论文标题
无监督的关节$ k $ -Node图表具有基于组成能量的模型
Unsupervised Joint $k$-node Graph Representations with Compositional Energy-Based Models
论文作者
论文摘要
通过预测图表中观察到的边缘，将学习归纳无监督图表示的现有图形神经网络（GNN）方法集中在学习节点和边缘表示上。尽管这种方法已经显示出下游节点分类任务的进步，但它们在共同表示较大的$ k $ node集的共同代表$ k {>} 2 $方面却无效。我们提出了MHM-GNN，这是一种吸引性的无监督图表示方法，将关节$ K $ -NODE表示与基于能量的模型（HyperGraph Markov Networks）和GNN结合在一起。为了解决这种组合造成的损失的棘手性，我们使用有限样本的无偏见的马尔可夫链蒙特卡洛估计器将优化赋予上限上限。我们的实验表明，MHM-GNN的无监督的MHM-GNN表示产生的无监督表示比文献中的现有方法更好。
Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in the graph. Although such approaches have shown advances in downstream node classification tasks, they are ineffective in jointly representing larger $k$-node sets, $k{>}2$. We propose MHM-GNN, an inductive unsupervised graph representation approach that combines joint $k$-node representations with energy-based models (hypergraph Markov networks) and GNNs. To address the intractability of the loss that arises from this combination, we endow our optimization with a loss upper bound using a finite-sample unbiased Markov Chain Monte Carlo estimator. Our experiments show that the unsupervised MHM-GNN representations of MHM-GNN produce better unsupervised representations than existing approaches from the literature.
