This paper introduces the strain elevation tension spring embedding (SETSe) algorithm. SETSe is a novel graph embedding method that uses a physical model to project feature-rich networks onto a manifold with semi-Euclidean properties. Due to its method, SETSe avoids the tractability issues faced by traditional force-directed graphs, having an iteration time and memory complexity that is linear to the number of edges in the network. SETSe is unusual as an embedding method as it does not reduce dimensionality or explicitly attempt to place similar nodes close together in the embedded space. Despite this, the algorithm outperforms five common graph embedding algorithms, on graph classification and node classification tasks, in low-dimensional space. The algorithm is also used to embed 100 social networks ranging in size from 700 to over 40,000 nodes and up to 1.5 million edges. The social network embeddings show that SETSe provides a more expressive alternative to the popular assortativity metric and that even on large complex networks, SETSe’s classification ability outperforms the naive baseline and the other embedding methods in low-dimensional representation. SETSe is a fast and flexible unsupervised embedding algorithm that integrates node attributes and graph topology to produce interpretable results.

介绍了应变高程拉伸弹簧嵌入算法。SETSe是一种新颖的图形嵌入方法，该方法使用物理模型将功能丰富的网络投影到具有半欧几里得性质的流形上。由于其方法，SETSe避免了传统的力导向图所面临的易处理性问题，其迭代时间和内存复杂度与网络中的边数成线性关系。SETSe作为一种嵌入方法是不寻常的，因为它不会降低维数或显式地尝试将相似的节点靠近在一起放置在嵌入式空间中。尽管如此，在低维空间中，在图分类和节点分类任务上，该算法优于五种常见的图嵌入算法。该算法还用于嵌入100个社交网络，大小从700到40多个不等，000个节点和多达150万个边。社交网络嵌入显示，SETSe提供了比流行的分类指标更富表现力的替代方法，并且即使在大型复杂网络上，SETSe的分类能力也比朴素的基线和其他嵌入方法的低维表示要好。SETSe是一种快速且灵活的无监督嵌入算法，该算法将节点属性和图拓扑集成在一起以产生可解释的结果。