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RTG: A Recursive Realistic Graph Generator using Random Typing

RTG: A Recursive Realistic Graph Generator using Random Typing

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Bled 2009. We propose a new, recursive model to generate realistic graphs, evolving over time. Our model has the following properties: it is (a) flexible, capable of generating the cross product of weighted/unweighted, directed/undirected, uni/bipartite graphs; (b) realistic, giving graphs that obey eleven static and dynamic laws that real graphs follow (we formally prove that for several of the (power) laws and we estimate their exponents as a function of the model parameters); (c) parsimonious, requiring only four parameters. (d) fast, being linear on the number of edges; (e) simple, intuitively leading to the generation of macroscopic patterns. We empirically show that our model mimics two real-world graphs very well: Blognet (unipartite, undirected, unweighted) with 27K nodes and 125K edges; and Committee-to-Candidate campaign donations (bipartite, directed, weighted) with 23K nodes and 880K edges. We also show how to handle time so that edge/weight additions are bursty and self-similar.

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