Quantifying the similarity between two networks is critical in many applications. A number of algorithms have been proposed to compute graph similarity, mainly based on the properties of nodes and edges. Interestingly, most of these algorithms ignore the physical location of the nodes, which is a key factor in the context of brain networks involving spatially defined functional areas. In this paper, we present a novel algorithm called “SimiNet” for measuring similarity between two graphs whose nodes are defined a priori within a 3D coordinate system. SimiNet provides a quantified index (ranging from 0 to 1) that accounts for node, edge and spatiality features. Complex graphs were simulated to evaluate the performance of SimiNet that is compared with eight state-of-art methods. Results show that SimiNet is able to detect weak spatial variations in compared graphs in addition to computing similarity using both nodes and edges. SimiNet was also applied to real brain networks obtained during a visual recognition task. The algorithm shows high performance to detect spatial variation of brain networks obtained during a naming task of two categories of visual stimuli: animals and tools. A perspective to this work is a better understanding of object categorization in the human brain.

中文翻译：

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SimiNet：一种定量脑网络相似性的新方法

在许多应用中，量化两个网络之间的相似性至关重要。主要基于节点和边的属性，提出了许多算法来计算图相似度。有趣的是，这些算法大多数都忽略了节点的物理位置，这是在涉及空间定义功能区域的大脑网络中的一个关键因素。在本文中，我们提出了一种称为“ SimiNet”的新颖算法，用于测量两个图之间的相似性，这些图的节点是在3D坐标系中先验定义的。SimiNet提供了一个量化的索引（范围从0到1），该索引说明了节点，边缘和空间特征。模拟了复杂图形以评估SimiNet的性能，并与八种最新方法进行了比较。结果表明，除了使用节点和边计算相似度之外，SimiNet还能够检测比较图中的微弱空间变化。SimiNet还应用于在视觉识别任务中获得的真实大脑网络。该算法在检测两类视觉刺激（动物和工具）的命名任务中获得的大脑网络的空间变化方面具有很高的检测性能。这项工作的一个观点是更好地了解人脑中的对象分类。