SimiNet: a Novel Method for Quantifying Brain Network Similarity

TL;DR

SimiNet is a new algorithm that measures brain network similarity by considering node, edge, and spatial features, outperforming existing methods in detecting subtle spatial variations.

Contribution

The paper introduces SimiNet, a novel graph similarity algorithm that incorporates spatial node information, specifically designed for brain networks, and demonstrates superior performance over existing methods.

Findings

SimiNet accurately detects weak spatial variations in simulated graphs.

SimiNet outperforms eight state-of-the-art methods in similarity detection.

Applied to brain data, SimiNet successfully identified spatial differences during visual tasks.

Abstract

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.