Networks, Crowds, and Markets: Index

By David A. Easley, Jon M. Kleinberg. 2010

718 citations recorded by the source corpus.

Subfields: Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/6dae63e0ab88fd8bdca5c4e63f8e6d978a153c53

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In Statistical and Nonlinear Physics

Citations in the corpus

Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks (2014, neutral): “The structural topology of cortical networks can be represented and formally studied using the graph model [1–3], whereby the brain is spatially parcellated into a set of grey matter nodes interconnected by a set of white matter edges [4,5].”
A Voting Architecture for the Governance of Free‐Driver Externalities, with Application to Geoengineering (2015, neutral): “Extending the argument for a well-known result from the median voter θ = 1/2 literature (e.g., Easley and Kleinberg, 2010, Section 23.6) to the case θ = 1/2 can be used to prove that a θ-voting rule applied to all pairs of alternatives produces a group voting-preference relation that is complete…”
Echo chambers and viral misinformation: Modeling fake news as complex contagion (2018, background): “For example, Easley and Kleinberg [46] show using mathematical reasoning that given that cascades start outside of the cluster, that they are seen as successful only if every node in the network is activated, and Echo chambers and viral misinformation”
On the consensus and bipartite consensus in high-order multi-agent dynamical systems with antagonistic interactions (2014, background): “On the contrary, in contexts like markets or social networks [8], for instance, agents may also display non-cooperative or antagonistic interactions with some of their neighboring agents.”
Differentially private data analysis of social networks via restricted sensitivity (2012, background): “In G1 the answer may be 0 even if there are two doctors that everyone else is friends with (e.g, the doctors are not friends with each other), but the answer jumps to n-2 in a neighboring graph G2 (e.g, if we simply connect the doctors to each other).”
DeepCas: An End-to-end Predictor of Information Cascades (2016, background): “In a networked environment, people tend to be influenced by their neighbors’ behaviors and decisions [13].”
Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment (2014, background): “Network embeddedness, or the number of friends that two individuals in a relationship share in common (Easley and Kleinberg 2010: 55), has long been theorized to affect the level of trust, altruism, cooperation and communication in relationships.”