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Amyra Meidiana
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2020 – today
- 2024
- [j5]Amyra Meidiana, Seok-Hee Hong, Peter Eades, Daniel A. Keim:
Automorphism Faithfulness Metrics for Symmetric Graph Drawings. IEEE Trans. Vis. Comput. Graph. 30(7): 3241-3255 (2024) - [j4]Amyra Meidiana, Seok-Hee Hong, Shijun Cai, Marnijati Torkel, Peter Eades:
SubLinearForce: Fully Sublinear-Time Force Computation for Large Complex Graph Drawing. IEEE Trans. Vis. Comput. Graph. 30(7): 3386-3399 (2024) - [c16]Amyra Meidiana, Seok-Hee Hong, Yongcheng Jing:
Connectivity-Faithful Graph Drawing. GD 2024: 17:1-17:17 - [c15]Shijun Cai, Seok-Hee Hong, Amyra Meidiana, Peter Eades, Daniel A. Keim:
Cluster-Faithful Graph Visualization: New Metrics and Algorithms. PacificVis 2024: 192-201 - 2023
- [c14]Seok-Hee Hong, Juan Pablo Bonilla Ataides, Rowena Kok, Amyra Meidiana, Kunsoo Park:
New Seeding Strategies for the Influence Maximization Problem. COMPLEX NETWORKS (2) 2023: 289-299 - 2022
- [c13]Seok-Hee Hong, Amyra Meidiana, James Wood, Juan Pablo Ataides, Peter Eades, Kunsoo Park:
dGG, dRNG, DSC: New Degree-based Shape-based Faithfulness Metrics for Large and Complex Graph Visualization. PacificVis 2022: 51-60 - [c12]Amyra Meidiana, Seok-Hee Hong, Peter Eades:
Shape-Faithful Graph Drawings. GD 2022: 93-108 - [c11]Peter Eades, Seok-Hee Hong, Martin McGrane, Amyra Meidiana:
GDot-i: Interactive System for Dot Paintings of Graphs. EuroVis (Posters) 2022: 5-6 - [c10]Shijun Cai, Amyra Meidiana, Seok-Hee Hong:
DNC: Dynamic Neighborhood Change Faithfulness Metrics. EuroVis (Short Papers) 2022: 49-53 - [c9]Amyra Meidiana, Seok-Hee Hong, Yanyi Pu, Justin Lee, Peter Eades, Jinwook Seo:
DSS: Drawing Dynamic Graphs with Spectral Sparsification. EuroVis (Short Papers) 2022: 55-59 - [i4]Amyra Meidiana, Seok-Hee Hong, Peter Eades:
Shape-Faithful Graph Drawings. CoRR abs/2208.14095 (2022) - 2021
- [j3]Jingming Hu, Tuan Tran Chu, Seok-Hee Hong, Jialu Chen, Amyra Meidiana, Marnijati Torkel, Peter Eades, Kwan-Liu Ma:
BC tree-based spectral sampling for big complex network visualization. Appl. Netw. Sci. 6(1): 60 (2021) - [c8]Amyra Meidiana, Seok-Hee Hong, Shijun Cai, Marnijati Torkel, Peter Eades:
Sublinear-Time Attraction Force Computation for Large Complex Graph Drawing. PacificVis 2021: 146-150 - [c7]Amyra Meidiana, James Wood, Seok-Hee Hong:
Sublinear-time Algorithms for Stress Minimization in Graph Drawing. PacificVis 2021: 166-175 - 2020
- [j2]Amyra Meidiana, Seok-Hee Hong, Marnijati Torkel, Shijun Cai, Peter Eades:
Sublinear Time Force Computation for Big Complex Network Visualization. Comput. Graph. Forum 39(3): 579-591 (2020) - [c6]Amyra Meidiana, Seok-Hee Hong, Peter Eades, Daniel A. Keim:
Quality Metrics for Symmetric Graph Drawings. PacificVis 2020: 11-15 - [c5]Amyra Meidiana, Seok-Hee Hong, Peter Eades:
New Quality Metrics for Dynamic Graph Drawing. GD 2020: 450-465 - [i3]Amyra Meidiana, Seok-Hee Hong, Peter Eades:
New Quality Metrics for Dynamic Graph Drawing. CoRR abs/2008.07764 (2020)
2010 – 2019
- 2019
- [c4]Amyra Meidiana, Seok-Hee Hong, Peter Eades, Daniel A. Keim:
A Quality Metric for Visualization of Clusters in Graphs. GD 2019: 125-138 - [c3]Amyra Meidiana, Seok-Hee Hong, Jiajun Huang, Peter Eades, Kwan-Liu Ma:
Topology-Based Spectral Sparsification. LDAV 2019: 73-82 - [i2]Amyra Meidiana, Seok-Hee Hong, Peter Eades, Daniel A. Keim:
A Quality Metric for Visualization of Clusters in Graphs. CoRR abs/1908.07792 (2019) - [i1]Amyra Meidiana, Seok-Hee Hong, Peter Eades, Daniel A. Keim:
A Quality Metric for Symmetric Graph Drawings. CoRR abs/1910.04974 (2019) - 2018
- [c2]Seok-Hee Hong, Quan Hoang Nguyen, Amyra Meidiana, Jiaxi Li, Peter Eades:
BC Tree-Based Proxy Graphs for Visualization of Big Graphs. PacificVis 2018: 11-20 - 2017
- [j1]Quan Hoang Nguyen, Seok-Hee Hong, Peter Eades, Amyra Meidiana:
Proxy Graph: Visual Quality Metrics of Big Graph Sampling. IEEE Trans. Vis. Comput. Graph. 23(6): 1600-1611 (2017) - 2015
- [c1]Amyra Meidiana, Seok-Hee Hong:
MultiStory: Visual analytics of dynamic multi-relational networks. PacificVis 2015: 75-79
Coauthor Index
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last updated on 2024-10-30 20:32 CET by the dblp team
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