Big Graph Search: Challenges and Techniques

TL;DR

This paper discusses the challenges and techniques of big graph search, emphasizing its importance for social computing applications and providing a comprehensive analysis of methods to address these challenges.

Contribution

It formalizes the big graph search problem, analyzes its challenges, and categorizes techniques into query, data, and distributed computing methods.

Findings

Identifies key challenges in big graph search

Classifies techniques into three main categories

Highlights the importance of distributed computing

Abstract

On one hand, compared with traditional relational and XML models, graphs have more expressive power and are widely used today. On the other hand, various applications of social computing trigger the pressing need of a new search paradigm. In this article, we argue that big graph search is the one filling this gap. To show this, we first introduce the application of graph search in various scenarios. We then formalize the graph search problem, and give an analysis of graph search from an evolutionary point of view, followed by the evidences from both the industry and academia. After that, we analyze the difficulties and challenges of big graph search. Finally, we present three classes of techniques towards big graph search: query techniques, data techniques and distributed computing techniques.