Summary of Deep Representation Learning For Forecasting Recursive and Multi-relational Events in Temporal Networks, by Tony Gracious et al.
Deep Representation Learning for Forecasting Recursive and Multi-Relational Events in Temporal Networks
by Tony Gracious, Ambedkar Dukkipati
First submitted to arxiv on: 27 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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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 addresses the challenge of predicting complex relationships among multiple entities, with applications in financial networks and e-commerce. The problem involves recursive relations between multiple entities, which is still an open issue. To tackle this problem, the authors propose a model called Relational Recursive Hyperedge Temporal Point Process (RRHyperTPP), which learns dynamic node representations based on historical interaction patterns and uses hyperedge link prediction to forecast interaction events. The model outperforms previous state-of-the-art methods for interaction forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about predicting relationships between many things, like people or companies. These relationships can be very complicated and involve multiple people or groups. This problem is important because it can help us understand and predict how these relationships will change over time. The authors propose a new way to do this using something called temporal hypergraphs, which are special kinds of graphs that can handle complex relationships. They also develop a new method to learn from this data and show that their approach performs better than others. |
