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 - Acknowledgement. Jiafeng Hu and Reynold Cheng were supported by the Research Grants Council of Hong Kong (RGC Projects HKU 17229116, 106150091, and 17205115), and the University of Hong Kong (Projects 104004572, 102009508, and 104004129), and the Innovation and Technology Commission of Hong Kong (ITF project MRP/029/18). Kevin Chen-Chuan Chang was supported by National Science Foundation IIS 16-19302 and IIS 16-33755, UIUC OVCR CCIL Planning Grant 434S34, and UIUC CSBS Small Grant 434C8U.
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 - https://www.scopus.com/pages/publications/85085859100
UR - https://www.scopus.com/pages/publications/85085859100#tab=citedBy
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 -