospatial structure of a planetary-scale social network. IEEE Transactions on Computational Social Systems 1, 3 (2014), 156--163.

[33]

J. Leskovec, J. Kleinberg, and C. Faloutsos. 2005. Graphs over time: Densification laws, shrinking diameters and possible explanations. In ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (KDD). ACM, 177--187.

Digital Library

[34]

J. Leskovec and A. Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data. (June 2014).

[35]

P. Lofgren, S. Banerjee, and A. Goel. 2016. Personalized PageRank estimation and search: A bidirectional approach. In ACM International Conference on Web Search and Data Mining (WSDM). ACM.

Digital Library

[36]

P. A. Lofgren, S. Banerjee, A. Goel, and C Seshadhri. 2014. Fast-ppr: Scaling personalized pagerank estimation for large graphs. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM, 1436--1445.

Digital Library

[37]

G. Malewicz, M. H. Austern, A. J. C. Bik, J. C. Dehnert, I. Horn, N. Leiser, and G. Czajkowski. 2010. Pregel: A system for large-scale graph processing. In ACM SIGMOD International Conference on Management of Data (SIGMOD). ACM, 135--146.

Digital Library

[38]

J. McAuley and J. Leskovec. 2012. Learning to discover social circles in ego networks. In Advances in Neural Information Processing Systems (NIPS).

[39]

J. McAuley and J. Leskovec. 2014. Discovering social circles in ego networks. ACM Transactions on Knowledge Discovery from Data 8, 1, Article 4 (Feb. 2014), 28 pages.

Digital Library

[40]

R. Milo, N. Kashtan, S. Itzkovitz, M. E. J. Newman, and U. Alon. 2003. On the uniform generation of random graphs with prescribed degree sequences. arXiv preprint cond-mat/0312028 (2003).

[41]

M. Newman. 2003. The structure and function of complex networks. SIAM Rev. 45, 2 (2003), 167--256.

Digital Library

[42]

M. Newman. 2010. Networks: An Introduction. OUP Oxford.

Digital Library

[43]

J. O’Madadhain, D. Fisher, P. Smyth, S. White, and Y. Boey. 2005. Analysis and visualization of network data using JUNG. Journal of Statistical Software 10, 2 (2005), 1--35.

[44]

L. Page, S. Brin, R. Motwani, and T. Winograd. November 1999. The Pagerank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab.

[45]

Y. Perez, R. Sosič, A. Banerjee, R. Puttagunta, M. Raison, P. Shah, and J. Leskovec. 2015. Ringo: Interactive graph analytics on big-memory machines. In ACM SIGMOD International Conference on Management of Data (SIGMOD). 1105--1110.

Digital Library

[46]

E. Ravasz and A.-L. Barabási. 2003. Hierarchical organization in complex networks. Physical Review E 67, 2 (2003), 026112.

[47]

S. Salihoglu and J. Widom. 2013. GPS: A graph processing system. In International Conference on Scientific and Statistical Database Management. ACM, 22.

Digital Library

[48]

C. Suen, S. Huang, C. Eksombatchai, R. Sosič, and J. Leskovec. 2013. NIFTY: A system for large scale information flow tracking and clustering. In International Conference on World Wide Web (WWW). International World Wide Web Conferences Steering Committee, 1237--1248.

Digital Library

[49]

D. J. Watts and S. H. Strogatz. 1998. Collective dynamics of small-world networks. Nature 393, 6684 (1998), 440--442.

[50]

R. S. Xin, J. E. Gonzalez, M. J. Franklin, and I. Stoica. 2013. GraphX: A resilient distributed graph system on Spark. In ACM International Workshop on Graph Data Management Experiences and Systems. ACM,  2.

Digital Library

[51]

J. Yang and J. Leskovec. 2012. Community-affiliation graph model for overlapping network community detection. In IEEE International Conference on Data Mining (ICDM). IEEE, 1170--1175.

Digital Library

[52]

J. Yang and J. Leskovec. 2013. Overlapping community detection at scale: A nonnegative matrix factorization approach. In ACM International Conference on Web Search and Data Mining (WSDM). ACM, 587--596.

Digital Library

[53]

J. Yang and J. Leskovec. 2014. Overlapping communities explain core-periphery organization of networks. Proc. IEEE 102, 12 (Dec 2014), 1892--1902.

[54]

J. Yang, J. McAuley, and J. Leskovec. 2013. Community detection in networks with node attributes. In IEEE International Conference on Data Mining (ICDM).

[55]

J. Yang, J. McAuley, and J. Leskovec. 2014. Detecting cohesive and 2-mode communities in directed and undirected networks. In ACM International Conference on Web Search and Data Mining (WSDM).

Digital Library

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Index Terms

SNAP: A General-Purpose Network Analysis and Graph-Mining Library
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Main memory engines
  2. Information systems applications
    1. Computing platforms
    2. Data mining

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 8, Issue 1

January 2017

363 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2973184

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2016 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 July 2016

Accepted: 01 February 2016

Received: 01 February 2016

Published in TIST Volume 8, Issue 1

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Lucena-Padros HBravo-Gil NTous CRojano ESeoane-Zonjic PFernández RRanea JAntiñolo GBorrego S(2024)Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung DiseaseBiomolecules10.3390/biom1402016414:2(164)Online publication date: 30-Jan-2024
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