利用网络嵌入改善网络对齐
Using Network Embeddings for Improving Network Alignment
摘要：
网络（或图）对齐算法旨在揭示图之间的结构相似性。特别地，局部网络对齐算法（LNA）在两个或更多个网络之间找到相似性的局部区域。这样的算法通常基于用于生长比对的种子节点的集合。几乎所有的LNA算法都使用基于上下文信息的一组顶点作为种子节点（例如，生物网络对齐中的一组生物相关），这可能会导致偏差或数据循环问题。最近，我们证明了在种子节点的选择中使用拓扑信息可以提高比对的质量。我们使用了一些常见的方法，全局对齐算法的基础上捕捉节点之间的拓扑相似性。同时，已经证明，使用网络嵌入方法（或表示学习）可以比其他方法更好地捕获节点之间的结构相似性。因此，我们建议使用网络嵌入来学习节点之间的结构相似性，并使用这种相似性来改进LNA扩展我们以前的算法。我们为LNA定义了一个框架。
英文摘要：
Network (or Graph) Alignment Algorithms aims to reveal structural similarities among graphs. In particular Local Network Alignment Algorithms (LNAs) finds local regions of similarity among two or more networks. Such algorithms are in general based on a set of seed nodes that are used to grow an alignment. Almost all LNAs algorithms use as seed nodes a set of vertices based on context information (e.g. a set of biologically related in biological network alignment) and this may cause a bias or a data-circularity problem. More recently, we demonstrated that the use of topological information in the choice of seed nodes may improve the quality of the alignments. We used some common approaches based on global alignment algorithms for capturing topological similarity among nodes. In parallel, it has been demonstrated that the use of network embedding methods (or representation learning), may capture the structural similarity among nodes better than other methods. Therefore we propose to use network embeddings to learn structural similarity among nodes and to use such similarity to improve LNA extendings our previous algorithms. We define a framework for LNA.
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。
