# ffstruc2vec: Flat, Flexible and Scalable Learning of Node Representations from Structural Identities

arXiv: https://arxiv.org/abs/2504.01122 · HTML: https://mlsanity.com/paper/2504.01122/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 75 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Builds on (most cited first, 28 of 28)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec](https://mlsanity.com/paper/1710.02971/) (2017)
- [Neural-Symbolic Learning and Reasoning: A Survey and Interpretation](https://mlsanity.com/paper/1711.03902/) (2017)
- [Cooperative Game Theory Approaches for Network Partitioning](https://mlsanity.com/paper/1707.03587/) (2017)
- [Community detection in networks: A user guide](https://mlsanity.com/paper/1608.00163/) (2016)
- [Learning Structural Node Embeddings Via Diffusion Wavelets](https://mlsanity.com/paper/1710.10321/) (2017)
- [A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts](https://mlsanity.com/paper/2105.07190/) (2021)
- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [Anonymous Walk Embeddings](https://mlsanity.com/paper/1805.11921/) (2018)
- [GraLSP: Graph Neural Networks with Local Structural Patterns](https://mlsanity.com/paper/1911.07675/) (2019)
- [A Survey of Community Search Over Big Graphs](https://mlsanity.com/paper/1904.12539/) (2019)
- [Stochastic Graphlet Embedding](https://mlsanity.com/paper/1702.00156/) (2017)
- [SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning](https://mlsanity.com/paper/2303.03379/) (2023)
- [NodeSim: Node Similarity based Network Embedding for Diverse Link Prediction](https://mlsanity.com/paper/2102.00785/) (2021)
- [Network Community Detection: A Review and Visual Survey](https://mlsanity.com/paper/1708.00977/) (2017)
- [RDF-star2Vec: RDF-star Graph Embeddings for Data Mining](https://mlsanity.com/paper/2312.15626/) (2023)
- [LoNe Sampler: Graph node embeddings by coordinated local neighborhood sampling](https://mlsanity.com/paper/2211.15114/) (2022)
- [Force-directed graph embedding with hops distance](https://mlsanity.com/paper/2309.05865/) (2023)
- [A Self-Attention Network based Node Embedding Model](https://mlsanity.com/paper/2006.12100/) (2020)
- [Multi-Level Graph Contrastive Learning](https://mlsanity.com/paper/2107.02639/) (2021)
- [CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations](https://mlsanity.com/paper/2111.03262/) (2021)
- [Node Embeddings via Neighbor Embeddings](https://mlsanity.com/paper/2503.23822/) (2025)

## Abstract (arXiv)

Node embedding refers to techniques that generate low-dimensional vector representations of nodes in a graph while preserving specific properties of the nodes. A key challenge in the field is developing scalable methods that can preserve structural properties suitable for the required types of structural patterns of a given downstream application task. While most existing methods focus on preserving node proximity, those that do preserve structural properties often lack the flexibility to preserve various types of structural patterns required by downstream application tasks. This paper introduces ffstruc2vec, a scalable deep-learning framework for learning node embedding vectors that preserve structural identities. Its flat, efficient architecture allows high flexibility in capturing diverse types of structural patterns, enabling broad adaptability to various downstream application tasks. The proposed framework significantly outperforms existing approaches across diverse unsupervised and supervised tasks in practical applications. Moreover, ffstruc2vec enables explainability by quantifying how individual structural patterns influence task outcomes, providing actionable interpretation. To our knowledge, no existing framework combines this level of flexibility, scalability, and structural interpretability, underscoring its unique capabilities.