In today’s increasingly connected world, graph mining plays a piv- otal role in many real-world application domains, including social network analysis, recommendations, marketing and financial secu- rity. Tremendous efforts have been made to develop a wide range of computational models. However, recent studies have revealed that many widely-applied graph mining models could suffer from potential discrimination. Fairness on graph mining aims to develop strategies in order to mitigate bias introduced/amplified during the mining process. The unique challenges of enforcing fairness on graph mining include (1) theoretical challenge on non-IID nature of graph data, which may invalidate the basic assumption behind many existing studies in fair machine learning, and (2) algorith- mic challenge on the dilemma of balancing model accuracy and fairness. This tutorial aims to (1) present a comprehensive review of state-of-the-art techniques in fairness on graph mining and (2) identify the open challenges and future trends. In particular, we start with reviewing the background, problem definitions, unique challenges and related problems; then we will focus on an in-depth overview of (1) recent techniques in enforcing group fairness, indi- vidual fairness and other fairness notions in the context of graph mining, and (2) future directions in studying algorithmic fairness on graphs. We believe this tutorial could be attractive to researchers and practitioners in areas including data mining, artificial intel- ligence, social science and beneficial to a plethora of real-world application domains.
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Simulating 3-D Stellar Hydrodynamics using PPM and PPB Multifluid Gas Dynamics on CPU and CPU+GPU Nodes
The special computational challenges of simulating 3-D hydrodynamics in deep stellar interiors are discussed, and numerical algorithmic responses described. Results of recent simulations carried out at scale on the NSF's Blue Waters machine at the University of Illinois are presented, with a special focus on the computational challenges they address. Prospects for future work using GPU-accelerated nodes such as those on the DoE's new Summit machine at Oak Ridge National Laboratory are described, with a focus on numerical algorithmic accommodations that we believe will be necessary.
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- NSF-PAR ID:
- 10101327
- Date Published:
- Journal Name:
- Journal of physics. Conference series
- Volume:
- 1225
- ISSN:
- 1742-6596
- Page Range / eLocation ID:
- 012020
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/https://doi.org/10.1088/1742-6596/1225/1/012020
