GNEA: A Graph Neural Network with ELM Aggregator for Brain Network Classification
GNEA: A Graph Neural Network with ELM Aggregator for Brain Network Classification
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GNEA：带有 ELM 聚合器的图神经网络，用于脑网络分类
DOI：
10.1155/2020/8813738
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发表时间：
2020-10-29
期刊：
影响因子：
2.3
通讯作者：
Liu, Hao
中科院分区：
文献类型：
--
作者：
Bi, Xin;Liu, Zhixun;Liu, Hao
大脑网络为功能性脑部疾病（例如阿尔茨海默病（AD））的诊断提供了重要的见解。许多机器学习方法已被应用于从欧几里得空间中的大脑图像或网络中学习。然而，学习非欧空间中复杂的网络结构和大脑区域的连接仍然具有挑战性。为了解决这个问题，在本文中，我们从图学习的角度对脑网络分类进行研究。我们提出了一种基于极限学习机（ELM）的聚合器，无需迭代调整即可提高图卷积的聚合能力和效率。然后，我们为图分类任务设计了一个名为 GNEA（带有 ELM Aggregator 的图神经网络）的图神经网络。使用真实世界的 AD 检测数据集进行了大量的实验，以评估和比较 GNEA 和最先进的图学习方法的图学习性能。结果表明，GNEA 在脑网络分类应用中以最佳的图表示能力实现了优异的学习性能。
Brain networks provide essential insights into the diagnosis of functional brain disorders, such as Alzheimer's disease (AD). Many machine learning methods have been applied to learn from brain images or networks in Euclidean space. However, it is still challenging to learn complex network structures and the connectivity of brain regions in non-Euclidean space. To address this problem, in this paper, we exploit the study of brain network classification from the perspective of graph learning. We propose an aggregator based on extreme learning machine (ELM) that boosts the aggregation ability and efficiency of graph convolution without iterative tuning. Then, we design a graph neural network named GNEA (Graph Neural Network with ELM Aggregator) for the graph classification task. Extensive experiments are conducted using a real-world AD detection dataset to evaluate and compare the graph learning performances of GNEA and state-of-the-art graph learning methods. The results indicate that GNEA achieves excellent learning performance with the best graph representation ability in brain network classification applications.