论文标题
通过对简单复合物的热扩散进行图分类
Graph Classification via Heat Diffusion on Simplicial Complexes
论文作者
论文摘要
在本文中，我们研究了顶点标记图中的图形分类问题。我们的主要目的是分类图形，以比较其高阶结构，这要归功于其简短的热量扩散。我们首先将顶点标记的图表示为简单加权的超级图。然后，我们将扩散特性函数定义在简单上，以编码高阶网络拓扑，并最终通过将功能值与机器学习算法相结合来实现我们的目标。我们在现实世界中生物信息学网络上的实验表明，在图形分类中使用扩散fr {é} chet函数有望比基线方法更有效。据我们所知，本文是文献中使用热挖掘问题中的高维简单上的热扩散的文献。我们认为，我们的方法可以扩展到不同的图挖掘域，而不仅仅是图形分类问题。
In this paper, we study the graph classification problem in vertex-labeled graphs. Our main goal is to classify the graphs comparing their higher-order structures thanks to heat diffusion on their simplices. We first represent vertex-labeled graphs as simplex-weighted super-graphs. We then define the diffusion Frechet function over their simplices to encode the higher-order network topology and finally reach our goal by combining the function values with machine learning algorithms. Our experiments on real-world bioinformatics networks show that using diffusion Fr{é}chet function on simplices is promising in graph classification and more effective than the baseline methods. To the best of our knowledge, this paper is the first paper in the literature using heat diffusion on higher-dimensional simplices in a graph mining problem. We believe that our method can be extended to different graph mining domains, not only the graph classification problem.
