![](bg8.png)
REFERENCES
Abello, J., van Ham, F., and Krishnan, N. (2006). Ask-
graphview: A large scale graph visualization system.
IEEE Trans. Vis. Comput. Graph., 12(5):669–676.
Bastian, M., Heymann, S., and Jacomy, M. (2009). Gephi:
An open source software for exploring and manipulat-
ing networks. In Proc. of the Int. AAAI Conf. on Web
and Social Media, volume 3.
Batagelj, V., Didimo, W., Liotta, G., Palladino, P., and Pa-
trignani, M. (2010). Visual analysis of large graphs
using (X, Y)-clustering and hybrid visualizations. In
IEEE Pacific Visualization Symposium, pages 209–
216.
Brewer, C. and Harrower, M. (2001). Colorbrewer 2.0.
https://colorbrewer2.org/.
Brinkmann, G. G., Rietveld, K. F., and Takes, F. W. (2017).
Exploiting GPUs for fast force-directed visualization
of large-scale networks. In 2017 46th Int. Conf. on
Parallel Processing (ICPP), pages 382–391. IEEE.
Cormode, G. and Muthukrishnan, S. (2005). An improved
data stream summary: the count-min sketch and its
applications. Journal of Algorithms, 55(1):58–75.
Elmqvist, N., Do, T.-N., Goodell, H., Henry, N., and Fekete,
J.-D. (2008). Zame: Interactive large-scale graph vi-
sualization. In 2008 IEEE Pacific visualization Symp.,
pages 215–222. IEEE.
Frishman, Y. and Tal, A. (2007). Multi-level graph layout
on the GPU. IEEE Transactions on Visualization and
Computer Graphics, 13(6):1310–1319.
Gallo, G., Grigoriadis, M. D., and Tarjan, R. E. (1989). A
fast parametric maximum flow algorithm and applica-
tions. SIAM Journal on Computing, 18(1):30–55.
Gibson, D., Kumar, R., and Tomkins, A. (2005). Discover-
ing large dense subgraphs in massive graphs. In Int.
Conf. on Very large data bases, pages 721–732. Cite-
seer.
Hachul, S. and J
¨
unger, M. (2004). Drawing large graphs
with a potential-field-based multilevel algorithm. In
Graph Drawing, pages 285–295. Springer.
Hollocou, A., Maudet, J., Bonald, T., and Lelarge, M.
(2017). A linear streaming algorithm for commu-
nity detection in very large networks. arXiv preprint
arXiv:1703.02955.
Hu, Y., Gansner, E. R., and Kobourov, S. G. (2010). Visu-
alizing graphs and clusters as maps. IEEE Computer
Graphics and Applications, 30(6):54–66.
Huang, X. and Huang, C. (2018). NGD: filtering graphs for
visual analysis. IEEE Trans. Big Data, 4(3):381–395.
Jacomy, M., Venturini, T., Heymann, S., and Bastian, M.
(2014). Forceatlas2, a continuous graph layout algo-
rithm for handy network visualization designed for the
gephi software. PloS one, 9(6):e98679.
Kobourov, S. G. (2012). Spring embedders and force
directed graph drawing algorithms. arXiv preprint
arXiv:1201.3011.
Kwon, O., Crnovrsanin, T., and Ma, K. (2018). What would
a graph look like in this layout? A machine learning
approach to large graph visualization. IEEE Trans.
Vis. Comput. Graph., 24(1):478–488.
Leskovec, J. and Faloutsos, C. (2006). Sampling from large
graphs. In ACM SIGKDD Int. Conf. on Knowledge
discovery and data mining, pages 631–636.
Leskovec, J. and Krevl, A. (2014a). Snap datasets: Stanford
large network dataset collection.
Leskovec, J. and Krevl, A. (2014b). SNAP Datasets: Stan-
ford large network dataset collection. http://snap.
stanford.edu/data.
Mondal, D. and Nachmanson, L. (2018). A new approach to
GraphMaps, a system browsing large graphs as inter-
active maps. In Proceedings of the 13th International
Joint Conference on Computer Vision, Imaging and
Computer Graphics Theory and Applications (VISI-
GRAPP), pages 108–119. SciTePress.
Moradi, E., Fazlali, M., and Malazi, H. T. (2015). Fast par-
allel community detection algorithm based on modu-
larity. In Int. Symp. on Comp. Architecture and Digital
Systems (CADS), pages 1–4. IEEE.
Nachmanson, L., Prutkin, R., Lee, B., Riche, N. H., Hol-
royd, A. E., and Chen, X. (2015). Graphmaps: Brows-
ing large graphs as interactive maps. In Graph Draw-
ing and Network Visualization (GD), volume 9411 of
LNCS, pages 3–15. Springer.
Newman, M. E. (2004). Fast algorithm for detecting com-
munity structure in networks. Physical review E,
69(6):066133.
Newman, M. E. (2006). Modularity and community struc-
ture in networks. Proc. of the national academy of
sciences, 103(23):8577–8582.
Perrot, A. and Auber, D. (2018). Cornac: Tackling huge
graph visualization with big data infrastructure. IEEE
Transactions on Big Data, 6(1):80–92.
Riondato, M., Garc
´
ıa-Soriano, D., and Bonchi, F. (2017).
Graph summarization with quality guarantees. Data
mining and knowledge discovery, 31(2):314–349.
Rossi, R. A. and Ahmed, N. K. (2015). The network data
repository with interactive graph analytics and visual-
ization. In AAAI Conf. on Artificial Intelligence, pages
4292–4293.
Shi, X., Zheng, Z., Zhou, Y., Jin, H., He, L., Liu, B., and
Hua, Q.-S. (2018). Graph processing on GPUs: A
survey. ACM Computing Surveys, 50(6):1–35.
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., and Su, Z.
(2008). Arnetminer: extraction and mining of aca-
demic social networks. In ACM SIGKDD Int. Conf.
on Knowledge discovery and data mining, pages 990–
998.
Von Landesberger, T., Kuijper, A., Schreck, T., Kohlham-
mer, J., van Wijk, J. J., Fekete, J.-D., and Fellner,
D. W. (2011). Visual analysis of large graphs: state-
of-the-art and future research challenges. In Computer
graphics forum, volume 30, pages 1719–1749. Wiley
Online Library.
Walshaw, C. (2000). A multilevel algorithm for force-
directed graph drawing. In Graph Drawing, pages
171–182. Springer.
Yoghourdjian, V., Dwyer, T., Klein, K., Marriott, K., and
Wybrow, M. (2018). Graph thumbnails: Identifying
and comparing multiple graphs at a glance. IEEE
Trans. Vis. Comput. Graph., 24(12):3081–3095.
IVAPP 2023 - 14th International Conference on Information Visualization Theory and Applications
202