Summary of Timegraphs: Graph-based Temporal Reasoning, by Paridhi Maheshwari et al.
TimeGraphs: Graph-based Temporal Reasoning
by Paridhi Maheshwari, Hongyu Ren, Yanan Wang, Rok Sosic, Jure Leskovec
First submitted to arxiv on: 6 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Many real-world systems exhibit complex dynamic behaviors that are captured as time series of agent interactions. Current methods primarily encode temporal dynamics through simple sequence-based models, but these models often fail to capture the full spectrum of rich dynamics in the input. Our proposed approach, TimeGraphs, characterizes dynamic interactions as a hierarchical temporal graph, diverging from traditional sequential representations. This compact graph-based representation enables adaptive reasoning across diverse time scales, and we demonstrate its effectiveness on multiple datasets with complex agent interactions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series data shows how things change over time. Researchers have been trying to understand these changes by using simple models that look at the sequence of events. But this approach has some limitations – it’s not good at capturing all the details in the data, and it can be slow when there are lots of events. Our new method, called TimeGraphs, looks at the relationships between events in a different way. It uses a graph to represent these relationships, which makes it more efficient and better at handling complex data. We tested this approach on several datasets and found that it performed much better than previous methods. It can even handle new situations without being trained specifically for them! |
Keywords
* Artificial intelligence * Time series
