Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification

Abstract

In this work, we study semi-supervised multi-label node classification in attributed graphs. We propose Multi-Label-Graph-Walk, a collaborative graph walk method that tunes node representations with available label assignments through reinforcement learning. Multiple label-specific agents walk over the graph cooperatively and learn relationships between labels, graph attributes, and label correlations.