论文标题
弹簧反弹：引入网络表示的应变高度张力弹簧嵌入算法
The spring bounces back: Introducing the Strain Elevation Tension Spring embedding algorithm for network representation
论文作者
论文摘要
本文介绍了应变高程张力弹簧嵌入（SETSE）算法，该算法是一种图形嵌入方法，该方法使用物理模型在非方向的属性网络中创建节点和边缘嵌入。使用低维表示，SetSe能够区分使用标准网络指标（例如节点数量，边数和各个分类性）的图表。嵌入生成的位置的节点使得在嵌入过程中隐藏的子类是线性分离的，这是由于它们连接到网络其余的方式。 SetSe在图形分化和子类识别上都优于其他五个常见的图形嵌入方法。该技术应用于社交网络数据，显示了其优于分类性的优势以及Setse量化网络结构和预测节点类型的能力。该算法的收敛复杂性约为$ \ MATHCAL {O}（n^2）$，并且迭代速度是线性的（$ \ Mathcal {O}（n）$），就像内存的复杂性一样。总体而言，SETSE是一个快速，灵活的框架，用于各种网络和图形任务，为复杂系统提供了分析洞察力和简单的可视化。
This paper introduces the Strain Elevation Tension Spring embedding (SETSe) algorithm, a graph embedding method that uses a physics model to create node and edge embeddings in undirected attribute networks. Using a low-dimensional representation, SETSe is able to differentiate between graphs that are designed to appear identical using standard network metrics such as number of nodes, number of edges and assortativity. The embeddings generated position the nodes such that sub-classes, hidden during the embedding process, are linearly separable, due to the way they connect to the rest of the network. SETSe outperforms five other common graph embedding methods on both graph differentiation and sub-class identification. The technique is applied to social network data, showing its advantages over assortativity as well as SETSe's ability to quantify network structure and predict node type. The algorithm has a convergence complexity of around $\mathcal{O}(n^2)$, and the iteration speed is linear ($\mathcal{O}(n)$), as is memory complexity. Overall, SETSe is a fast, flexible framework for a variety of network and graph tasks, providing analytical insight and simple visualisation for complex systems.
