Whiletheseapproaches arewidely used inpractice andachieveimpressiveempirical gains, their theoretical understanding largely lags behind. Towards bridging this gap, we present a unifying perspectivewhere several such approaches can beviewed asimposing a regularization on the representation via alearnable function using unlabeled data. Wepropose adiscriminativetheoretical framework for analyzing the sample complexity of these approaches, which generalizes the framework of [3] to allow learnable regularization functions.

This is the state-of-the-art graph contrastive learning based recommendation method, which proposes randomly node dropout, edge dropout, and random walk for augmentation onthebipartite graph.

Graph convolution networks (GCNs) for recommendations haveemerged asan important research topic due to their ability to exploit higher-order neighbors. Despite their success, most of them suffer from the popularity bias brought by a small number of active users and popular items.

Pedestrian pre-collision pose is one of the key factors to determine the degree of pedestrian-vehicle injury in collision. Human pose estimation algorithm is an effective method to estimate pedestrian emergency pose from accident video.

Technically,various methods of both categories exploit the link between graph data and linear algebra by representing graphs by their (normalized) adjacency matrix. Such methods are often defined or can be interpreted in terms ofwalks. On the other hand, the Weisfeiler-Leman heuristic for graph isomorphism testing has attracted great interest in machine learning [33, 34].