1 code implementation • 31 Dec 2022 • Meng-Chieh Lee, Shubhranshu Shekhar, Jaemin Yoo, Christos Faloutsos
Given a large graph with few node labels, how can we (a) identify whether there is generalized network-effects (GNE) or not, (b) estimate GNE to explain the interrelations among node classes, and (c) exploit GNE efficiently to improve the performance on downstream tasks?
no code implementations • 5 Nov 2022 • Shubhranshu Shekhar, Jetson Leder-Luis, Leman Akoglu
The US federal government spends more than a trillion dollars per year on health care, largely provided by private third parties and reimbursed by the government.
1 code implementation • 8 Oct 2022 • Jaemin Yoo, Meng-Chieh Lee, Shubhranshu Shekhar, Christos Faloutsos
Graph neural networks (GNNs) have succeeded in many graph mining tasks, but their generalizability to various graph scenarios is limited due to the difficulty of training, hyperparameter tuning, and the selection of a model itself.
no code implementations • 11 Nov 2021 • Shubhranshu Shekhar, Dhivya Eswaran, Bryan Hooi, Jonathan Elmer, Christos Faloutsos, Leman Akoglu
Given a cardiac-arrest patient being monitored in the ICU (intensive care unit) for brain activity, how can we predict their health outcomes as early as possible?
1 code implementation • 6 Sep 2021 • Meng-Chieh Lee, Shubhranshu Shekhar, Christos Faloutsos, T. Noah Hutson, Leon Iasemidis
Our main contribution is the gen2Out algorithm, that has the following desirable properties: (a) Principled and Sound anomaly scoring that obeys the axioms for detectors, (b) Doubly-general in that it detects, as well as ranks generalized anomaly -- both point- and group-anomalies, (c) Scalable, it is fast and scalable, linear on input size.
1 code implementation • 5 Dec 2020 • Shubhranshu Shekhar, Neil Shah, Leman Akoglu
Fairness and Outlier Detection (OD) are closely related, as it is exactly the goal of OD to spot rare, minority samples in a given population.
no code implementations • 6 May 2018 • Shubhranshu Shekhar, Leman Akoglu
We introduce a new unsupervised anomaly detection ensemble called SPI which can harness privileged information - data available only for training examples but not for (future) test examples.