# Exploring Communities in Large Profiled Graphs

## TL;DR

This paper introduces profiled community search (PCS) in large hierarchical labeled graphs, demonstrating its effectiveness in identifying thematically coherent communities and proposing an efficient tree index for fast online querying.

## Contribution

It presents the PCS problem, shows its advantages over existing methods, and develops a tree index to enable efficient community search in profiled graphs.

## Key findings

- PCS identifies thematically coherent communities.
- The tree index significantly improves search efficiency.
- PCS outperforms existing community search approaches.

## Abstract

Given a graph $G$ and a vertex $q\in G$, the community search (CS) problem aims to efficiently find a subgraph of $G$ whose vertices are closely related to $q$. Communities are prevalent in social and biological networks, and can be used in product advertisement and social event recommendation. In this paper, we study profiled community search (PCS), where CS is performed on a profiled graph. This is a graph in which each vertex has labels arranged in a hierarchical manner. Extensive experiments show that PCS can identify communities with themes that are common to their vertices, and is more effective than existing CS approaches. As a naive solution for PCS is highly expensive, we have also developed a tree index, which facilitate efficient and online solutions for PCS.