# GraLSP: Graph Neural Networks with Local Structural Patterns

arXiv: https://arxiv.org/abs/1911.07675 · HTML: https://mlsanity.com/paper/1911.07675/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2019.

## Built on by (most cited first, 25 of 25)

- [Reconstruction for Powerful Graph Representations](https://mlsanity.com/paper/2110.00577/) (2021)
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- [ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network](https://mlsanity.com/paper/2110.07888/) (2021)
- [Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels](https://mlsanity.com/paper/2104.02995/) (2021)
- [Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks](https://mlsanity.com/paper/2107.10234/) (2021)
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- [Topological Regularization for Graph Neural Networks Augmentation](https://mlsanity.com/paper/2104.02478/) (2021)
- [Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations](https://mlsanity.com/paper/2211.02501/) (2022)
- [A Survey on Role-Oriented Network Embedding](https://mlsanity.com/paper/2107.08379/) (2021)
- [What Do GNNs Actually Learn? Towards Understanding their Representations](https://mlsanity.com/paper/2304.10851/) (2023)
- [Network representation learning: A macro and micro view](https://mlsanity.com/paper/2111.10772/) (2021)
- [Scalable Motif Counting for Large-scale Temporal Graphs](https://mlsanity.com/paper/2204.09236/) (2022)
- [Strong Transitivity Relations and Graph Neural Networks](https://mlsanity.com/paper/2401.01384/) (2024)
- [DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation](https://mlsanity.com/paper/2409.08946/) (2024)
- [Efficient Network Embedding by Approximate Equitable Partitions](https://mlsanity.com/paper/2409.10160/) (2024)
- [Efficient Identity and Position Graph Embedding via Spectral-Based Random Feature Aggregation](https://mlsanity.com/paper/2505.20992/) (2025)
- [Representation Learning on Heterostructures via Heterogeneous Anonymous Walks](https://mlsanity.com/paper/2201.06972/) (2022)
- [A Network Science perspective of Graph Convolutional Networks: A survey](https://mlsanity.com/paper/2301.04824/) (2023)
- [Structure-Guided Input Graph for GNNs facing Heterophily](https://mlsanity.com/paper/2412.01757/) (2024)
- [Supervised Attention Using Homophily in Graph Neural Networks](https://mlsanity.com/paper/2307.05217/) (2023)
- [Positional Encoding-based Resident Identification in Multi-resident Smart Homes](https://mlsanity.com/paper/2310.17836/) (2023)
- [ffstruc2vec: Flat, Flexible and Scalable Learning of Node Representations from Structural Identities](https://mlsanity.com/paper/2504.01122/) (2025)
- [Topology-induced Operators Reveal Complementary Graph Representations without Training](https://mlsanity.com/paper/2609.08152/) (2026)

## Abstract (arXiv)

It is not until recently that graph neural networks (GNNs) are adopted to perform graph representation learning, among which, those based on the aggregation of features within the neighborhood of a node achieved great success. However, despite such achievements, GNNs illustrate defects in identifying some common structural patterns which, unfortunately, play significant roles in various network phenomena. In this paper, we propose GraLSP, a GNN framework which explicitly incorporates local structural patterns into the neighborhood aggregation through random anonymous walks. Specifically, we capture local graph structures via random anonymous walks, powerful and flexible tools that represent structural patterns. The walks are then fed into the feature aggregation, where we design various mechanisms to address the impact of structural features, including adaptive receptive radius, attention and amplification. In addition, we design objectives that capture similarities between structures and are optimized jointly with node proximity objectives. With the adequate leverage of structural patterns, our model is able to outperform competitive counterparts in various prediction tasks in multiple datasets.