Summary of Inference Of Sequential Patterns For Neural Message Passing in Temporal Graphs, by Jan Von Pichowski et al.
Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs
by Jan von Pichowski, Vincenzo Perri, Lisi Qarkaxhija, Ingo Scholtes
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI); Machine Learning (stat.ML)
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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 The proposed HYPA-DBGNN model combines two steps: inferring anomalous sequential patterns in time series data on graphs based on a statistically principled null model, and a neural message passing approach that utilizes a higher-order De Bruijn graph. This framework introduces an inductive bias that enhances model interpretability. It leverages hypergeometric graph ensembles to identify anomalous edges within both first- and higher-order De Bruijn graphs, which encode the temporal ordering of events. The model is evaluated for static node classification using benchmark datasets and a synthetic dataset, demonstrating its ability to incorporate the observed inductive bias regarding over- and under-represented temporal edges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The HYPA-DBGNN model is a new way to study patterns on graphs that change over time. It works by first looking at how often certain events happen, then using this information to find unusual patterns. This helps make the model more understandable and reliable. The researchers tested their approach on different datasets and found it performed better than other methods. |
Keywords
» Artificial intelligence » Classification » Time series
