# HARP: Hierarchical Representation Learning for Networks

arXiv: https://arxiv.org/abs/1706.07845 · HTML: https://mlsanity.com/paper/1706.07845/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2017.

## Built on by (most cited first, 30 of 58)

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- [Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding](https://mlsanity.com/paper/2110.07582/) (2021)
- [Learning Hierarchical Graph Neural Networks for Image Clustering](https://mlsanity.com/paper/2107.01319/) (2021)
- [A Broader Picture of Random-walk Based Graph Embedding](https://mlsanity.com/paper/2110.12344/) (2021)
- [A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening](https://mlsanity.com/paper/2306.08854/) (2023)
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- [FairGAT: Fairness-aware Graph Attention Networks](https://mlsanity.com/paper/2303.14591/) (2023)
- [Data Augmentation on Graphs: A Technical Survey](https://mlsanity.com/paper/2212.09970/) (2022)
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- [REFINE: Random RangE FInder for Network Embedding](https://mlsanity.com/paper/2108.10703/) (2021)
- [A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations](https://mlsanity.com/paper/2204.05885/) (2022)
- [FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy](https://mlsanity.com/paper/2205.15896/) (2022)
- [Cleora: A Simple, Strong and Scalable Graph Embedding Scheme](https://mlsanity.com/paper/2102.02302/) (2021)
- [Semi-supervised Network Embedding with Differentiable Deep Quantisation](https://mlsanity.com/paper/2108.09128/) (2021)
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- [Recent Advances in Malware Detection: Graph Learning and Explainability](https://mlsanity.com/paper/2502.10556/) (2025)
- [Calibrating and Improving Graph Contrastive Learning](https://mlsanity.com/paper/2101.11525/) (2021)
- [Diversified Multiscale Graph Learning with Graph Self-Correction](https://mlsanity.com/paper/2103.09754/) (2021)
- [JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms](https://mlsanity.com/paper/2101.06427/) (2021)
- [Graph Embedding with Hierarchical Attentive Membership](https://mlsanity.com/paper/2111.00604/) (2021)
- [FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning](https://mlsanity.com/paper/2211.09925/) (2022)
- [Unsupervised Graph Clustering with Deep Structural Entropy](https://mlsanity.com/paper/2505.14040/) (2025)
- [Multiple Kernel Representation Learning on Networks](https://mlsanity.com/paper/2106.05057/) (2021)
- [Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE](https://mlsanity.com/paper/2306.03659/) (2023)

## Abstract (arXiv)

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. This simplified graph is used to learn a set of initial representations, which serve as good initializations for learning representations in the original, detailed graph. We inductively extend this idea, by decomposing a graph in a series of levels, and then embed the hierarchy of graphs from the coarsest one to the original graph. HARP is a general meta-strategy to improve all of the state-of-the-art neural algorithms for embedding graphs, including DeepWalk, LINE, and Node2vec. Indeed, we demonstrate that applying HARP's hierarchical paradigm yields improved implementations for all three of these methods, as evaluated on both classification tasks on real-world graphs such as DBLP, BlogCatalog, CiteSeer, and Arxiv, where we achieve a performance gain over the original implementations by up to 14% Macro F1.