As the scale of cluster computing grows, it is becoming hard for long-running applications to complete without facing failures on large-scale clusters. To address this issue, chec...
Semi-supervised learning methods construct classifiers using both labeled and unlabeled training data samples. While unlabeled data samples can help to improve the accuracy of trai...
: We initiate the study of local, sublinear time algorithms for finding vertices with extreme topological properties -- such as high degree or clustering coefficient -- in large so...
In this paper, we present a stream-based mining algorithm for online anomaly prediction. Many real-world applications such as data stream analysis requires continuous cluster opera...
: Recent work on social networks has tackled the measurement and optimization of these networks’ robustness and resilience to both failures and attacks. Different metrics have be...