Supporting Window Analytics over Large-scale Dynamic Graphs

TL;DR

This paper introduces graph window queries, formalizes their processing, and develops novel indices that significantly improve performance for large-scale dynamic graph analytics.

Contribution

It formally defines graph window functions, proposes two new indices for efficient processing, and demonstrates substantial performance improvements over existing methods.

Findings

Index-based solutions achieve up to four orders of magnitude performance gain.

Proposed indices outperform existing algorithms in scalability and efficiency.

Effective processing of large-scale dynamic graph window queries is demonstrated.

Abstract

In relational DBMS, window functions have been widely used to facilitate data analytics. Surprisingly, while similar concepts have been employed for graph analytics, there has been no explicit notions of graph window analytic functions. In this paper, we formally introduce window queries for graph analytics. In such queries, for each vertex, the analysis is performed on a window of vertices defined based on the graph structure. In particular, we identify two instantiations, namely the k-hop window and the topological window. We develop two novel indices, Dense Block index (DBIndex) and Inheritance index (I-Index), to facilitate efficient processing of these two types of windows respectively. Extensive experiments are conducted over both real and synthetic datasets with hundreds of millions of vertices and edges. Experimental results indicate that our proposed index-based query processing solutions achieve four orders of magnitude of query performance gain than the non-index algorithm and are superior over EAGR wrt scalability and efficiency.