Embedding Learning on Multiplex Networks for Link Prediction
University of Rostock（罗斯托克大学）；Leibniz-Institute for Food Systems Biology at Technical University Munich（慕尼黑工业大学莱布尼茨食品系统生物学研究所）；Stellenbosch Institute of Advanced Study（斯泰伦博斯高等研究院）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）
Over the past years, embedding learning on networks has shown tremendous results in link prediction tasks for complex systems, with a wide range of real-life applications. Learning a representation for each node in a knowledge graph allows us to capture topological and semantic information, which can be processed in downstream analyses later. In the link prediction task, high-dimensional network information is encoded into low-dimensional vectors, which are then fed to a predictor to infer new connections between nodes in the network. As the network complexity (that is, the numbers of connections and types of interactions) grows, embedding learning turns out increasingly challenging. This review covers published models on embedding learning on multiplex networks for link prediction. First, we propose refined taxonomies to classify and compare models, depending on the type of embeddings and embedding techniques. Second, we review and address the problem of reproducible and fair evaluation of embedding learning on multiplex networks for the link prediction task. Finally, we tackle evaluation on directed multiplex networks by proposing a novel and fair testing procedure. This review constitutes a crucial step towards the development of more performant and tractable embedding learning approaches for multiplex networks and their fair evaluation for the link prediction task. We also suggest guidelines on the evaluation of models, and provide an informed perspective on the challenges and tools currently available to address downstream analyses applied to multiplex networks.
Facebook AI（脸书人工智能）；Santa Clara University（圣克拉拉大学）；Google Research（谷歌研究院）；Meituan NLP Center（美团自然语言处理中心）；Sun Yat-sen University（中山大学）；Rochester Institute of Technology（罗切斯特理工学院）
Link prediction in multiplex networks via triadic closure
ISI Foundation（ISI基金会）；Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza（萨拉戈萨大学生物计算与复杂系统物理研究所（BIFI））；University of Zaragoza（萨拉戈萨大学）
RCoCo: Contrastive Collective Link Prediction across Multiplex Network in Riemannian Space
North China Electric Power University（华北电力大学）；State Grid Handan Electric Power Supply Company（国网邯郸供电公司）；Capital Normal University（首都师范大学）；Beijing Institute of Electronic Science and Technology（北京电子科技学院）；Anhui University of Science and Technology（安徽理工大学）；Beihang University（北京航空航天大学）
DDeMON: Ontology-based function prediction by Deep Learning from Dynamic Multiplex Networks
Jožef Stefan Institute（约热夫·斯特凡研究所）；National Institute of Biology（国家生物学研究所）
生物系统可以在多个信息水平上进行研究，包括基因、蛋白质、RNA和不同的相互作用网络水平。这项工作的目标是探索如何将系统级信息与基因表达的时间动态相融合，并结合深度神经网络的非线性逼近能力来预测非模式生物马铃薯\emph{Solanum tuberosum}中的新基因功能。我们提出了DDeMON（Dynamic Deep learning from temporal Multiplex Ontology-annotated Networks），这是一种使用时间相关的多尺度生物信息进行功能注释的可扩展系统级推理的方法。所提出的方法，这是能够考虑感兴趣的基因之间的潜在的数十亿的链接，被施加到实验基因表达数据和背景知识网络，可靠地分类与未知功能的基因到五个不同的功能本体类别，链接到实验数据集。预测的新功能的基因进行了验证，使用广泛的蛋白质结构域搜索方法。
