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.

本研究提出 NetSimile，一个能够快速评估不同网络相似性和提取描述性数字特征的方法，以在图挖掘中进行聚类、异常检测、可视化、网络转移学习和重新识别等任务中具有卓越的效果，并在多个合成和真实图表现出更好的性能表现。