TY - GEN
T1 - Inside the atoms
T2 - 17th IEEE International Conference on Data Mining Workshops, ICDMW 2017
AU - Tong, Hanghang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/15
Y1 - 2017/12/15
N2 - Networks (i.e., graphs) appears in many high-impact applications. Often these networks are collected from different sources, at different times, at different granularities. In this talk, I will present our recent work on mining such multiple networks. First, we will present several new data models, whose key idea is to leverage networks as context to connect different data sets or different data mining models, including a network of networks (NoN) model, a network of co-evolving time series (NoT) model and a network of regression model. Second, we will present some algorithmic examples on how to perform mining with such new models where the key idea is to leverage the contextual network as an effective regularizer during the mining process, including ranking, imputation, prediction and inference. Finally, we will demonstrate the effectiveness of our new models and algorithms in some applications, including bioinformatics, sensor networks, critical infrastructure networks and scholarly data mining.
AB - Networks (i.e., graphs) appears in many high-impact applications. Often these networks are collected from different sources, at different times, at different granularities. In this talk, I will present our recent work on mining such multiple networks. First, we will present several new data models, whose key idea is to leverage networks as context to connect different data sets or different data mining models, including a network of networks (NoN) model, a network of co-evolving time series (NoT) model and a network of regression model. Second, we will present some algorithmic examples on how to perform mining with such new models where the key idea is to leverage the contextual network as an effective regularizer during the mining process, including ranking, imputation, prediction and inference. Finally, we will demonstrate the effectiveness of our new models and algorithms in some applications, including bioinformatics, sensor networks, critical infrastructure networks and scholarly data mining.
KW - Graph Mining
KW - Network of Networks
UR - http://www.scopus.com/inward/record.url?scp=85044086390&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044086390&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2017.138
DO - 10.1109/ICDMW.2017.138
M3 - Conference contribution
AN - SCOPUS:85044086390
T3 - IEEE International Conference on Data Mining Workshops, ICDMW
SP - 983
BT - Proceeding - 17th IEEE International Conference on Data Mining Workshops, ICDMW 2017
A2 - Gottumukkala, Raju
A2 - Karypis, George
A2 - Raghavan, Vijay
A2 - Wu, Xindong
A2 - Miele, Lucio
A2 - Aluru, Srinivas
A2 - Ning, Xia
A2 - Dong, Guozhu
PB - IEEE Computer Society
Y2 - 18 November 2017 through 21 November 2017
ER -