DDSL: Efficient Subgraph Listing on Distributed and Dynamic Graphs

TL;DR

This paper introduces DDSL, an efficient distributed approach for subgraph listing on large-scale dynamic graphs, which incrementally updates results and outperforms existing methods in response time.

Contribution

It proposes a novel distributed and dynamic subgraph listing framework with incremental updates and an optimized join algorithm for dynamic graphs.

Findings

DDSL outperforms existing methods in response time.

The approach effectively handles large-scale dynamic graphs.

The cost model accurately estimates I/O costs for subgraph listing.

Abstract

Subgraph listing is a fundamental problem in graph theory and has wide applications in areas like sociology, chemistry, and social networks. Modern graphs can usually be large-scale as well as highly dynamic, which challenges the efficiency of existing subgraph listing algorithms. Recent works have shown the benefits of partitioning and processing big graphs in a distributed system, however, there is only few work targets subgraph listing on dynamic graphs in a distributed environment. In this paper, we propose an efficient approach, called Distributed and Dynamic Subgraph Listing (DDSL), which can incrementally update the results instead of running from scratch. DDSL follows a general distributed join framework. In this framework, we use a Neighbor-Preserved storage for data graphs, which takes bounded extra space and supports dynamic updating. After that, we propose a comprehensive cost model to estimate the I/O cost of listing subgraphs. Then based on this cost model, we develop an algorithm to find the optimal join tree for a given pattern. To handle dynamic graphs, we propose an efficient left-deep join algorithm to incrementally update the join results. Extensive experiments are conducted on real-world datasets. The results show that DDSL outperforms existing methods in dealing with both static dynamic graphs in terms of the responding time.