Summary of Expressivity Of Representation Learning on Continuous-time Dynamic Graphs: An Information-flow Centric Review, by Sofiane Ennadir et al.
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review
by Sofiane Ennadir, Gabriela Zarzar Gandler, Filip Cornell, Lele Cao, Oleg Smirnov, Tianze Wang, Levente Zólyomi, Björn Brinne, Sahar Asadi
First submitted to arxiv on: 5 Dec 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 The paper reviews Graph Neural Networks (GNNs) for learning expressive representations on Continuous-Time Dynamic Graphs (CTDGs). It presents a novel theoretical framework analyzing the expressivity of CTDG models through an Information-Flow (IF) lens, which quantifies their ability to propagate and encode temporal and structural information. The paper categorizes existing CTDG methods based on their suitability for different graph types and application scenarios. It also examines Self-Supervised Representation Learning (SSRL) methods tailored to CTDGs, including predictive and contrastive approaches, highlighting their potential to mitigate the reliance on labeled data. Empirical evaluations validate the theoretical insights, demonstrating strengths and limitations across various graph types. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to learn from graphs that change over time. It introduces a new way to think about this called Continuous-Time Dynamic Graphs (CTDGs). The authors show how different methods for learning from CTDGs work and which ones are best suited for different kinds of graphs and tasks. They also explore ways to learn without labeled data, which can be helpful when we don’t have enough information to train a model. Overall, the paper helps us understand how to use GNNs on dynamic graphs and choose the right method for our needs. |
Keywords
» Artificial intelligence » Representation learning » Self supervised
