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Graph evolution: Densification and shrinking diameters (2007)
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| Venue: | ACM TKDD |
| Citations: | 257 - 16 self |
BibTeX
@ARTICLE{Leskovec07graphevolution:,
author = {Jure Leskovec and Jon Kleinberg and Christos Faloutsos},
title = {Graph evolution: Densification and shrinking diameters},
journal = {ACM TKDD},
year = {2007},
pages = {2}
}
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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. We also notice that the “forest fire” model exhibits a sharp transition between sparse graphs and graphs that are densifying. Graphs with decreasing distance between the nodes are generated around this transition point. Last, we analyze the connection between the temporal evolution of the degree distribution and densification of a graph. We find that the two are fundamentally related. We also observe that real networks exhibit this type of r
Keyphrases
graph evolution static graph network evolution average distance temporal evolution many study long period surprising phenomenon qualitative level intuitive justification out-degree distribution full range graph generation model real graph small-world phenomenon degree distribution new graph generator conventional wisdom small number sharp transition prior work present study forest fire transition point real graph evolve single snapshot sparse graph normal growth pattern real network wide range information network large network heavy tail fire model distance parameter
