Unsupervised constrained community detection via self-expressive graph neural network
Unsupervised constrained community detection via self-expressive graph neural network
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发表时间：
2020-11
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通讯作者：
S. Bandyopadhyay;Vishal Peter
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作者：
S. Bandyopadhyay;Vishal Peter
图神经网络（GNN）能够在多个图下游任务（例如节点分类和链接预测）上取得良好的性能。设计可直接用于图上社区检测的 GNN 的工作相对较少。传统上，GNN 接受半监督或自监督损失函数的训练，然后应用聚类算法来检测社区。然而，这种解耦方法本质上是次优的。设计一个无监督损失函数来训练 GNN 并以集成的方式提取社区是一项基本挑战。为了解决这个问题，我们在文献中首次将自我表达原则与自监督图神经网络框架相结合，进行无监督社区检测。我们的解决方案以端到端的方式进行训练，并在多个公开可用的数据集上实现了最先进的社区检测性能。
Graph neural networks (GNNs) are able to achieve promising performance on multiple graph downstream tasks such as node classification and link prediction. Comparatively lesser work has been done to design GNNs which can operate directly for community detection on graphs. Traditionally, GNNs are trained on a semi-supervised or self-supervised loss function and then clustering algorithms are applied to detect communities. However, such decoupled approaches are inherently sub-optimal. Designing an unsupervised loss function to train a GNN and extract communities in an integrated manner is a fundamental challenge. To tackle this problem, we combine the principle of self-expressiveness with the framework of self-supervised graph neural network for unsupervised community detection for the first time in literature. Our solution is trained in an end-to-end fashion and achieves state-of-the-art community detection performance on multiple publicly available datasets.