TopCom: Index for Shortest Distance Query in Directed Graph

TL;DR

TopCom is a new indexing method for exact shortest distance queries in directed graphs, offering superior scalability and faster query response times by leveraging DAG structures, especially in graphs with small SCCs.

Contribution

The paper introduces TopCom, a novel index structure that improves exact shortest distance querying efficiency, outperforming existing methods in scalability and speed.

Findings

TopCom outperforms IS-Label and TreeMap in scalability.

TopCom provides faster query responses for large graphs.

Exploiting DAG structures enhances performance in graphs with small SCCs.

Abstract

Finding shortest distance between two vertices in a graph is an important problem due to its numerous applications in diverse domains, including geo-spatial databases, social network analysis, and information retrieval. Classical algorithms (such as, Dijkstra) solve this problem in polynomial time, but these algorithms cannot provide real-time response for a large number of bursty queries on a large graph. So, indexing based solutions that pre-process the graph for efficiently answering (exactly or approximately) a large number of distance queries in real-time is becoming increasingly popular. Existing solutions have varying performance in terms of index size, index building time, query time, and accuracy. In this work, we propose T OP C OM , a novel indexing-based solution for exactly answering distance queries. Our experiments with two of the existing state-of-the-art methods (IS-Label and TreeMap) show the superiority of T OP C OM over these two methods considering scalability and query time. Besides, indexing of T OP C OM exploits the DAG (directed acyclic graph) structure in the graph, which makes it significantly faster than the existing methods if the SCCs (strongly connected component) of the input graph are relatively small.