Graph Computing Systems and Partitioning Techniques: A Survey
<h3>Abstract</h3><p dir="ltr">Graphs are a tremendously suitable data representations that model the relationships of entities in many application domains, such as recommendation systems, machine learning, computational biology, social network analysis, and other applicat...
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2022
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| _version_ | 1864513506188984320 |
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| author | Tewodros Alemu Ayall (19273717) |
| author2 | Huawen Liu (840748) Changjun Zhou (451444) Abegaz Mohammed Seid (19170901) Fantahun Bogale Gereme (19517581) Hayla Nahom Abishu (19517584) Yasin Habtamu Yacob (19517587) |
| author2_role | author author author author author author |
| author_facet | Tewodros Alemu Ayall (19273717) Huawen Liu (840748) Changjun Zhou (451444) Abegaz Mohammed Seid (19170901) Fantahun Bogale Gereme (19517581) Hayla Nahom Abishu (19517584) Yasin Habtamu Yacob (19517587) |
| author_role | author |
| dc.creator.none.fl_str_mv | Tewodros Alemu Ayall (19273717) Huawen Liu (840748) Changjun Zhou (451444) Abegaz Mohammed Seid (19170901) Fantahun Bogale Gereme (19517581) Hayla Nahom Abishu (19517584) Yasin Habtamu Yacob (19517587) |
| dc.date.none.fl_str_mv | 2022-11-22T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2022.3219422 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Graph_Computing_Systems_and_Partitioning_Techniques_A_Survey/26889376 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Computer vision and multimedia computation Data management and data science Distributed computing graph computing systems graph partitioning graph processing systems graph databases graph algorithm large-scale graph analysis Partitioning algorithms Computational modeling Global Positioning System Classification algorithms Social networking (online) Heuristic algorithms Data models Graph theory |
| dc.title.none.fl_str_mv | Graph Computing Systems and Partitioning Techniques: A Survey |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Abstract</h3><p dir="ltr">Graphs are a tremendously suitable data representations that model the relationships of entities in many application domains, such as recommendation systems, machine learning, computational biology, social network analysis, and other application domains. Graphs with many vertices and edges have become quite prevalent in recent years. Therefore, graph computing systems with integrated various graph partitioning techniques have been envisioned as a promising paradigm to handle large-scale graph analytics in these application domains. However, scalable processing of large-scale graphs is challenging due to their high volume and inherent irregular structure of the real-world graphs. Hence, industry and academia have been recently proposing graph partitioning and computing systems to process and analyze large-scale graphs efficiently. The graph partitioning and computing systems have been designed to improve scalability issues and reduce processing time complexity. This paper presents an overview, classification, and investigation of the most popular graph partitioning and computing systems. The various methods and approaches of graph partitioning and diverse categories of graph computing systems are presented. Finally, we discuss main challenges and future research directions in graph partitioning and computing systems.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3219422" target="_blank">https://dx.doi.org/10.1109/access.2022.3219422</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_76aa3f14820ac32a91f6276e5f9cc409 |
| identifier_str_mv | 10.1109/access.2022.3219422 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26889376 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Graph Computing Systems and Partitioning Techniques: A SurveyTewodros Alemu Ayall (19273717)Huawen Liu (840748)Changjun Zhou (451444)Abegaz Mohammed Seid (19170901)Fantahun Bogale Gereme (19517581)Hayla Nahom Abishu (19517584)Yasin Habtamu Yacob (19517587)Information and computing sciencesComputer vision and multimedia computationData management and data scienceDistributed computinggraph computing systemsgraph partitioninggraph processing systemsgraph databasesgraph algorithmlarge-scale graph analysisPartitioning algorithmsComputational modelingGlobal Positioning SystemClassification algorithmsSocial networking (online)Heuristic algorithmsData modelsGraph theory<h3>Abstract</h3><p dir="ltr">Graphs are a tremendously suitable data representations that model the relationships of entities in many application domains, such as recommendation systems, machine learning, computational biology, social network analysis, and other application domains. Graphs with many vertices and edges have become quite prevalent in recent years. Therefore, graph computing systems with integrated various graph partitioning techniques have been envisioned as a promising paradigm to handle large-scale graph analytics in these application domains. However, scalable processing of large-scale graphs is challenging due to their high volume and inherent irregular structure of the real-world graphs. Hence, industry and academia have been recently proposing graph partitioning and computing systems to process and analyze large-scale graphs efficiently. The graph partitioning and computing systems have been designed to improve scalability issues and reduce processing time complexity. This paper presents an overview, classification, and investigation of the most popular graph partitioning and computing systems. The various methods and approaches of graph partitioning and diverse categories of graph computing systems are presented. Finally, we discuss main challenges and future research directions in graph partitioning and computing systems.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3219422" target="_blank">https://dx.doi.org/10.1109/access.2022.3219422</a></p>2022-11-22T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2022.3219422https://figshare.com/articles/journal_contribution/Graph_Computing_Systems_and_Partitioning_Techniques_A_Survey/26889376CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/268893762022-11-22T15:00:00Z |
| spellingShingle | Graph Computing Systems and Partitioning Techniques: A Survey Tewodros Alemu Ayall (19273717) Information and computing sciences Computer vision and multimedia computation Data management and data science Distributed computing graph computing systems graph partitioning graph processing systems graph databases graph algorithm large-scale graph analysis Partitioning algorithms Computational modeling Global Positioning System Classification algorithms Social networking (online) Heuristic algorithms Data models Graph theory |
| status_str | publishedVersion |
| title | Graph Computing Systems and Partitioning Techniques: A Survey |
| title_full | Graph Computing Systems and Partitioning Techniques: A Survey |
| title_fullStr | Graph Computing Systems and Partitioning Techniques: A Survey |
| title_full_unstemmed | Graph Computing Systems and Partitioning Techniques: A Survey |
| title_short | Graph Computing Systems and Partitioning Techniques: A Survey |
| title_sort | Graph Computing Systems and Partitioning Techniques: A Survey |
| topic | Information and computing sciences Computer vision and multimedia computation Data management and data science Distributed computing graph computing systems graph partitioning graph processing systems graph databases graph algorithm large-scale graph analysis Partitioning algorithms Computational modeling Global Positioning System Classification algorithms Social networking (online) Heuristic algorithms Data models Graph theory |