Identifying influential neighbors in social networks and venue affiliations among young MSM: a data science approach to predict HIV infection.
Identifying influential neighbors in social networks and venue affiliations among young MSM: a data science approach to predict HIV infection.
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DOI：
10.1097/qad.0000000000002784
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发表时间：
2021-05-01
期刊：
影响因子：
--
通讯作者：
Tao C
中科院分区：
文献类型：
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作者：
Xiang Y;Fujimoto K;Li F;Wang Q;Del Vecchio N;Schneider J;Zhi D;Tao C
在美国，男男性行为者（YMSM）承担着不成比例的艾滋病毒感染负担，他们感染艾滋病毒的风险可能受到复杂的多层社交网络的影响。这些网络不仅通过与社会/性伙伴的直接接触形成，还通过参加社交场所时遇到的间接匿名接触形成。我们介绍了一种最先进的基于图的深度学习方法的新应用，用于预测HIV感染，可以识别这些多个网络环境中有影响力的邻居。我们使用了2014年至2016年在美国休斯顿和芝加哥收集的16-29岁YMSM的经验网络数据。提出了一种通过识别社交网络中有影响力的邻居来预测HIV感染的计算框架GAT-HIV（Graph Attention Networks for HIV）。这些网络是由社会/性伙伴和共享场地出席，并使用个人层面的变量组成的多重关系形成的。此外，GAT-HIV被扩展为使用多图GAT方法来联合收割机组合多个社交网络。还开发了一个可视化工具来突出显示多个社交网络中每个人的有影响力的网络成员。多图GAT-HIV模型分别获得芝加哥和休斯顿的平均AUC值为0.776和0.824，表现优于经验预测模型（例如，随机森林的AUC：0.758和0.798）。单网络上的GAT-HIV也提供了有前途的预测性能。所提出的方法提供了一个全面的和可解释的框架，以图形为基础的建模，可以告知最容易感染艾滋病毒的人群中有效的艾滋病毒预防干预策略。
Young men who have sex with men (YMSM) bear a disproportionate burden of HIV infection in the United States and their risks of acquiring HIV may be shaped by complex multi-layer social networks. These networks are formed through not only direct contact with social/sex partners but also indirect anonymous contacts encountered when attending social venues. We introduced a new application of a state-of-the-art graph-based deep learning method to predict HIV infection that can identify influential neighbors within these multiple network contexts. We used empirical network data among YMSM aged 16–29 years old collected from Houston and Chicago in the U.S. between 2014 and 2016. A computational framework GAT-HIV (Graph Attention Networks for HIV) was proposed to predict HIV infections by identifying influential neighbors within social networks. These networks were formed by multiple relations comprised of social/sex partners and shared venue attendances, and using individual-level variables. Further, GAT-HIV was extended to combine multiple social networks using multi-graph GAT methods. A visualization tool was also developed to highlight influential network members for each individual within the multiple social networks. The multi-graph GAT-HIV models obtained average AUC values of 0.776 and 0.824 for Chicago and Houston respectively, performing better than empirical predictive models (e.g. AUCs of random forest: 0.758 and 0.798). GAT-HIV on single networks also delivered promising prediction performances. The proposed methods provide a comprehensive and interpretable framework for graph-based modeling that may inform effective HIV prevention intervention strategies among populations most vulnerable to HIV.