Machine Learning and Knowledge Discovery in Databases

Volume 5781 of the series Lecture Notes in Computer Science pp 13-28

RTG: A Recursive Realistic Graph Generator Using Random Typing

  • Leman AkogluAffiliated withSchool of Computer Science, Carnegie Mellon University
  • , Christos FaloutsosAffiliated withSchool of Computer Science, Carnegie Mellon University

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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.