Analysis and algorithms for ℓ-based semi-supervised learning on graphs

Semi-Supervised Learning on Graphs Based on Local Label Distributions

In this work, we propose a novel approach for the semi-supervised node classification. Precisely, we propose a method which takes labels in the local neighborhood of different locality levels into consideration. Most previous approaches that tackle the problem of node classification consider nodes to be similar, if they have shared neighbors or are close to each other in the graph. Recent metho...

Hyper-parameter Learning for Graph Based Semi-supervised Learning Algorithms

Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms

Semi-supervised learning algorithms have been successfully applied in many applications with scarce labeled data, by utilizing the unlabeled data. One important category is graph based semi-supervised learning algorithms, for which the performance depends considerably on the quality of the graph, or its hyperparameters. In this paper, we deal with the less explored problem of learning the graph...

Online Semi-Supervised Learning on Quantized Graphs

In this paper, we tackle the problem of online semi-supervised learning (SSL). When data arrive in a stream, the dual problems of computation and data storage arise for any SSL method. We propose a fast approximate online SSL algorithm that solves for the harmonic solution on an approximate graph. We show, both empirically and theoretically, that good behavior can be achieved by collapsing near...

Semi-supervised Learning on Directed Graphs

Given a directed graph in which some of the nodes are labeled, we investigate the question of how to exploit the link structure of the graph to infer the labels of the remaining unlabeled nodes. To that extent we propose a regularization framework for functions defined over nodes of a directed graph that forces the classification function to change slowly on densely linked subgraphs. A powerful...