In this paper, we present a general framework to estimate the network entropy that is represented by means of an undirected graph and subsequently employ this framework for graph classification tasks. The proposed framework is based on local information functionals which are defined using induced connected subgraphs of different sizes. These induced subgraphs are termed graphlets. Specifically, we extract the set of all graphlets of a specific sizes and compute the graph entropy using our proposed framework. To classify the network into different categories, we construct a feature vector whose components are obtained by computing entropies of different graphlet sizes. We apply the proposed framework to two different tasks, namely view-based object recognition and biomedical datasets with binary outcomes classification. Finally, we report and compare the classification accuracies of the proposed method and compare against some of the state-of-the-art methods.

在本文中，我们提出了一个通用框架来估计由无向图表示的网络熵，然后将该框架用于图分类任务。所提出的框架基于使用不同大小的诱导连接子图定义的本地信息功能。这些诱导子图称为图小图。具体来说，我们提取特定大小的所有图集的集合，并使用我们提出的框架来计算图的熵。为了将网络分类为不同的类别，我们构造了一个特征向量，该特征向量的组成部分是通过计算不同小图大小的熵来获得的。我们将提出的框架应用于两个不同的任务，即基于视图的对象识别和具有二进制结果分类的生物医学数据集。最后，