  A scalable semi-supervised node classification method on graph-structured
data, called GraphHop, is proposed in this work. The graph contains attributes
of all nodes but labels of a few nodes. The classical label propagation (LP)
method and the emerging graph convolutional network (GCN) are two popular
semi-supervised solutions to this problem. The LP method is not effective in
modeling node attributes and labels jointly or facing a slow convergence rate
on large-scale graphs. GraphHop is proposed to its shortcoming. With proper
initial label vector embeddings, each iteration of GraphHop contains two steps:
1) label aggregation and 2) label update. In Step 1, each node aggregates its
neighbors' label vectors obtained in the previous iteration. In Step 2, a new
label vector is predicted for each node based on the label of the node itself
and the aggregated label information obtained in Step 1. This iterative
procedure exploits the neighborhood information and enables GraphHop to perform
well in an extremely small label rate setting and scale well for very large
graphs. Experimental results show that GraphHop outperforms state-of-the-art
graph learning methods on a wide range of tasks (e.g., multi-label and
multi-class classification on citation networks, social graphs, and commodity
consumption graphs) in graphs of various sizes. Our codes are publicly
available on GitHub (https://github.com/TianXieUSC/GraphHop).
