A Review of In-Memory Space-Efficient Data Structures for Temporal Graphs

TL;DR

This paper reviews space-efficient data structures for temporal graphs, focusing on methods to bring large-scale data into main memory and optimize query performance, highlighting current techniques and future research directions.

Contribution

It provides a comprehensive overview of existing data structures for temporal graphs, emphasizing space efficiency and query speed improvements, and suggests future research avenues.

Findings

Diverse data compression techniques are used in current structures.

Self-indexed compressed data structures are prominent.

Future research directions are identified for enhancing state-of-the-art.

Abstract

Temporal graphs model relationships among entities over time. Recent studies applied temporal graphs to abstract complex systems such as continuous communication among participants of social networks. Often, the amount of data is larger than main memory, therefore, we need specialized structures that balance space usage and query efficiency. In this paper, we review space-efficient data structures that bring large temporal graphs from external memory to primary memory and speed up specialized queries. We found a great variety of studies using data compression techniques and self-indexed compressed data structures. We point further research directions to improve the current state-of-the-art.