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Graph rationales are representative subgraph structures that best explain and support the graph neural network (GNN) predictions. Graph rationalization involves the joint identification of these subgraphs during GNN training, resulting in improved interpretability and generalization. GNN is widely used for node-level tasks such as paper classification and graph-level tasks such as molecular property prediction. However, on both levels, little attention has been given to GNN rationalization and the lack of training examples makes it difficult to identify the optimal graph rationales. In this work, we address the problem by proposing a unified data augmentation framework with two novel operations on environment subgraphs to rationalize GNN prediction. We define the environment subgraph as the remaining subgraph after rationale identification and separation. The framework efficiently performs rationale–environment separation in therepresentation spacefor a node’s neighborhood graph or a graph’s complete structure to avoid the high complexity of explicit graph decoding and encoding. We conduct experiments on 17 datasets spanning node classification, graph classification, and graph regression. Results demonstrate that our framework is effective and efficient in rationalizing and enhancing GNNs for different levels of tasks on graphs.
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Data Practices for Studying the Impacts of Environmental Amenities and Hazards with Nationwide Property Data
We discuss data quality and modeling issues inherent in the use of nationwide property data to value environmental amenities. By example of ZTRAX, a U.S.-wide real estate database, we identify challenges and propose guidance for: (1) the identification of arm’s-length sales, (2) the geo-location of parcels and buildings, (3) temporal linkages between transaction, assessor, and parcel data, (4) the identification of property types, such as single-family homes and vacant lands, and (5) dealing with missing or mismeasured data for standard housing attributes. We review current practice and show that how researchers address these issues can meaningfully influence research findings.
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- PAR ID:
- 10465545
- Date Published:
- Journal Name:
- Land Economics
- ISSN:
- 0023-7639
- Page Range / eLocation ID:
- 102122-0090R
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3368/le.100.1.102122-0090R
