Summary of A Multi-core Periphery Perspective: Ranking Via Relative Centrality, by Chandra Sekhar Mukherjee et al.
A multi-core periphery perspective: Ranking via relative centrality
by Chandra Sekhar Mukherjee, Jiapeng Zhang
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 A novel graph structure quantification approach is proposed to better understand the coexistence of community and core-periphery structures in real-world graphs. The method infers the core-periphery structure of a graph and its impact on understanding community structure, leveraging ground truth communities with densely connected cores and sparse peripheries. This work addresses an open problem in the field by introducing a novel quantification for graphs with known communities. By exploring the relationship between these two graph structures, the approach enables more accurate community detection and analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand how communities and core-periphery structures are connected is introduced. Usually, we study these concepts separately, but this paper shows that they’re related. The goal is to better understand how densely connected “cores” in a graph’s communities relate to the more sparse “peripheries”. This can help us detect communities more accurately. |
