图 书 馆
大型图形挖掘：电动工具和实践指南Large Graph-Mining: Power Tools and a Practitioner's Guide |
|
| 课程网址: | http://videolectures.net/kdd09_faloutsos_miller_tsourakakis_lgm/ |
| 主讲教师: | Charalampos E. Tsourakakis, Christos Faloutsos, Gary L Miller |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2009-09-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Numerous real-world datasets are in matrix form, thus matrix algebra, linear and multilinear, provides important algorithmic tools for analyzing them. The main type of datasets of interest in this tutorial are graphs. Important datasets modeled as graphs include the Internet, the Web, social networks (e,g Facebook, LinkedIn), computer networks, biological networks and many more. We will discuss how we represent a graph as a matrix (adjacency matrix, Laplacian) and the important properties of those representations. We will then show how these properties are used in several important problems, including node importance via random walks (Pagerank), community detection (METIS, Cheeger inequality), graph isomorphism and graph similarity. Important dimensionality reduction techniques (SVD and random projections) will be discussed in the context of graph mining problems. Furthermore, we provide a survey of the work on the epidemic threshold, node proximity and center-piece subgraphs. State-of-art graph mining tools for analyzing time evolving graphs will also be presented. Throughout the tutorial, patterns in static and time evolving, weighted and unweighted real-world graphs will be presented. The target audience are data mining professionals who wish to know the most important matrix algebra tools, their applications in large graph mining and the theory behind them. Prerequisites: Computer science background (B.Sc or equivalent); familiarity with undergraduate linear algebra. Demos will be presented. |
| 关 键 词: | 矩阵代数; 随机游走; 大图挖掘 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-10：lxf |
| 阅读次数: | 50 |