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RTG: A Recursive Realistic Graph Generator using Random Typing[RTG:一种生成实景图的随机类型递归模型}RTG: A Recursive Realistic Graph Generator using Random Typing[RTG:一种生成实景图的随机类型递归模型} |
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| 课程网址: | http://videolectures.net/ecmlpkdd09_akoglu_rtg/ |
| 主讲教师: | Leman Akoglu |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | 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. |
| 关 键 词: | 递归模型; （幂）定律; 模型参数 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-22：chenxin |
| 阅读次数: | 89 |