A Random Graph Model for Power Law Graphs

By William Aiello, Fan Chung Graham, Linyuan Lu. Experimental Mathematics, 2001

462 citations recorded by the source corpus.

Subfields: Geometry and Topology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8756e1a0511ce5187395f00c212f2d05ecd3db35

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In Geometry and Topology

In Statistical and Nonlinear Physics

Citations in the corpus

Distances in random graphs with infinite mean degrees (2004, background): “See [2] for an introduction to the mathematical results of various models for complex networks (also called massive graphs), as well as a detailed account of the results in [1].”
The Network Data Repository with Interactive Graph Analytics and Visualization (2015, methodology): “…save their synthetically generated networks and visualizations created using NR. • Interactive graph generation including model-based (e.g., Chung-Lu (Aiello, Chung, and Lu 2001), BA (Barabási and Albert 1999), pattern-based (e.g., cliques, stars, cycles, etc.), and hybrid generators that…”
Mathematical results on scale‐free random graphs (2005, background): “The degree distribution of the graph of telephone calls seems to follow a power law as well; motivated by this, Aiello, Chung and Lu [1] proposed a model for ‘massive graphs’.”
Statistical analysis of financial networks (2005, unknown): “It turns out that all these graphs coming from diverse applications follow the power-law model (Aiello et al., 2001; Albert and Barabasi, 2002; Barabasi and Albert, 1999; Boginski et al., 2003a; Broder et al., 2000; Faloutsos et al., 1999; Hayes, 2000; Watts, 1999; Watts and Strogatz, 1998), which…”
AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing (2019, background): “rs, the GCN is able to extract very high-level abstracted features for various learning purposes. 2.2 Characteristics of Power-Law Graphs Real-world graphs typically follow the power-law distribution [3,4,12,40], which states that the number of nodes y of a given degree x is proportional to x b for ”
Robustness and Vulnerability of Scale-Free Random Graphs (2004, background): “See [Aiello et al. 01, Cooper and Frieze 02, Bollob´as et al. 01, Buckley and Osthus to appear, Cooper and Frieze 01] and [Cooper and Frieze 02] for some examples, or the survey [Bollob´as and Riordan 02].”
Mining market data: A network approach (2006, background): “2 the degree distribution of the market graph follows the power-law model [2].”
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters (2008, methodology): “(Note that this model is different than the so-called “configuration model” in which the degree distribution is exactly specified and which was studied by Molloy and Reed [124, 125] and also Aiello, Chung, and Lu [7, 8].”
Distribution of subgraphs of random regular graphs (2008, background): “Such graphs arise in a variety of applications such asWeb connectivity [2,5,6,9,24,26,28,29], communication networks [1,3], biological networks [22], collaboration graphs [27], etc.”
Distances in Random Graphs with Finite Mean and Infinite Variance Degrees (2005, background): “In [1], random graphs were considered with a degree sequence that is precisely equal to a power law, meaning that the number of nodes with degree k is precisely proportional to k−τ .”
Impact of human activity patterns on the dynamics of information diffusion. (2007, unknown): “Most people behave close to the average behavior, but a not negligible portion of humans show bursts of activities, like the number of e-mails sent per day [22], the number of telephone calls placed by users [9], the number of weblogs posts by a single user[10], the time spent between receiving and ”