IEEE Trans Pattern Anal Mach Intell. 2022 Sep;44(9):5103-5113. doi: 10.1109/TPAMI.2021.3080635. Epub 2022 Aug 4.
We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. With the advances of deep learning, current link prediction methods commonly compute features from subgraphs centered at two neighboring nodes and use the features to predict the label of the link between these two nodes. In this formalism, a link prediction problem is converted to a graph classification task. In order to extract fixed-size features for classification, graph pooling layers are necessary in the deep learning model, thereby incurring information loss. To overcome this key limitation, we propose to seek a radically different and novel path by making use of the line graphs in graph theory. In particular, each node in a line graph corresponds to a unique edge in the original graph. Therefore, link prediction problems in the original graph can be equivalently solved as a node classification problem in its corresponding line graph, instead of a graph classification task. Experimental results on fourteen datasets from different applications demonstrate that our proposed method consistently outperforms the state-of-the-art methods, while it has fewer parameters and high training efficiency.
我们研究了图链接预测任务，这是一个具有许多实际应用的经典图分析问题。随着深度学习的发展，当前的链接预测方法通常从两个相邻节点为中心的子图中计算特征，并使用这些特征来预测这两个节点之间的链接的标签。在这种形式主义中，链接预测问题被转化为图分类任务。为了提取固定大小的分类特征，深度学习模型中需要图池化层，从而导致信息丢失。为了克服这个关键的限制，我们建议通过利用图论中的线图来寻求一条截然不同的新路径。特别是，线图中的每个节点对应于原始图中的一个唯一边。因此，原始图中的链接预测问题可以等效地作为其相应线图中的节点分类问题来解决，而不是图分类任务。来自不同应用的十四个数据集的实验结果表明，我们提出的方法始终优于最先进的方法，同时具有更少的参数和更高的训练效率。
