TY - GEN
T1 - Property Graphs in Arachne
AU - Rodriguez, Oliver Alvarado
AU - Buschmann, Fernando Vera
AU - Du, Zhihui
AU - Bader, David A.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Analyzing large-scale graphs poses challenges due to their increasing size and the demand for interactive and user-friendly analytics tools. These graphs arise from various domains, including cybersecurity, social sciences, health sciences, and network sciences, where networks can represent interactions between humans, neurons in the brain, or malicious flows in a network. Exploring these large graphs is crucial for revealing hidden structures and metrics that are not easily computable without parallel computing. Currently, Python users can leverage the open-source Arkouda framework to efficiently execute Pandas and NumPy-related tasks on thousands of cores. To address large-scale graph analysis, Arachne, an extension to Arkouda, enables easy transformation of Arkouda dataframes into graphs. This paper proposes and evaluates three distributable data structures for property graphs, implemented in Chapel, that are integrated into Arachne. Enriching Arachne with support for property graphs will empower data scientists to extend their analysis to new problem domains. Property graphs present additional complexities, requiring efficient storage for extra information on vertices and edges, such as labels, relationships, and properties.
AB - Analyzing large-scale graphs poses challenges due to their increasing size and the demand for interactive and user-friendly analytics tools. These graphs arise from various domains, including cybersecurity, social sciences, health sciences, and network sciences, where networks can represent interactions between humans, neurons in the brain, or malicious flows in a network. Exploring these large graphs is crucial for revealing hidden structures and metrics that are not easily computable without parallel computing. Currently, Python users can leverage the open-source Arkouda framework to efficiently execute Pandas and NumPy-related tasks on thousands of cores. To address large-scale graph analysis, Arachne, an extension to Arkouda, enables easy transformation of Arkouda dataframes into graphs. This paper proposes and evaluates three distributable data structures for property graphs, implemented in Chapel, that are integrated into Arachne. Enriching Arachne with support for property graphs will empower data scientists to extend their analysis to new problem domains. Property graphs present additional complexities, requiring efficient storage for extra information on vertices and edges, such as labels, relationships, and properties.
KW - distributed-memory
KW - graph analytics
KW - parallel algorithms
KW - property graphs
UR - http://www.scopus.com/inward/record.url?scp=85182602514&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85182602514&partnerID=8YFLogxK
U2 - 10.1109/HPEC58863.2023.10363498
DO - 10.1109/HPEC58863.2023.10363498
M3 - Conference contribution
AN - SCOPUS:85182602514
T3 - 2023 IEEE High Performance Extreme Computing Conference, HPEC 2023
BT - 2023 IEEE High Performance Extreme Computing Conference, HPEC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE High Performance Extreme Computing Conference, HPEC 2023
Y2 - 25 September 2023 through 29 September 2023
ER -