The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Vertex similarity in networks
Open accessAbstract
We consider methods for quantifying the similarity of vertices in networks. We propose a measure of similarity based on the concept that two vertices are similar if their immediate neighbors in the network are themselves similar. This leads to a self-consistent matrix formulation of similarity that can be evaluated iteratively using only a knowledge of the adjacency matrix of the network. We test our similarity measure on computer-generated networks for which the expected results are known, and on a number of real-world networks.
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
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Hierarchical Organization of Modularity in Metabolic Networks
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A.-L. (2002) · Science · doi:10.1126/science.1073374 · Cited by 4,224
Defining and identifying communities in networks
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0400054101 · Cited by 2,450
Cited by
Navigability of complex networks
Boguñá, M., Krioukov, D., & Claffy, K. C. (2009) · Nature Physics · doi:10.1038/nphys1130 · Cited by 486
Deep Recursive Network Embedding with Regular Equivalence
Tu, K., Cui, P., Wang, X., Yu, P. S., & Zhu, W. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220068 · Cited by 179
Li, Z. (., Fang, X., & Sheng, O. R. L. (2018) · ACM Transactions on Management Information Systems · doi:10.1145/3131782 · Cited by 65
Metadata
- Authors
- Leicht, E. A., Holme, P., & Newman, M. E. J.
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2006-02-17
- Volume
- 73
- Issue
- 2
- Pages
- 026120-026120
- ISSN
- 1539-3755 (ISSN-L)
- 1550-2376
- OpenAlex
- W2037705937
- Cited by
- 916
- References
- 3
- FWCI
- 12.46
- Type
- journal article
- Language
- en
- Open access
- green
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: Vertex similarity in networks. https://app.cochat.ai/papers/10.1103/physreve.73.026120
- The paper
- Leicht, E. A., Holme, P., & Newman, M. E. J. (2006). Vertex similarity in networks. Physical Review E. https://doi.org/10.1103/physreve.73.026120
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.
