TY - GEN
T1 - Webshapes
T2 - 13th ACM International Conference on Web Search and Data Mining, WSDM 2020
AU - Jin, Shengmin
AU - Wituszynski, Richard
AU - Caiello-Gingold, Max
AU - Zafarani, Reza
N1 - Publisher Copyright:
© 2020 Association for Computing Machinery.
PY - 2020/1/20
Y1 - 2020/1/20
N2 - Network visualization has played a critical role in graph analysis, as it not only presents a big picture of a network but also helps reveal the structural information of a network. The most popular visual representation of networks is the node-link diagram. However, visualizing a large network with the node-link diagram can be challenging due to the difficulty in obtaining an optimal graph layout. To address this challenge, a recent advancement in network representation: network shape, allows one to compactly represent a network and its subgraphs with the distribution of their embeddings. Inspired by this research, we have designed a web platform WebShapes that enables researchers and practitioners to visualize their network data as customized 3D shapes (http://b.link/webshapes). Furthermore, we provide a case study on real-world networks to explore the sensitivity of network shapes to different graph sampling, embedding, and fitting methods, and we show examples of understanding networks through their network shapes.
AB - Network visualization has played a critical role in graph analysis, as it not only presents a big picture of a network but also helps reveal the structural information of a network. The most popular visual representation of networks is the node-link diagram. However, visualizing a large network with the node-link diagram can be challenging due to the difficulty in obtaining an optimal graph layout. To address this challenge, a recent advancement in network representation: network shape, allows one to compactly represent a network and its subgraphs with the distribution of their embeddings. Inspired by this research, we have designed a web platform WebShapes that enables researchers and practitioners to visualize their network data as customized 3D shapes (http://b.link/webshapes). Furthermore, we provide a case study on real-world networks to explore the sensitivity of network shapes to different graph sampling, embedding, and fitting methods, and we show examples of understanding networks through their network shapes.
KW - Graph representation
KW - Network visualization
UR - http://www.scopus.com/inward/record.url?scp=85079546320&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079546320&partnerID=8YFLogxK
U2 - 10.1145/3336191.3371867
DO - 10.1145/3336191.3371867
M3 - Conference contribution
AN - SCOPUS:85079546320
T3 - WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining
SP - 837
EP - 840
BT - WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining
PB - Association for Computing Machinery, Inc
Y2 - 3 February 2020 through 7 February 2020
ER -