Graph metrics, such as the simple but popular vertex degree and others based on it, are well defined for static graphs. However, adapting static metrics for temporal graphs is still part of current research. In this paper, we propose a set of temporal extensions of four degree-dependent metrics, as well as aggregations like minimum, maximum, and average degree of (i) a vertex over a time interval and (ii) a graph at a specific point in time. We show why using the static degree can lead to wrong assumptions about the relevance of a vertex in a temporal graph and highlight the need to include time as a dimension in the metric. We propose a baseline algorithm to calculate the degree evolution of all vertices in a temporal graph and show its implementation in a distributed in-memory dataflow system. Using real-world and synthetic datasets containing up to 462 million vertices and 1.7 billion edges, we show the scalability of our algorithm on a distributed cluster achieving a speedup of around 12 on 16 machines.
%0 Conference Paper
%1 Rost2023-oi
%A Rost, Christopher
%A Gomez, Kevin
%A Christen, Peter
%A Rahm, Erhard
%D 2023
%I Gesellschaft für Informatik e.V.
%K
%T Evolution of degree metrics in large temporal graphs
%X Graph metrics, such as the simple but popular vertex degree and others based on it, are well defined for static graphs. However, adapting static metrics for temporal graphs is still part of current research. In this paper, we propose a set of temporal extensions of four degree-dependent metrics, as well as aggregations like minimum, maximum, and average degree of (i) a vertex over a time interval and (ii) a graph at a specific point in time. We show why using the static degree can lead to wrong assumptions about the relevance of a vertex in a temporal graph and highlight the need to include time as a dimension in the metric. We propose a baseline algorithm to calculate the degree evolution of all vertices in a temporal graph and show its implementation in a distributed in-memory dataflow system. Using real-world and synthetic datasets containing up to 462 million vertices and 1.7 billion edges, we show the scalability of our algorithm on a distributed cluster achieving a speedup of around 12 on 16 machines.
@inproceedings{Rost2023-oi,
abstract = {Graph metrics, such as the simple but popular vertex degree and others based on it, are well defined for static graphs. However, adapting static metrics for temporal graphs is still part of current research. In this paper, we propose a set of temporal extensions of four degree-dependent metrics, as well as aggregations like minimum, maximum, and average degree of (i) a vertex over a time interval and (ii) a graph at a specific point in time. We show why using the static degree can lead to wrong assumptions about the relevance of a vertex in a temporal graph and highlight the need to include time as a dimension in the metric. We propose a baseline algorithm to calculate the degree evolution of all vertices in a temporal graph and show its implementation in a distributed in-memory dataflow system. Using real-world and synthetic datasets containing up to 462 million vertices and 1.7 billion edges, we show the scalability of our algorithm on a distributed cluster achieving a speedup of around 12 on 16 machines.},
added-at = {2024-09-10T10:41:24.000+0200},
author = {Rost, Christopher and Gomez, Kevin and Christen, Peter and Rahm, Erhard},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/23b3e3409c8cec49fb5d2267d7b858d6b/scadsfct},
interhash = {b04f64149fb9b652ac86fa1738b39887},
intrahash = {3b3e3409c8cec49fb5d2267d7b858d6b},
keywords = {},
publisher = {Gesellschaft f{\"u}r Informatik e.V.},
timestamp = {2024-09-10T10:47:32.000+0200},
title = {Evolution of degree metrics in large temporal graphs},
year = 2023
}
