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

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.

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

Hilsabeck

Arastuie

2022

Appl Netw Sci

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The shortest-path and bee colony optimization algorithms for traffic control at single intersection with NetworkX application

Jovanović,

Uzelac,

Kukić

et al. 2024

Demonstratio Mathematica

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In this article, we study the application of NetworkX, a Python library for dealing with traffic networks, to the problem of signal optimization at a single intersection. We use the shortest-path algorithms such as Bellman-Ford (Dynamic Programming), A star (A*), and Dijkstra’s algorithm to compute an optimal solution to the problem. We consider both undersaturated and oversaturated traffic conditions. The results show that we find optimal results with short Central Processor Unit (CPU) time using all the applied algorithms, where Dijkstra’s algorithm slightly outperformed others. Moreover, we show that bee colony optimization can find the optimal solution for all tested problems with different degrees of computational complexity for less CPU time, which is a new contribution to knowledge in this field.

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

Cited by 2 publications

References 26 publications