Label propagation is one of the state-of-the-art methods for semi-supervised learning, which estimates labels by propagating label information through a graph. Label propagation assumes that data points (nodes) connected in a graph should have similar labels. Consequently, the label estimation heavily depends on edge weights in a graph which represent similarity of each node pair. We propose a ...

Learning from complex data is becoming increasingly important, and graph kernels have recently evolved into a rapidly developing branch of learning on structured data. However, previously proposed kernels rely on having discrete node label information. In this paper, we explore the power of continuous node-level features for propagation-based graph kernels. Speci cally, propagation kernels expl...

Graph neural networks (GNNs) have emerged as effective approaches for graph analysis, especially in the scenario of semi-supervised learning. Despite its success, GNN often suffers from over-smoothing and over-fitting problems, which affects performance on node classification tasks. We analyze that an alternative method, label propagation algorithm (LPA), avoids aforementioned problems thus it ...

Label Propagation Algorithm (LPA) and Graph Convolutional Neural Networks (GCN) are both message passing algorithms on graphs. Both solve the task of node classification, but LPA propagates label information across edges graph, while GCN transforms feature information. However, conceptually similar, theoretical relationship between has not yet been systematically investigated. Moreover, it is u...

Partial Label Learning (PLL) aims to learn from the data where each training example is associated with a set of candidate labels, among which only one correct. The key deal such problem disambiguate label sets and obtain correct assignments between instances their labels. In this paper, we interpret as instance-to-label matchings, reformulate task PLL matching selection problem. To model probl...