A hybrid adjacency and time-based data structure for analysis of temporal networks

Abstract

Dynamic or temporal networks enable representation of time-varying edges between nodes. Conventional adjacency-based data structures used for storing networks such as adjacency lists were designed without incorporating time and can thus quickly retrieve all edges between two sets of nodes (a node-based slice) but cannot quickly retrieve all edges that occur within a given time interval (a time-based slice). We propose a hybrid data structure for storing temporal networks that stores edges in both an adjacency dictionary, enabling rapid node-based slices, and an interval tree, enabling rapid time-based slices. Our hybrid structure also enables compound slices, where one needs to slice both over nodes and time, either by slicing first over nodes or slicing first over time. We further propose an approach for predictive compound slicing, which attempts to predict whether a node-based or time-based compound slice is more efficient. We evaluate our hybrid data structure on many real temporal network data sets and find that they achieve much faster slice times than existing data structures with only a modest increase in creation time and memory usage.

A Hybrid Adjacency and Time-Based Data Structure for Analysis of Temporal Networks

“…Preliminary results from this paper were presented in the conference publication [16]. This paper significantly extends those preliminary results in the following ways.…”

“…The main novel contribution in this paper is the development of a new dual linear regression approach for predictive compound slicing along with a thorough investigation of its benefits and drawbacks. We also consider a wider range of features to use in the predictive compound slices compared to [16]. We further quantify tradeoffs in the prediction accuracy, compound slice times, and creation times as we vary the size of the training data for our predictive model, whereas we focused only on the compound slice times in [16].…”

“…We also consider a wider range of features to use in the predictive compound slices compared to [16]. We further quantify tradeoffs in the prediction accuracy, compound slice times, and creation times as we vary the size of the training data for our predictive model, whereas we focused only on the compound slice times in [16]. Due to these novel contributions in predictive compound slicing, we have restructured this paper to have a separate section on predictive compound slicing with its own set of experiments.…”