Twitter User-Hashtag Bipartite Network (Munmun)
Twitter user–hashtag network, a network of the Koblenz Network Collection (KONECT) (User–hashtag usage). KONECT's file for it has the header `% bip positive`, i.e. a bipartite network with weights marked `positive`. This copy holds 4,664,605 edges between 175,214 user nodes and 530,418 hashtag nodes, timed from 7 September 2006 to 6 November 2009.
Cite this dataset at
twitter-user-hashtag.echo.directory
Analytics
The full graph has 705,632 nodes; this view shows the 1,000-node sample the pipeline drew for display. Node size is centrality, colour is community.
Computed over the full corpus.
Record
- Catalog key
munmun-twitter-ex- Authors
- De Choudhury, Munmun; Lin, Yu-Ru; Sundaram, Hari; Candan, Kasim Selcuk; Xie, Lexing; Kelliher, Aisling
- Year
- 2010
- Source paper
- How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media?
- DOI / arXiv
- 10.1609/icwsm.v4i1.14024
- License
- CC BY-SA 2.0 Germany (as stated by KONECT) (LicenseRef-CC-BY-SA-2.0-DE)
- Records
- 705,632
- Nodes
- 705,632
- Edges
- 1,890,661
- Nodes are
- user / hashtag
- Edges are
- hashtag usage
- Attributes
- nodes have no attributes; edges have attributes
- Languages
- en
- Collected
- 2009 (2000s)
- Tasks
- From the graph: link prediction, community detection
- Region
- Global (inferred: the platform is used worldwide)
Provenance
- Originally published at
- https://konect.cc/networks/munmun_twitterex_ut/ unverified
- Original files as received
- The source files, unconverted, kept for reference: mirrored on echo.directory on 2026-10-02, 27.4 MB across 1 files. Checksums in MANIFEST.json. For use, start with the data package below.
- Data package
- Version 1.0.0 of this dataset as an ECHO Data Package: edges (4,664,605 rows).
- Structure
- Bipartite network, Multigraph (repeated links), Temporal (timestamped)
Citations
How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media?
Munmun De Choudhury, Y. Lin, Hari Sundaram, K. Candan, Lexing Xie
Journal Article · International Conference on Web and Social Media · 2010
Cited by
- Asymmetric Transitivity Preserving Graph Embedding
Mingdong Ou, Peng Cui et al. · Knowledge Discovery and Data Mining · 2016 · cited 1.3K× - A Survey on Network Embedding
Peng Cui, Xiao Wang et al. · IEEE Transactions on Knowledge and Data Engineering · 2017 · cited 1.2K× - Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose
F. Morstatter, J. Pfeffer et al. · International Conference on Web and Social Media · 2013 · cited 1K× - Information diffusion in online social networks: a survey
Adrien Guille, H. Hacid et al. · SGMD · 2013 · cited 925× - Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Alexandra Olteanu, Carlos Castillo et al. · Frontiers in Big Data · 2019 · cited 771×
Show all 10
- Sentiment Knowledge Discovery in Twitter Streaming Data
A. Bifet, E. Frank · IFIP Working Conference on Database Semantics · 2010 · cited 658× - Community detection in networks: A multidisciplinary review
M. Javed, M. S. Younis et al. · Journal of Network and Computer Applications · 2018 · cited 458× - Bad news travel fast: a content-based analysis of interestingness on Twitter
N. Naveed, Thomas Gottron et al. · Web Science Conference · 2011 · cited 432× - Network Sampling: From Static to Streaming Graphs
Nesreen Ahmed, Jennifer Neville et al. · TKDD · 2012 · cited 286× - Whisper: Tracing the Spatiotemporal Process of Information Diffusion in Real Time
Nan Cao, Y. Lin et al. · IEEE Transactions on Visualization and Computer Graphics · 2012 · cited 233×
Citation data from Semantic Scholar, retrieved 2026-10-19.
How to cite
BibTeX
@misc{echo2026,
title = {ECHO: Exploratory Compendium of Humanity Online},
author = {Switzer, Braden and Zafarani, Reza},
year = {2026},
url = {https://echo.directory},
note = {Syracuse University}
}
@article{De_Choudhury_2010,
title = {How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media?},
volume = {4},
issn = {2162-3449},
url = {http://dx.doi.org/10.1609/icwsm.v4i1.14024},
doi = {10.1609/icwsm.v4i1.14024},
number = {1},
journal = {Proceedings of the International AAAI Conference on Web and Social Media},
publisher = {Association for the Advancement of Artificial Intelligence (AAAI)},
author = {De Choudhury, Munmun and Lin, Yu-Ru and Sundaram, Hari and Candan, Kasim Selcuk and Xie, Lexing and Kelliher, Aisling},
year = {2010},
month = May,
pages = {34–41}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
De Choudhury, M., Lin, Y.-R., Sundaram, H., Candan, K. S., Xie, L., & Kelliher, A. (2010). Twitter User-Hashtag Bipartite Network (Munmun) [Data set]. https://doi.org/10.1609/icwsm.v4i1.14024
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
De Choudhury, Munmun, et al. Twitter User-Hashtag Bipartite Network (Munmun). 2010, doi.org/10.1609/icwsm.v4i1.14024.