Summary of A Deep Probabilistic Framework For Continuous Time Dynamic Graph Generation, by Ryien Hosseini et al.
A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation
by Ryien Hosseini, Filippo Simini, Venkatram Vishwanath, Henry Hoffmann
First submitted to arxiv on: 20 Dec 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 Medium Difficulty summary: Recent advancements in graph representation learning have led to a growing focus on dynamic graphs, which exhibit evolving topologies and features over time. To address this need, we propose DG-Gen, a novel generative framework for continuous-time dynamic graphs that models interactions as a joint probability of an edge forming between two nodes at a given time. This approach allows for scalable and inductive generation of synthetic dynamic graphs, enabling applications like data augmentation, obfuscation, and anomaly detection. Our experimental results demonstrate the effectiveness of DG-Gen on five datasets, showcasing its ability to generate higher-fidelity graphs and significantly improve link prediction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research focuses on creating new types of computer networks called dynamic graphs that change over time. These changing networks are important for applications like predicting links between people or devices. The current problem is that there aren’t many ways to generate these changing networks in a realistic way. To solve this, the researchers created a new method called DG-Gen that can create synthetic networks that look like real ones. They tested this method on five different datasets and found that it works better than previous methods at predicting links. |
Keywords
» Artificial intelligence » Anomaly detection » Data augmentation » Probability » Representation learning
