TY - GEN
T1 - MC-Explorer
T2 - 36th IEEE International Conference on Data Engineering, ICDE 2020
AU - Li, Boxuan
AU - Cheng, Reynold
AU - Hu, Jiafeng
AU - Fang, Yixiang
AU - Ou, Min
AU - Luo, Ruibang
AU - Chang, Kevin Chen Chuan
AU - Lin, Xuemin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - Large networks with labeled nodes are prevalent in various applications, such as biological graphs, social networks, and e-commerce graphs. To extract insight from this rich information source, we propose MC-Explorer, which is an advanced analysis and visualization system. A highlight of MC-Explorer is its ability to discover motif-cliques from a graph with labeled nodes. A motif, such as a 3-node triangle, is a fundamental building block of a graph. A motif-clique is a "complete" subgraph in a network with respect to a desired higher-order connection pattern. For example, on a large biological graph, we found out some motif-cliques, which disclose new side effects of a drug, and potential drugs for healing diseases. MC-Explorer includes online and interactive facilities for exploring a large labeled network through the use of motif-cliques. We will demonstrate how MC-Explorer can facilitate the analysis and visualization of a labeled biological network.An online demo video of MC-Explorer can be accessed from https://www.dropbox.com/s/vkalumc28wqp8yl/demo.mov.
AB - Large networks with labeled nodes are prevalent in various applications, such as biological graphs, social networks, and e-commerce graphs. To extract insight from this rich information source, we propose MC-Explorer, which is an advanced analysis and visualization system. A highlight of MC-Explorer is its ability to discover motif-cliques from a graph with labeled nodes. A motif, such as a 3-node triangle, is a fundamental building block of a graph. A motif-clique is a "complete" subgraph in a network with respect to a desired higher-order connection pattern. For example, on a large biological graph, we found out some motif-cliques, which disclose new side effects of a drug, and potential drugs for healing diseases. MC-Explorer includes online and interactive facilities for exploring a large labeled network through the use of motif-cliques. We will demonstrate how MC-Explorer can facilitate the analysis and visualization of a labeled biological network.An online demo video of MC-Explorer can be accessed from https://www.dropbox.com/s/vkalumc28wqp8yl/demo.mov.
UR - http://www.scopus.com/inward/record.url?scp=85085859100&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085859100&partnerID=8YFLogxK
U2 - 10.1109/ICDE48307.2020.00154
DO - 10.1109/ICDE48307.2020.00154
M3 - Conference contribution
AN - SCOPUS:85085859100
T3 - Proceedings - International Conference on Data Engineering
SP - 1722
EP - 1725
BT - Proceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
PB - IEEE Computer Society
Y2 - 20 April 2020 through 24 April 2020
ER -