# Stochastic Graphlet Embedding

arXiv: https://arxiv.org/abs/1702.00156 · HTML: https://mlsanity.com/paper/1702.00156/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2017.

## Built on by (most cited first, 9 of 9)

- [FFNB: Forgetting-Free Neural Blocks for Deep Continual Visual Learning](https://mlsanity.com/paper/2111.11366/) (2021)
- [Computing Graph Descriptors on Edge Streams](https://mlsanity.com/paper/2109.01494/) (2021)
- [Active learning for interactive satellite image change detection](https://mlsanity.com/paper/2110.04250/) (2021)
- [Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing](https://mlsanity.com/paper/2404.00589/) (2024)
- [Simplifying complex machine learning by linearly separable network embedding spaces](https://mlsanity.com/paper/2410.01865/) (2024)
- [Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection](https://mlsanity.com/paper/2203.11559/) (2022)
- [Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships](https://mlsanity.com/paper/2310.15999/) (2023)
- [ffstruc2vec: Flat, Flexible and Scalable Learning of Node Representations from Structural Identities](https://mlsanity.com/paper/2504.01122/) (2025)
- [Can Circuit Alignment Predict OOD Generalization?](https://mlsanity.com/paper/2609.31996/) (2026)

## Abstract (arXiv)

Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments, etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of -- explicit/implicit -- graph vectorization and embedding. This embedding process should be resilient to intra-class graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding (SGE) that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When combined with maximum margin classifiers, these graphlet-based representations have positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases.