The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality
Open accessAbstract
Using computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of these disciplines. In these networks two scientists are considered connected if they have coauthored one or more papers together. Here we study a variety of nonlocal statistics for these networks, such as typical distances between scientists through the network, and measures of centrality such as closeness and betweenness. We further argue that simple networks such as these cannot capture variation in the strength of collaborative ties and propose a measure of collaboration strength based on the number of papers coauthored by pairs of scientists, and the number of other scientists with whom they coauthored those papers.
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
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557
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
Bollobás, B. (2001) · doi:10.1017/cbo9780511814068 · Cited by 2,639 · cited 2×
“In a standard random graph (Erdős and Rényi, 1960; Bollobás, 2001), for instance, ` = log n/ log z, where z is the average degree of a vertex, the average number of collaborators in our terminology.”
Computer Networks As Social Networks
Wellman, B. (2001) · Science · doi:10.1126/science.1065547 · Cited by 918
Cited by
Statistical mechanics of complex networks
Albert, R., & Barabási, A.-L. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.47 · Cited by 20,471
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Self-citationCommunity structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502
Self-citationFinding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931
Self-citationComplex 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
Network analysis: An integrative approach to the structure of psychopathology
Borsboom, D., & Cramer, A. O. J. (2013) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050212-185608 · Cited by 4,359
Statistical physics of social dynamics
Castellano, C., Fortunato, S., & Loreto, V. (2009) · Reviews of Modern Physics · doi:10.1103/revmodphys.81.591 · Cited by 4,119
Synchronization in complex networks
Arenas, A., Díaz-Guilera, A., Kurths, J., Moreno, Y., & Zhou, C. (2008) · Physics Reports · doi:10.1016/j.physrep.2008.09.002 · Cited by 3,713
Cascade-based attacks on complex networks
Motter, A. E., & Lai, Y.-C. (2002) · Physical Review E · doi:10.1103/physreve.66.065102 · Cited by 1,929
Self-similar community structure in a network of human interactions
Guimerà, R., Danon, L., Díaz-Guilera, A., Giralt, F., & Arenas, A. (2003) · Physical Review E · doi:10.1103/physreve.68.065103 · Cited by 1,515
Constructing bibliometric networks: A comparison between full and fractional counting
Perianes-Rodriguez, A., Waltman, L., & van Eck, N. J. (2016) · Journal of Informetrics · doi:10.1016/j.joi.2016.10.006 · Cited by 1,416
Random Walks on Complex Networks
Noh, J. D., & Rieger, H. (2004) · Physical Review Letters · doi:10.1103/physrevlett.92.118701 · Cited by 1,148
Clustering in weighted networks
Opsahl, T., & Panzarasa, P. (2009) · Social Networks · doi:10.1016/j.socnet.2009.02.002 · Cited by 1,037
Detecting rich-club ordering in complex networks
Colizza, V., Flammini, A., Serrano, M. A., & Vespignani, A. (2006) · Nature Physics · doi:10.1038/nphys209 · Cited by 1,001
Scale-Free Networks from Varying Vertex Intrinsic Fitness
Caldarelli, G., Capocci, A., De Los Rios, P., & Muñoz, M. A. (2002) · Physical Review Letters · doi:10.1103/physrevlett.89.258702 · Cited by 794
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
Homological scaffolds of brain functional networks
Petri, G., Expert, P., Turkheimer, F., Carhart-Harris, R., Nutt, D., Hellyer, P. J., & Vaccarino, F. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.0873 · Cited by 675
Cronin, B. (2001) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.1097 · Cited by 500
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
Maximal planar networks with large clustering coefficient and power-law degree distribution
Zhou, T., Yan, G., & Wang, B.-H. (2005) · Physical Review E · doi:10.1103/physreve.71.046141 · Cited by 265
Koseoglu, M. A. (2016) · BRQ Business Research Quarterly · doi:10.1016/j.brq.2016.02.001 · Cited by 242
Fowler, J. H., & Aksnes, D. W. (2007) · Scientometrics · doi:10.1007/s11192-007-1777-2 · Cited by 219
Structure and evolution of co-authorship network in an interdisciplinary research field
Liu, P., & Xia, H. (2015) · Scientometrics · doi:10.1007/s11192-014-1525-y · Cited by 84
Efficient computation of the shapley value for centrality in networks
Aadithya, K. V., Ravindran, B., Michalak, T. P., & Jennings, N. R. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-17572-5_1 · Cited by 59
Marin, E. C., Morris, B. J., Stürner, T., Champion, A. S., Krzeminski, D., Badalamente, G., Gkantia, M., Dunne, C. R., Eichler, K., Takemura, S.-Y., et al. (2024) · doi:10.7554/elife.97766.1 · Cited by 44
Ideological biases in social sharing of online information about climate change
Cann, T. J. B., Weaver, I. S., & Williams, H. T. P. (2021) · PLOS ONE · doi:10.1371/journal.pone.0250656 · Cited by 38
Topological Structure of Urban Street Networks from the Perspective of Degree Correlations
Jiang, B., Duan, Y., Lu, F., Yang, T., & Zhao, J. (2014) · Environment and Planning B: Planning and Design · doi:10.1068/b39110 · Cited by 38
Marin, E. C., Morris, B. J., Stürner, T., Champion, A. S., Krzeminski, D., Badalamente, G., Gkantia, M., Dunne, C. R., Eichler, K., Takemura, S.-Y., et al. (2023) · doi:10.1101/2023.06.05.543407 · Cited by 33
Scaling laws in enzyme function reveal a new kind of biochemical universality
Gagler, D. C., Karas, B., Kempes, C. P., Malloy, J., Mierzejewski, V., Goldman, A. D., Kim, H., & Walker, S. I. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2106655119 · Cited by 34
Disrupting networks of hate: characterising hateful networks and removing critical nodes
Alorainy, W., Burnap, P., Liu, H., Williams, M., & Giommoni, L. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00818-z · Cited by 15
Marin, E. C., Morris, B. J., Stürner, T., Champion, A. S., Krzeminski, D., Badalamente, G., Gkantia, M., Dunne, C. R., Eichler, K., Takemura, S.-Y., et al. (2024) · eLife · doi:10.7554/elife.97766 · Cited by 13
Metadata
- Authors
- Newman, M. E. J.
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2001-06-28
- Volume
- 64
- Issue
- 1
- Pages
- 016132-016132
- ISSN
- 1063-651X (ISSN-L)
- 1095-3787
- OpenAlex
- W1671906456
- Cited by
- 2,613
- References
- 5
- FWCI
- 31.11
- 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.
