论文标题
图形自动编码器中的新边界：联合社区检测和链接预测
New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction
论文作者
论文摘要
Graph AutoCododers（GAE）和变分图自动编码器（VGAE）作为链接预测（LP）的强大方法出现。他们的表现在社区检测（CD）上的印象不那么令人印象深刻，在这些替代方案（例如Louvain方法）中，它们的表现通常优于他们的表现。目前尚不清楚在多大程度上可以用GAE和VGAE改善CD，尤其是在没有节点特征的情况下。此外，尚不确定是否可以在多任务设置中同时保留LP上的良好性能。在这篇研讨会的论文中，总结了我们的杂志出版物（Salha-Galvan等，2022）的结果，我们表明，以高准确性共同解决这两个任务是可能的。为此，我们介绍了一个社区保留的消息传递方案，通过在计算嵌入空间时考虑初始图形和基于卢文的先验社区来掺杂我们的GAE和VGAE编码器。受基于模块化聚类的启发，我们进一步提出了专门为关节LP和CD设计的新型培训和优化策略。我们在各种现实世界图上证明了方法的经验有效性，称为模块化感知的GAE和VGAE。
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction (LP). Their performances are less impressive on community detection (CD), where they are often outperformed by simpler alternatives such as the Louvain method. It is still unclear to what extent one can improve CD with GAE and VGAE, especially in the absence of node features. It is moreover uncertain whether one could do so while simultaneously preserving good performances on LP in a multi-task setting. In this workshop paper, summarizing results from our journal publication (Salha-Galvan et al. 2022), we show that jointly addressing these two tasks with high accuracy is possible. For this purpose, we introduce a community-preserving message passing scheme, doping our GAE and VGAE encoders by considering both the initial graph and Louvain-based prior communities when computing embedding spaces. Inspired by modularity-based clustering, we further propose novel training and optimization strategies specifically designed for joint LP and CD. We demonstrate the empirical effectiveness of our approach, referred to as Modularity-Aware GAE and VGAE, on various real-world graphs.
