论文标题
图形匹配超出完美重叠的erdős--rényi随机图
Graph matching beyond perfectly-overlapping Erdős--Rényi random graphs
论文作者
论文摘要
就算法和理论而言，图形匹配是一个富有成果的领域。在本文中，我们利用了学位信息，该信息以前仅在无噪声图中使用，并且完全重叠的erdős--rényi随机图匹配。我们关注的是部分重叠图和随机块模型的图形匹配，这些模型在解决现实生活问题方面更有用。我们提出了边缘利用度谱图匹配方法和两个精制的变化。我们在一系列具有挑战性的场景中对我们提出的方法的性能进行了彻底的分析，包括斑马鱼神经元活动数据集和共授权数据集。事实证明，我们的方法比最先进的方法优越。
Graph matching is a fruitful area in terms of both algorithms and theories. In this paper, we exploit the degree information, which was previously used only in noiseless graphs and perfectly-overlapping Erdős--Rényi random graphs matching. We are concerned with graph matching of partially-overlapping graphs and stochastic block models, which are more useful in tackling real-life problems. We propose the edge exploited degree profile graph matching method and two refined varations. We conduct a thorough analysis of our proposed methods' performances in a range of challenging scenarios, including a zebrafish neuron activity data set and a coauthorship data set. Our methods are proved to be numerically superior than the state-of-the-art methods.
