HARP: Hierarchical Representation Learning for Networks

Understand

We present HARP, a novel method for learning low dimensional embeddings of a graph's nodes which preserves higher-order structural features.

• Our proposed method achieves this by compressing the input graph prior to embedding it, effectively avoiding troublesome embedding configurations (i.e.
• local minima) which can pose problems to non-convex optimization.
• HARP works by finding a smaller graph which approximates the global structure of its input.