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NetLSD：听到图形的形状NetLSD: Hearing the Shape of a Graph |
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| 课程网址: | http://videolectures.net/kdd2018_tsitsulin_netLSD_shape_graph/ |
| 主讲教师: | Anton Tsitsulin |
| 开课单位: | 波茨坦大学哈索·普拉特纳研究所 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Comparison among graphs is ubiquitous in graph analytics. However, it is a hard task in terms of the expressiveness of the employed similarity measure and the efficiency of its computation. Ideally, graph comparison should be invariant to the order of nodes and the sizes of compared graphs, adaptive to the scale of graph patterns, and scalable. Unfortunately, these properties have not been addressed together. Graph comparisons still rely on direct approaches, graph kernels, or representation-based methods, which are all inefficient and impractical for large graph collections. In this paper, we propose the Network Laplacian Spectral Descriptor (NetLSD): the first, to our knowledge, permutation- and size-invariant, scale-adaptive, and efficiently computable graph representation method that allows for straightforward comparisons of large graphs. NetLSD extracts a compact signature that inherits the formal properties of the Laplacian spectrum, specifically its heat or wave kernel; thus, it hears the shape of a graph. Our evaluation on a variety of real-world graphs demonstrates that it outperforms previous works in both expressiveness and efficiency |
| 关 键 词: | NetLSD; 听到图形的形状; 大型图集合; 拉普拉斯谱; 真实世界图形 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-23：cyh |
| 最后编审: | 2023-03-23：cyh |
| 阅读次数: | 47 |