The existing methods to calculate the Eigenvector Centrality(EC) tend to not be robust enough for determination of EC in low time complexity or not well-scalable for large networks, hence rendering them practically unreliable/ computationally expensive. So, it is of the essence to develop a method that is scalable in low computational time. Hence, we propose a deep learning model for the identification of nodes with high Eigenvector Centrality. There have been a few previous works in identifying the high ranked nodes with supervised learning methods, but in real-world cases, the graphs are not labelled and hence deployment of supervised learning methods becomes a hazard and its usage becomes impractical. So, we devise CUL(Centrality with Unsupervised Learning) method to learn the relative EC scores in a network in an unsupervised manner. To achieve this, we develop an Encoder-Decoder based framework that maps the nodes to their respective estimated EC scores. Extensive experiments were conducted on different synthetic and real-world networks. We compared CUL against a baseline supervised method for EC estimation similar to some of the past works. It was observed that even with training on a minuscule number of training datasets, CUL delivers a relatively better accuracy score when identifying the higher ranked nodes than its supervised counterpart. We also show that CUL is much faster and has a smaller runtime than the conventional baseline method for EC computation. The code is available at https://github.com/codexhammer/CUL.
翻译:计算Eigenvector Centrality(EC)的现有方法往往不够稳健,不足以在低时间复杂性下确定EC, 或无法对大型网络进行适当缩放, 从而使其实际上不可靠/ 计算成本昂贵。 因此, 开发一种在低计算时间上可缩放的方法至关重要。 因此, 我们提出一个深层次的学习模式, 用于识别高 Eigenvector Central(EC) 的节点。 以前曾进行过一些工作, 确定有受监督的常规计算方法的高分节点, 但在现实世界案例中, 图表没有贴标签, 因而使用受监督的学习方法成为一种危险, 因而其使用不切实际。 因此, 我们设计了 CUL( 不受监督的学习) 方法, 在一个网络中以不受监督的方式学习相对的EC的得分。 为此, 我们开发了一个基于节点和各自估计的ECCUL 的快速度框架。 在不同的合成和现实世界网络上进行了广泛的实验。 我们比较了CUL 与EC 的基线估计方法比某些EC 的基线方法要更接近于 CUCUCL 。 在以往的排序中, 也观察到了比前的排序数据要更精确 。