TY - GEN
T1 - A Distributed Algorithm for Force Directed Edge Bundling
AU - Tuyishime, Yves
AU - Pan, Yu
AU - Yu, Hongfeng
N1 - Funding Information:
This research has been sponsored by the National Science Foundation through the grant IIS-1652846.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - Existing edge bundling algorithms typically require the global information structure of a graph. Therefore, with a simple division of the edges of a graph, it is challenging to conduct edge bundling in a distributed environment and achieve scalable performance. We select a representative edge bundling algorithm, Force-Directed Edge Bundling (FDEB), and parallelize it in a distributed environment. Particularly, to address the difficulties of partitioning and distributions of a large graph among processors, we first create a high dimensional space to represent the data distribution of a graph in FDEB. Second, we map each edge as a data point in this high dimensional space, and then partition and distribute the point cloud among processors. In this way, we can significantly reduce the data communication across processors, and ensure each processor assigned with a similar workload.
AB - Existing edge bundling algorithms typically require the global information structure of a graph. Therefore, with a simple division of the edges of a graph, it is challenging to conduct edge bundling in a distributed environment and achieve scalable performance. We select a representative edge bundling algorithm, Force-Directed Edge Bundling (FDEB), and parallelize it in a distributed environment. Particularly, to address the difficulties of partitioning and distributions of a large graph among processors, we first create a high dimensional space to represent the data distribution of a graph in FDEB. Second, we map each edge as a data point in this high dimensional space, and then partition and distribute the point cloud among processors. In this way, we can significantly reduce the data communication across processors, and ensure each processor assigned with a similar workload.
KW - Graph drawings
KW - Human-centered computing
KW - Visu-Alization techniques
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85099590970&partnerID=8YFLogxK
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U2 - 10.1109/LDAV51489.2020.00013
DO - 10.1109/LDAV51489.2020.00013
M3 - Conference contribution
AN - SCOPUS:85099590970
T3 - Proceedings - 2020 IEEE 10th Symposium on Large Data Analysis and Visualization, LDAV 2020
SP - 53
EP - 54
BT - Proceedings - 2020 IEEE 10th Symposium on Large Data Analysis and Visualization, LDAV 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2020
Y2 - 25 October 2020
ER -