Summary of Exploring Edge Probability Graph Models Beyond Edge Independency: Concepts, Analyses, and Algorithms, by Fanchen Bu et al.
Exploring Edge Probability Graph Models Beyond Edge Independency: Concepts, Analyses, and Algorithms
by Fanchen Bu, Ruochen Yang, Paul Bogdan, Kijung Shin
First submitted to arxiv on: 26 May 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 This paper proposes a novel approach to generating desirable random graph models (RGMs) by introducing an “edge-dependent realization framework” called binding. This framework enables the production of realistic structures with high clustering and variable graphs, while maintaining tractability and control over graph statistics. The authors demonstrate that existing RGMs, which assume edge independence, are limited in their ability to generate such structures. They theoretically derive closed-form results for subgraph densities and propose practical algorithms for graph generation and parameter fitting. The empirical results show that the proposed binding framework outperforms existing methods in generating realistic graphs with high clustering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want to create artificial networks, like social media or communication systems. These networks should be natural-looking and have different patterns. But current ways of making these networks are limited because they assume each connection is independent from others. This paper shows how to overcome this limitation by introducing a new way to generate networks that takes into account the relationships between connections. The authors prove that their method can create more realistic networks with diverse patterns, which is important for many applications. |
Keywords
» Artificial intelligence » Clustering
