Fast Search for Dynamic Multi-Relational Graphs

TL;DR

This paper introduces a novel algorithm and data structure for efficiently executing continuous subgraph searches on dynamic multi-relational graphs, enabling real-time monitoring of social media and news streams.

Contribution

It presents the SJ-Tree data structure and an exact subgraph search algorithm tailored for dynamic multi-relational graphs, improving real-time query performance.

Findings

Demonstrates efficiency on real-world datasets

Validates the approach's accuracy and speed

Enables real-time social media and news monitoring

Abstract

Acting on time-critical events by processing ever growing social media or news streams is a major technical challenge. Many of these data sources can be modeled as multi-relational graphs. Continuous queries or techniques to search for rare events that typically arise in monitoring applications have been studied extensively for relational databases. This work is dedicated to answer the question that emerges naturally: how can we efficiently execute a continuous query on a dynamic graph? This paper presents an exact subgraph search algorithm that exploits the temporal characteristics of representative queries for online news or social media monitoring. The algorithm is based on a novel data structure called the Subgraph Join Tree (SJ-Tree) that leverages the structural and semantic characteristics of the underlying multi-relational graph. The paper concludes with extensive experimentation on several real-world datasets that demonstrates the validity of this approach.