The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Growing network with local rules: Preferential attachment, clustering hierarchy, and degree correlations
Open accessAbstract
The linear preferential attachment hypothesis has been shown to be quite successful in explaining the existence of networks with power-law degree distributions. It is then quite important to determine if this mechanism is the consequence of a general principle based on local rules. In this work it is claimed that an effective linear preferential attachment is the natural outcome of growing network models based on local rules. It is also shown that the local models offer an explanation for other properties like the clustering hierarchy and degree correlations recently observed in complex networks. These conclusions are based on both analytical and numerical results for different local rules, including some models already proposed in the literature.
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
A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae
Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., Lockshon, D., Narayan, V., Srinivasan, M., Pochart, P., et al. (2000) · Nature · doi:10.1038/35001009 · Cited by 4,706
Classes of small-world networks
Amaral, L. A. N., Scala, A., Barthélémy, M., & Stanley, H. E. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.200327197 · Cited by 3,051
Large-scale topological and dynamical properties of the Internet
Vázquez, A., Pastor-Satorras, R., & Vespignani, A. (2002) · Physical Review E · doi:10.1103/physreve.65.066130 · Cited by 712
Self-citationSize-dependent degree distribution of a scale-free growing network
Dorogovtsev, S. N., Mendes, J. F. F., & Samukhin, A. N. (2001) · Physical Review E · doi:10.1103/physreve.63.062101 · Cited by 311
Topology of technology graphs: Small world patterns in electronic circuits
Cancho, R. F. I., Janssen, C., & Solé, R. V. (2001) · Physical Review E · doi:10.1103/physreve.64.046119 · Cited by 265
Cited by
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Complex networks: structure and dynamics
BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010
Popularity versus similarity in growing networks
Papadopoulos, F., Kitsak, M., Serrano, M. Á., Boguñá, M., & Krioukov, D. (2012) · Nature · doi:10.1038/nature11459 · Cited by 634
Structural Information and Dynamical Complexity of Networks
Li, A., & Pan, Y. (2016) · IEEE Transactions on Information Theory · doi:10.1109/tit.2016.2555904 · Cited by 200
Coevolutionary networks with homophily and heterophily
Kimura, D., & Hayakawa, Y. (2008) · Physical Review E · doi:10.1103/physreve.78.016103 · Cited by 107
The memory of science: Inflation, myopia, and the knowledge network
Pan, R. K., Petersen, A. M., Pammolli, F., & Fortunato, S. (2018) · Journal of Informetrics · doi:10.1016/j.joi.2018.06.005 · Cited by 79
Evolving complexity: how tinkering shapes cells, software and ecological networks.
Solé, R., & Valverde, S. (2020) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2019.0325 · Cited by 37
Attention Inequality in Social Media
Zhu, L., & Lerman, K. (2016) · arXiv · doi:10.48550/arxiv.1601.07200 · Cited by 13
Colonna, G. (2024) · Livers · doi:10.3390/livers4020016 · Cited by 7
Metadata
- Authors
- Vázquez, A.
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2003-05-07
- Volume
- 67
- Issue
- 5
- Pages
- 056104-056104
- ISSN
- 1063-651X (ISSN-L)
- 1095-3787
- OpenAlex
- W2007541434
- Cited by
- 563
- References
- 6
- FWCI
- 12.11
- 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
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.
