  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.
