标题： NetSimile：一种与尺寸无关的网络相似性的可扩展方法 显示英文标题

标题： NetSimile: A Scalable Approach to Size-Independent Network Similarity

摘要： 给定一组k个网络，这些网络可能具有不同的大小，并且节点或边之间没有重叠，我们如何快速评估它们之间的相似性，而无需解决节点对应问题？ 类似地，如何从每个图中提取少量描述性的数值特征，这些特征有效地作为图的“标识符”？ 拥有这样的特征将能够实现大量图挖掘任务，包括聚类、异常检测、可视化等。 我们提出了NetSimile——一种新颖、有效且可扩展的方法，用于解决上述问题。 NetSimile具有以下优点： (a) 它给出的相似性分数与大小无关。 (b) 它是可扩展的，对于“标识符”向量提取，其复杂度与边的数量成线性关系。 (c) 它不需要解决节点对应问题。 我们在来自不同领域的众多合成和真实图上进行了广泛的实验，并展示了NetSimile优于基线竞争方法。 我们还展示了NetSimile如何使多种挖掘任务成为可能，例如聚类、可视化、不连续性检测、网络迁移学习以及跨网络的重新识别。 显示英文摘要

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.