Finding Theme Communities from Database Networks

TL;DR

This paper introduces scalable algorithms TCFI and TC-Tree for efficiently discovering and indexing theme communities in database networks, enabling rapid retrieval of meaningful subgraphs despite computational challenges.

Contribution

The paper proposes novel properties of theme communities and develops scalable algorithms TCFI and TC-Tree for effective discovery and indexing in large database networks.

Findings

TCFI effectively prunes patterns, reducing computational complexity.

TC-Tree indexes hundreds of millions of theme communities for fast retrieval.

Algorithms demonstrate high scalability and accuracy in experiments.

Abstract

Given a database network where each vertex is associated with a transaction database, we are interested in finding theme communities. Here, a theme community is a cohesive subgraph such that a common pattern is frequent in all transaction databases associated with the vertices in the subgraph. Finding all theme communities from a database network enjoys many novel applications. However, it is challenging since even counting the number of all theme communities in a database network is #P-hard. Inspired by the observation that a theme community shrinks when the length of the pattern increases, we investigate several properties of theme communities and develop TCFI, a scalable algorithm that uses these properties to effectively prune the patterns that cannot form any theme community. We also design TC-Tree, a scalable algorithm that decomposes and indexes theme communities efficiently. Retrieving a ranked list of theme communities from a TC-Tree of hundreds of millions of theme communities takes less than 1 second. Extensive experiments and a case study demonstrate the effectiveness and scalability of TCFI and TC-Tree in discovering and querying meaningful theme communities from large database networks.