The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
E-Mail as Spectroscopy: Automated Discovery of Community Structure within Organizations
Abstract
We describe a method for the automatic identification of communities of practice from e-mail logs within an organization. We use a betweenness centrality algorithm that can rapidly find communities within a graph representing information flows. We apply this algorithm to an initial e-mail corpus of nearly 1 million messages collected over a 2-month span, and show that the method is effective at identifying true communities, both formal and informal, within these scale-free graphs. This approach also enables the identification of leadership roles within the communities. These studies are complemented by a qualitative evaluation of the results in the field.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Situated Learning: Legitimate Peripheral Participation
Lave, J., & Wenger, E. (1991) · Cambridge University Press eBooks · doi:10.1017/cbo9780511815355 · Cited by 35,324
Computer Networks As Social Networks
Wellman, B. (2001) · Science · doi:10.1126/science.1065547 · Cited by 918
Cited by
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Finding community structure in very large networks
Clauset, A., Newman, M. E. J., & Moore, C. (2004) · Physical Review E · doi:10.1103/physreve.70.066111 · Cited by 7,390
You are who you know: inferring user profiles in online social networks
Mislove, A., Viswanath, B., Gummadi, K. P., & Druschel, P. (2010) · Proceedings of the third ACM international conference on Web search and data mining · doi:10.1145/1718487.1718519 · Cited by 668
Community detection in Social Media: Performance and application considerations
Papadopoulos, S., Kompatsiaris, Y., Vakali, A., & Spyridonos, P. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-011-0224-z · Cited by 615
Community detection in networks: A multidisciplinary review
Javed, M. A., Younis, M. S., Latif, S., Qadir, J., & Baig, A. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.02.011 · Cited by 499
Metadata
- Authors
- Tyler, J. R., Wilkinson, D. M., & Huberman, B. A.
- Venue
- The Information Society
- Publisher
- Informa UK Limited
- Published
- 2005-04
- Volume
- 21
- Issue
- 2
- Pages
- 143-153
- ISSN
- 0197-2243 (ISSN-L)
- 1087-6537
- OpenAlex
- W2606584716
- Cited by
- 327
- References
- 2
- FWCI
- 19.60
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: E-Mail as Spectroscopy: Automated Discovery of Community Structure within Organizations. https://app.cochat.ai/papers/10.1080/01972240590925348
- The paper
- Tyler, J. R., Wilkinson, D. M., & Huberman, B. A. (2005). E-Mail as Spectroscopy: Automated Discovery of Community Structure within Organizations. The Information Society. https://doi.org/10.1080/01972240590925348
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
