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Networks, Crowds, and Markets: Reasoning about a Highly Connected World
Abstract
Are all film stars linked to Kevin Bacon? Why do the stock markets rise and fall sharply on the strength of a vague rumour? How does gossip spread so quickly? Are we all related through six degrees of separation? There is a growing awareness of the complex networks that pervade modern society. We see them in the rapid growth of the internet, the ease of global communication, the swift spread of news and information, and in the way epidemics and financial crises develop with startling speed and intensity. This introductory book on the new science of networks takes an interdisciplinary approach, using economics, sociology, computing, information science and applied mathematics to address fundamental questions about the links that connect us, and the ways that our decisions can have consequences for others.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The low marginal cost to distributing and selling content online adds to the power law, which arises from the feedback introduced by correlated decisions across a population (Easley, J. Kleinberg, et al. 2010).”
From "Rich-Get-Richer"? Platform Attention and Earnings Inequality using Patreon Earnings Data (2025) · cites this paper as Easley, J. Kleinberg, et al. 2010
In the citing paper
“In the copying model (Mitzenmacher 2004; Easley, J. Kleinberg, et al. 2010; Chung et al. 2003), α ≈ 2 corresponds to near-pure preferential attachment ( γ → 0) in a one-edge-per-arrival regime, where each impression or recommendation resolves into a single allocation of attention - predominantly steered toward already-popular creators, with a small net exploratory component.”
From "Rich-Get-Richer"? Platform Attention and Earnings Inequality using Patreon Earnings Data (2025) · cites this paper as Easley, J. Kleinberg, et al. 2010
Citations
Cited by
Sorted by citations
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Park, J. S., O'Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023) · Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3586183.3606763 · Cited by 2,187
Can Large Language Models Transform Computational Social Science?
Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2024) · Computational Linguistics · doi:10.1162/coli_a_00502 · Cited by 585
Gotcha! Network-based fraud detection for social security fraud
Van Vlasselaer, V., Eliassi-Rad, T., Akoglu, L., Snoeck, M., & Baesens, B. (2017) · Management Science · doi:10.1287/mnsc.2016.2489 · Cited by 164
Weitzman, M. L. (2015) · The Scandinavian Journal of Economics · doi:10.1111/sjoe.12120 · Cited by 159
"Rich-Get-Richer"? Platform Attention and Earnings Inequality using Patreon Earnings Data
Strauss, I., Yang, J., & Mazzucato, M. (2025) · arXiv
Metadata
- Authors
- Easley, D., & Kleinberg, J.
- Publisher
- Cambridge University Press
- Published
- 2010-07-19
- Cited by
- 2,590
- References
- 0
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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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