Graph Representation Learning Beyond Node and Homophily
发布时间：2022-05-24
点击次数：
影响因子：7.0
发表刊物：IEEE Transactions on Knowledge and Data Engineering （CCF A类）
摘要：Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding. However, existing graph representation learning approaches are largely designed under the node homophily assumption: connected nodes tend to have similar labels and aim to optimize performance on node-centric downstream tasks. Their design apparently against the task-agnostic principle and generally suffer poor performance in tasks, e.g., edge classification task, that demands feature signals beyond both the node-view and homophily assumption. To condense different feature signals into the edge embeddings, this paper proposes PairE, a novel unsupervised graph embedding method using two paired nodes as the basic unit of embedding to retain the high-frequency signals between nodes to support both node-related and edge-related tasks. Accordingly, a multi-self-supervised autoencoder is designed to fulfill two pretext tasks: one retains the high-frequency signal better, and another enhances the representation of commonality. Our extensive experiments on a diversity of benchmark datasets clearly show that PairE outperforms the unsupervised state-of-the-art baselines, with up to 81% improvement on the edge classification tasks that rely on both the high and low-frequency signals in the pair and up to 42% performance gain on the node classification tasks.
合写作者：Lin Bei, Luo Binli
第一作者：Li You
论文类型：期刊论文
通讯作者：Gui Ning
是否译文：否
发表时间：2022-01-03
收录刊物：SSCI、SCI、EI