Maximizing edge connectivity in graph partitioning using hotspots
Graphs have long been used to model relationships between entities. For some applications, a single graph is sufficient; for other problems, a collection of graphs may be more appropriate to represent the underlying data. Many contemporary problem domains, for which graphs are an ideal data mod...
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| Format: | Article |
| Language: | English |
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Growing Science
2025-01-01
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| Series: | International Journal of Data and Network Science |
| Online Access: | https://www.growingscience.com/ijds/Vol9/ijdns_2025_8.pdf |
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| author | Isam A. Alobaidi Hiba G. Fareed Jennifer L. Leopold Andrea E. Smit |
| author_facet | Isam A. Alobaidi Hiba G. Fareed Jennifer L. Leopold Andrea E. Smit |
| author_sort | Isam A. Alobaidi |
| collection | DOAJ |
| description |
Graphs have long been used to model relationships between entities. For some applications, a single graph is sufficient; for other problems, a collection of graphs may be more appropriate to represent the underlying data. Many contemporary problem domains, for which graphs are an ideal data model, contain an enormous amount of data (e.g., social networks). Hence, researchers frequently employ parallelized or distributed processing. The graph data must first be partitioned and assigned to the multiple processors in a way that the workload is balanced and inter-processor communication is minimized. The latter problem may be complicated by the existence of edges between vertices in a graph that have been assigned to different processors. Herein we introduce a strategy that combines vocabulary-based summarization of graphs (VoG) and detection of hotspots (i.e., vertices of high degree) to determine how a single undirected graph should be partitioned to optimize multi-processor load balancing and minimize the number of edges that exist between the partitioned subgraphs. We benchmark our method against another well-known partitioning algorithm (METIS) to demonstrate the benefits of our approach. |
| format | Article |
| id | doaj-art-e8ebef2a6d9341148bf7df792bdc4de6 |
| institution | OA Journals |
| issn | 2561-8148 2561-8156 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | International Journal of Data and Network Science |
| spelling | doaj-art-e8ebef2a6d9341148bf7df792bdc4de62025-08-20T02:03:23ZengGrowing ScienceInternational Journal of Data and Network Science2561-81482561-81562025-01-019338539410.5267/j.ijdns.2025.4.002Maximizing edge connectivity in graph partitioning using hotspotsIsam A. AlobaidiHiba G. FareedJennifer L. LeopoldAndrea E. Smit Graphs have long been used to model relationships between entities. For some applications, a single graph is sufficient; for other problems, a collection of graphs may be more appropriate to represent the underlying data. Many contemporary problem domains, for which graphs are an ideal data model, contain an enormous amount of data (e.g., social networks). Hence, researchers frequently employ parallelized or distributed processing. The graph data must first be partitioned and assigned to the multiple processors in a way that the workload is balanced and inter-processor communication is minimized. The latter problem may be complicated by the existence of edges between vertices in a graph that have been assigned to different processors. Herein we introduce a strategy that combines vocabulary-based summarization of graphs (VoG) and detection of hotspots (i.e., vertices of high degree) to determine how a single undirected graph should be partitioned to optimize multi-processor load balancing and minimize the number of edges that exist between the partitioned subgraphs. We benchmark our method against another well-known partitioning algorithm (METIS) to demonstrate the benefits of our approach.https://www.growingscience.com/ijds/Vol9/ijdns_2025_8.pdf |
| spellingShingle | Isam A. Alobaidi Hiba G. Fareed Jennifer L. Leopold Andrea E. Smit Maximizing edge connectivity in graph partitioning using hotspots International Journal of Data and Network Science |
| title | Maximizing edge connectivity in graph partitioning using hotspots |
| title_full | Maximizing edge connectivity in graph partitioning using hotspots |
| title_fullStr | Maximizing edge connectivity in graph partitioning using hotspots |
| title_full_unstemmed | Maximizing edge connectivity in graph partitioning using hotspots |
| title_short | Maximizing edge connectivity in graph partitioning using hotspots |
| title_sort | maximizing edge connectivity in graph partitioning using hotspots |
| url | https://www.growingscience.com/ijds/Vol9/ijdns_2025_8.pdf |
| work_keys_str_mv | AT isamaalobaidi maximizingedgeconnectivityingraphpartitioningusinghotspots AT hibagfareed maximizingedgeconnectivityingraphpartitioningusinghotspots AT jenniferlleopold maximizingedgeconnectivityingraphpartitioningusinghotspots AT andreaesmit maximizingedgeconnectivityingraphpartitioningusinghotspots |