Windows into Relational Events: Data Structures for Contiguous Subsequences of Edges

TL;DR

This paper introduces efficient data structures for analyzing temporal social network data, enabling fast queries on subgraphs formed from contiguous timestamp intervals, with applications in connectivity and reachability analysis.

Contribution

The authors develop near-linear preprocessing, linear space, and sublogarithmic query time data structures for temporal subgraph analysis in social networks.

Findings

Supports fast queries on connected components and cycles

Enables efficient reachability and degree-based queries

Achieves near-linear preprocessing with sublogarithmic query times

Abstract

We consider the problem of analyzing social network data sets in which the edges of the network have timestamps, and we wish to analyze the subgraphs formed from edges in contiguous subintervals of these timestamps. We provide data structures for these problems that use near-linear preprocessing time, linear space, and sublogarithmic query time to handle queries that ask for the number of connected components, number of components that contain cycles, number of vertices whose degree equals or is at most some predetermined value, number of vertices that can be reached from a starting set of vertices by time-increasing paths, and related queries.