Group-Wise Hub Identification by Learning Common Graph Embeddings on Grassmannian Manifold.
Group-Wise Hub Identification by Learning Common Graph Embeddings on Grassmannian Manifold.
复制标题
通过学习格拉斯曼尼亚歧管上的常见图嵌入通过学习群中心的识别。
DOI：
10.1109/tpami.2021.3081744
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发表时间：
2022-11
影响因子：
23.6
通讯作者：
中科院分区：
文献类型：
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作者：
人类大脑是一个复杂但经济组织的系统，其中一小部分关键枢纽区域支持大部分大脑功能。在网络群体中识别共同的枢纽节点通常被简化为在单个大脑网络中识别的枢纽节点集合上的投票程序，这忽略了内在的数据几何形状，并且部分缺乏神经科学中的可重复发现。因此，我们提出了一个有史以来第一次组明智的枢纽识别方法，以确定枢纽节点是共同的人口的个人大脑网络。具体来说，我们的方法的骨干是学习共同的图嵌入，可以代表大多数的本地拓扑配置文件。通过要求图嵌入向量之间的正交性，每个图嵌入作为数据元素驻留在格拉斯曼流形上。我们提出了一种新的格拉斯曼流形优化方案，使我们能够找到共同的图嵌入，这不仅确定了最可靠的枢纽节点在每个网络，但也产生一个基于人口的共同枢纽节点地图。在合成和真实的网络数据上的准确性和可复制性的结果表明，所提出的流形学习方法优于在此评估中采用的所有枢纽识别方法。
Human brain is a complex yet economically organized system, where a small portion of critical hub regions support the majority of brain functions. The identification of common hub nodes in a population of networks is often simplified as a voting procedure on the set of identified hub nodes across individual brain networks, which ignores the intrinsic data geometry and partially lacks the reproducible findings in neuroscience. Hence, we propose a first-ever group-wise hub identification method to identify hub nodes that are common across a population of individual brain networks. Specifically, the backbone of our method is to learn common graph embedding that can represent the majority of local topological profiles. By requiring orthogonality among the graph embedding vectors, each graph embedding as a data element is residing on the Grassmannian manifold. We present a novel Grassmannian manifold optimization scheme that allows us to find the common graph embeddings, which not only identify the most reliable hub nodes in each network but also yield a population-based common hub node map. Results of the accuracy and replicability on both synthetic and real network data show that the proposed manifold learning approach outperforms all hub identification methods employed in this evaluation.