# Subgraph nomination: Query by Example Subgraph Retrieval in Networks

arXiv: https://arxiv.org/abs/2101.12430 · HTML: https://mlsanity.com/paper/2101.12430/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 29 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Graph Matching Networks for Learning the Similarity of Graph Structured Objects](https://mlsanity.com/paper/1904.12787/) (2019)
- [SimGNN: A Neural Network Approach to Fast Graph Similarity Computation](https://mlsanity.com/paper/1808.05689/) (2018)
- [Hierarchical Block Structures and High-resolution Model Selection in Large Networks](https://mlsanity.com/paper/1310.4377/) (2013)
- [Community Detection and Classification in Hierarchical Stochastic Blockmodels](https://mlsanity.com/paper/1503.02115/) (2015)
- [Foundations of Modern Query Languages for Graph Databases](https://mlsanity.com/paper/1610.06264/) (2016)
- [Hierarchical community detection by recursive partitioning](https://mlsanity.com/paper/1810.01509/) (2018)
- [Vertex nomination schemes for membership prediction](https://mlsanity.com/paper/1312.2638/) (2013)
- [Vertex Nomination, Consistent Estimation, and Adversarial Modification](https://mlsanity.com/paper/1905.01776/) (2019)
- [Vertex nomination: The canonical sampling and the extended spectral nomination schemes](https://mlsanity.com/paper/1802.04960/) (2018)
- [Matched Filters for Noisy Induced Subgraph Detection](https://mlsanity.com/paper/1803.02423/) (2018)
- [Vertex Nomination Via Seeded Graph Matching](https://mlsanity.com/paper/1705.00674/) (2017)
- [On consistent vertex nomination schemes](https://mlsanity.com/paper/1711.05610/) (2017)
- [On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching](https://mlsanity.com/paper/1607.01369/) (2016)

## Built on by (most cited first, 1 of 1)

- [Detection of Model-based Planted Pseudo-cliques in Random Dot Product Graphs by the Adjacency Spectral Embedding and the Graph Encoder Embedding](https://mlsanity.com/paper/2312.11054/) (2023)

## Abstract (arXiv)

This paper introduces the subgraph nomination inference task, in which example subgraphs of interest are used to query a network for similarly interesting subgraphs. This type of problem appears time and again in real world problems connected to, for example, user recommendation systems and structural retrieval tasks in social and biological/connectomic networks. We formally define the subgraph nomination framework with an emphasis on the notion of a user-in-the-loop in the subgraph nomination pipeline. In this setting, a user can provide additional post-nomination light supervision that can be incorporated into the retrieval task. After introducing and formalizing the retrieval task, we examine the nuanced effect that user-supervision can have on performance, both analytically and across real and simulated data examples.