  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.
