Summary of Deepsn: a Sheaf Neural Framework For Influence Maximization, by Asela Hevapathige et al.
DeepSN: A Sheaf Neural Framework for Influence Maximization
by Asela Hevapathige, Qing Wang, Ahad N. Zehmakan
First submitted to arxiv on: 16 Dec 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 proposes a novel framework called DeepSN for influence maximization in social networks. It addresses two fundamental challenges: (1) traditional Graph Neural Networks (GNNs) struggle to capture complex dynamics, and (2) designing optimization objectives is combinatorially expensive. DeepSN employs sheaf neural diffusion to learn diverse influence patterns end-to-end, providing enhanced separability. The framework also includes an optimization technique that accounts for overlapping influences, reducing the search space and identifying optimal seed sets efficiently. The authors demonstrate the effectiveness of their approach on both synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem in social networks: finding the best people to influence others. Researchers have been using machine learning techniques to help with this, but there are still some challenges. One issue is that traditional methods don’t work well when trying to understand how complex behaviors spread through a network. Another problem is that it’s hard to find the right way to optimize for the best influencers. To solve these problems, the authors propose a new approach called DeepSN. It uses a special type of neural network to learn about different influence patterns and finds the best people to target. The authors tested their method on some datasets and showed that it works well. |
Keywords
» Artificial intelligence » Diffusion » Machine learning » Neural network » Optimization
