Knowledge discovery on social network data can uncover latent social trends and produce valuable findings that benefit the welfare of the general public. A growing amount of resea...
This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting ...
Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zha...
Dyadic data matrices, such as co-occurrence matrix, rating matrix, and proximity matrix, arise frequently in various important applications. A fundamental problem in dyadic data a...
—Due to the open nature of a sensor network, it is relatively easy for an adversary to eavesdrop and trace packet movement in the network in order to capture the receiver physica...
Automatically extracting semantic content from audio streams can be helpful in many multimedia applications. Motivated by the known limitations of traditional supervised approache...