Subgraph nomination: Query by Example Subgraph Retrieval in Networks

TL;DR

This paper defines the subgraph nomination task, where example subgraphs are used to retrieve similar subgraphs in networks, emphasizing user-in-the-loop supervision to improve performance.

Contribution

It introduces a formal framework for subgraph nomination with user-in-the-loop supervision and analyzes its impact on retrieval effectiveness.

Findings

User supervision enhances subgraph retrieval accuracy.

Formal framework for subgraph nomination is established.

Performance improvements are demonstrated on real and simulated data.

Abstract

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.