# HUGE: An Efficient and Scalable Subgraph Enumeration System

arXiv: https://arxiv.org/abs/2103.14294 · HTML: https://mlsanity.com/paper/2103.14294/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2021.

## Built on by (most cited first, 3 of 3)

- [Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching](https://mlsanity.com/paper/2201.11251/) (2022)
- [Interpreting Graph Inference with Skyline Explanations](https://mlsanity.com/paper/2505.07635/) (2025)
- [gHAWK: Local and Global Structure Encoding for Scalable Training of Graph Neural Networks on Knowledge Graphs](https://mlsanity.com/paper/2512.08274/) (2025)

## Abstract (arXiv)

Subgraph enumeration is a fundamental problem in graph analytics, which aims to find all instances of a given query graph on a large data graph. In this paper, we propose a system called HUGE to efficiently process subgraph enumeration at scale in the distributed context. HUGE features 1) an optimiser to compute an advanced execution plan without the constraints of existing works; 2) a hybrid communication layer that supports both pushing and pulling communication; 3) a novel two-stage execution mode with a lock-free and zero-copy cache design, 4) a BFS/DFS-adaptive scheduler to bound memory consumption, and 5) two-layer intra- and inter-machine load balancing. HUGE is generic such that all existing distributed subgraph enumeration algorithms can be plugged in to enjoy automatic speed up and bounded-memory execution.