Label Propagation on K-partite Graphs with Heterophily

TL;DR

This paper introduces a novel label propagation model for heterogeneous, K-partite graphs under heterophily, enabling efficient inference and incremental updates, validated by experiments on real datasets.

Contribution

It proposes a new K-partite label propagation model for heterophily in heterogeneous graphs, with a fast inference algorithm and an incremental update method.

Findings

Effective on real datasets

Outperforms state-of-the-art methods

Supports fast incremental updates

Abstract

In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption. Homophily label propagation (i.e., two connected nodes share similar labels) in homogeneous graph (with same types of vertices and relations) has been extensively studied before. Unfortunately, real-life networks are heterogeneous, they contain different types of vertices (e.g., users, images, texts) and relations (e.g., friendships, co-tagging) and allow for each node to propagate both the same and opposite copy of labels to its neighbors. We propose a $\mathcal{K}$-partite label propagation model to handle the mystifying combination of heterogeneous nodes/relations and heterophily propagation. With this model, we develop a novel label inference algorithm framework with update rules in near-linear time complexity. Since real networks change over time, we devise an incremental approach, which supports fast updates for both new data and evidence (e.g., ground truth labels) with guaranteed efficiency. We further provide a utility function to automatically determine whether an incremental or a re-modeling approach is favored. Extensive experiments on real datasets have verified the effectiveness and efficiency of our approach, and its superiority over the state-of-the-art label propagation methods.