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Graphs over time: densification laws, shrinking diameters and possible explanations
Abstract
How do real graphs evolve over time? What are "normal" growth patterns in social, technological, and information networks? Many studies have discovered patterns in static graphs, identifying properties in a single snapshot of a large network, or in a very small number of snapshots; these include heavy tails for in- and out-degree distributions, communities, small-world phenomena, and others. However, given the lack of information about network evolution over long periods, it has been hard to convert these findings into statements about trends over time.Here we study a wide range of real graphs, and we observe some surprising phenomena. First, most of these graphs densify over time, with the number of edges growing super-linearly in the number of nodes. Second, the average distance between nodes often shrinks over time, in contrast to the conventional wisdom that such distance parameters should increase slowly as a function of the number of nodes (like O(log n) or O(log(log n)).Existing graph generation models do not exhibit these types of behavior, even at a qualitative level. We provide a new graph generator, based on a "forest fire" spreading process, that has a simple, intuitive justification, requires very few parameters (like the "flammability" of nodes), and produces graphs exhibiting the full range of properties observed both in prior work and in the present study.
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“Finally, the study of networks in a variety of applications assumes static networks, with some notable exceptions.21”
From A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science (2014) · cites this paper as [21]
Citations
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Milgram, S. (1967) · PsycEXTRA Dataset · doi:10.1037/e400002009-005 · Cited by 5,528
On power-law relationships of the Internet topology
Faloutsos, M., Faloutsos, P., & Faloutsos, C. (1999) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/316188.316229 · Cited by 4,279
Self-citationDiameter of the World-Wide Web
Albert, R., Jeong, H., & Barabási, A.-Ĺ. (1999) · Nature · doi:10.1038/43601 · Cited by 4,240
The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools
Hall, B. H., Jaffe, A. B., & Trajtenberg, M. (2001) · National Bureau of Economic Research · doi:10.3386/w8498 · Cited by 3,450
R-MAT: A Recursive Model for Graph Mining
Chakrabarti, D., Zhan, Y., & Faloutsos, C. (2004) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972740.43 · Cited by 1,275
Self-citationA Brief History of Generative Models for Power Law and Lognormal Distributions
Mitzenmacher, M. (2004) · Internet Mathematics · doi:10.1080/15427951.2004.10129088 · Cited by 1,188
Identity and Search in Social Networks
Watts, D. J., Dodds, P. S., & Newman, M. E. J. (2002) · Science · doi:10.1126/science.1070120 · Cited by 1,060
Research works printed but not linked: 4
- 10.5555/647908.740141 · DOI
- 10.5555/779232 · DOI
- A. Broder , R. Kumar , F. Maghoul , P. Raghavan , S. Rajagopalan , R. Stata , A. Tomkins , and J. Wiener . Graph structure in the web: experiments and models . In Proceedings of World Wide Web Conference , 2000 .]] A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, and J. Wiener. Graph structure in the web: experiments and models. In Proceedings of World Wide Web Conference, 2000.]]
- 10.5555/795666.796570 · DOI
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Chen, C., Yan, X., Zhu, F., Han, J., & Yu, P. S. (2009) · Knowledge and Information Systems · doi:10.1007/s10115-009-0228-9 · Cited by 88
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Hébert‐Dufresne, L., Grochow, J. A., & Allard, A. (2016) · Scientific Reports · doi:10.1038/srep31708 · Cited by 65
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IMGPU: GPU-Accelerated Influence Maximization in Large-Scale Social Networks
Liu, X., Li, M., Li, S., Peng, S., Liao, X., & Lu, X. (2013) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2013.41 · Cited by 64
Metadata
- Authors
- Leskovec, J., Kleinberg, J., & Faloutsos, C.
- Affiliations
- Carnegie Mellon University (US)
- Cornell University (US)
- Venue
- Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
- Publisher
- ACM
- Published
- 2005-08-21
- Pages
- 177-187
- OpenAlex
- W2111708605
- Cited by
- 2,281
- References
- 31
- Citations by year
- 2026: 42 · 2025: 88 · 2024: 78 · 2023: 79 · 2022: 85 · 2021: 135 · 2020: 118 · 2019: 128 · 2018: 146 · 2017: 127 · 2016: 157 · 2015: 164 · 2014: 166 · 2013: 168 · 2012: 155
- FWCI
- 77.15
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Graph Theory and Algorithms
- Physical Sciences › Mathematics › Discrete Mathematics and Combinatorics › Graph theory and applications
- Keywords
- graph evolution, network densification, superlinear growth, forest fire model, social networks, technological networks, information networks, small-world effect, degree distribution, community structure
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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.
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