We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical re...
Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, ...
In this paper, we propose a probabilistic framework targeting three important issues in the computation of quality and trust in decentralized systems. Specifically, our approach a...
Operational network data, management data such as customer care call logs and equipment system logs, is a very important source of information for network operators to detect prob...
Chi-Yao Hong, Matthew Caesar, Nick G. Duffield, Ji...
Levelwise algorithms (e.g., the Apriori algorithm) have been proved eective for association rule mining from sparse data. However, in many practical applications, the computation ...
The dQUOB system satis es client need for speci c information from high-volume data streams. The data streams we speak of are the ow of data existing during large-scale visualizat...