Summary of Inductive Spatial Temporal Prediction Under Data Drift with Informative Graph Neural Network, by Jialun Zheng et al.
Inductive Spatial Temporal Prediction Under Data Drift with Informative Graph Neural Network
by Jialun Zheng, Divya Saxena, Jiannong Cao, Hanchen Yang, Penghui Ruan
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to inductive spatial-temporal prediction, which can generalize historical data to predict unseen data in highly dynamic scenarios such as traffic systems and stock markets. However, external events and emerging new entities can undermine prediction accuracy by inducing data drift over time. The authors design an Informative Graph Neural Network (INF-GNN) to distill diversified invariant patterns and improve prediction accuracy under data drift. INF-GNN uses a uniquely designed metric, Relation Importance (RI), to select stable entities and distinct spatial relationships, generalizing new entities’ data via neighbors merging. Additionally, the model incorporates an informative temporal memory buffer to emphasize valuable timestamps extracted using influence functions within time intervals. The authors also propose RI loss optimization for pattern consolidation. Extensive experiments on a real-world dataset demonstrate that INF-GNN significantly outperforms existing alternatives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem in making predictions about things that change over time, like traffic patterns or stock prices. Right now, most methods try to find stable patterns but don’t account for new information that comes along. The authors create a new way of looking at this data by using something called an Informative Graph Neural Network (INF-GNN). This tool helps the model understand what’s important and what’s not, so it can make better predictions even when new things show up. They tested their method on real-world data and found that it worked much better than other approaches. |
Keywords
» Artificial intelligence » Gnn » Graph neural network » Optimization
