Investigating brain connectivity networks for neurological disorder identification has attracted great interest in recent years, most of which focus on the graph representation alone. However, in addition to brain networks derived from the neuroimaging data, hundreds of clinical, immunologic, serologic, and cognitive measures may also be documented for each subject. These measures compose multiple side views encoding a tremendous amount of supplemental information for diagnostic purposes, yet are often ignored. In this paper, we study the problem of subgraph selection from brain networks with side information guidance and propose a novel solution to find an optimal set of subgraph patterns for graph classification by exploring a plurality of side views. We derive a feature evaluation criterion, named gSide, to estimate the usefulness of subgraph patterns based upon side views. Then we develop a branch-and-bound algorithm, called gMSV, to efficiently search for optimal subgraph patterns by integrating the subgraph mining process and the procedure of discriminative feature selection. Empirical studies on graph classification tasks for neurological disorders using brain networks demonstrate that subgraph patterns selected by the multi-side-view-guided subgraph selection approach can effectively boost graph classification performances and are relevant to disease diagnosis.

中文翻译：

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通过辅助信息识别大脑网络中HIV诱导的子图模式。

近年来，研究用于神经系统疾病识别的大脑连接网络引起了极大的兴趣，其中大多数只关注图形表示。但是，除了从神经影像数据中得出的大脑网络外，还可以为每个受试者记录数百种临床，免疫，血清学和认知指标。这些措施组成了多个侧视图，这些侧视图编码了用于诊断目的的大量补充信息，但常常被忽略。在本文中，我们研究了在带有辅助信息指导的情况下从大脑网络中选择子图的问题，并提出了一种新颖的解决方案，通过探索多个侧视图来找到用于图分类的最优子图模式集。我们推导了一个名为gSide的特征评估标准，根据侧视图估计子图模式的有用性。然后，我们开发了一种称为gMSV的分支定界算法，通过整合子图挖掘过程和判别特征选择过程来有效地搜索最佳子图模式。使用脑网络对神经系统疾病进行图分类任务的实证研究表明，通过多视图引导子图选择方法选择的子图模式可以有效地提高图分类的性能，并且与疾病诊断有关。