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 methods for attributed graphs additionally take attributes of the neighboring nodes into account. We argue that the labels of the neighbors bear important information and considering them helps to improve classification quality. Two nodes which are similar based on labels in their neighborhood do not need to lie close-by in the graph and may even belong to different connected components. Considering labels can improve node classification for graphs with and without node attributes. However, as we will show, existing methods cannot be adapted to consider the labels of neighboring nodes in a straightforward fashion. Therefore, we propose a new method to learn label-based node embeddings which can mirror a variety of relations between the class labels of neighboring nodes. Furthermore, we propose several network architectures which combine multiple representations of the label distribution in the neighborhood with different localities. Our experimental evaluation demonstrates that our new methods can significantly improve the prediction quality on real world data sets.