Link prediction aims to infer the link existence between pairs of nodes in networks/graphs. Despite their wide application, the success of traditional link prediction algorithms is hindered by three major challenges -- link sparsity, node attribute noise and dynamic changes -- that are faced by many real-world networks. To address these challenges, we propose a Contextualized Self-Supervised Learning (CSSL) framework that fully exploits structural context prediction for link prediction. The proposed CSSL framework learns a link encoder to infer the link existence probability from paired node embeddings, which are constructed via a transformation on node attributes. To generate informative node embeddings for link prediction, structural context prediction is leveraged as a self-supervised learning task to boost the link prediction performance. Two types of structural context are investigated, i.e., context nodes collected from random walks vs. context subgraphs. The CSSL framework can be trained in an end-to-end manner, with the learning of model parameters supervised by both the link prediction and self-supervised learning tasks. The proposed CSSL is a generic and flexible framework in the sense that it can handle both attributed and non-attributed networks, and operate under both transductive and inductive link prediction settings. Extensive experiments and ablation studies on seven real-world benchmark networks demonstrate the superior performance of the proposed self-supervision based link prediction algorithm over state-of-the-art baselines, on different types of networks under both transductive and inductive settings. The proposed CSSL also yields competitive performance in terms of its robustness to node attribute noise and scalability over large-scale networks.
翻译:链接预测旨在推断网络/绘图网对节点之间存在的联系。尽管传统链接预测算法的应用范围广泛,但传统链接预测算法的成功受到三大挑战的阻碍 -- -- 许多现实世界网络所面临的链路、节点属性噪音和动态变化 -- -- 许多现实世界网络面临的三重挑战 -- -- 链接的偏斜、节点属性噪音和动态变化。为了应对这些挑战,我们提议了一个“环境化自闭学习”框架,充分利用结构背景背景预测来进行链接预测。拟议的CSSL框架可以学习一个链接编码,从配对的节点嵌入的链接概率来推断存在的可能性。为了产生信息化的连接预测的节点嵌入类型,结构背景预测被作为自我监督的学习任务加以利用,以提高链接预测绩效的绩效。为了应对这些挑战,我们提议了两种结构背景,即从随机行走收集的背景节点与背景次谱。CSSL框架可以以端对端到端通向端方式进行培训,同时学习由链接预测和自我监控的学习状态参数。拟议的CSS LL在高级网络中进行直观和弹性的预测,在不连续的轨道上,在拟议的轨道上运行的交付的轨道上,在真实和递流流流流路路路基路路路路路路路路路路基下进行。