NetSimile：一种可扩展的与网络规模无关的网络相似性方法
NetSimile: A Scalable Approach to Size-Independent Network Similarity
摘要：
给定一组k个网络，可能具有不同的大小，并且节点或边没有重叠，我们如何在不解决节点对应问题的情况下快速评估它们之间的相似性？类似地，我们如何从每个图中提取少量描述性的数字特征，有效地作为图的“签名”？有这样的功能将使丰富的图形挖掘任务，包括聚类，离群值检测，可视化等，我们提出NetSimile -一种新颖的，有效的，可扩展的方法来解决上述问题。NetSimile具有以下可取的属性：（a）它给出了大小不变的相似性分数。(b)它是可扩展的，对于“签名”向量提取的边缘数量是线性的。(c)它不需要解决节点对应问题。我们提出了大量的实验，从不同的领域的合成和真实的图形，并显示NetSimile的优势，基线竞争对手。我们还展示了NetSimile如何实现几个挖掘任务，如聚类，可视化，不连续性检测，网络迁移学习和跨网络的重新识别。
英文摘要：
Given a set of k networks, possibly with different sizes and no overlaps in nodes or edges, how can we quickly assess similarity between them, without solving the node-correspondence problem? Analogously, how can we extract a small number of descriptive, numerical features from each graph that effectively serve as the graph's "signature"? Having such features will enable a wealth of graph mining tasks, including clustering, outlier detection, visualization, etc. We propose NetSimile -- a novel, effective, and scalable method for solving the aforementioned problem. NetSimile has the following desirable properties: (a) It gives similarity scores that are size-invariant. (b) It is scalable, being linear on the number of edges for "signature" vector extraction. (c) It does not need to solve the node-correspondence problem. We present extensive experiments on numerous synthetic and real graphs from disparate domains, and show NetSimile's superiority over baseline competitors. We also show how NetSimile enables several mining tasks such as clustering, visualization, discontinuity detection, network transfer learning, and re-identification across networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
