Summary of Graphsl: An Open-source Library For Graph Source Localization Approaches and Benchmark Datasets, by Junxiang Wang and Liang Zhao
GraphSL: An Open-Source Library for Graph Source Localization Approaches and Benchmark Datasets
by Junxiang Wang, Liang Zhao
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper introduces GraphSL, a new library for studying graph source localization. The authors describe it as an inverse problem, where graph diffusion predicts information spread from sources, and graph source localization predicts the sources from the diffusions. The library enables exploration of various graph diffusion models and evaluation of cutting-edge source localization approaches on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GraphSL is a tool for understanding how information spreads through graphs. It helps researchers try out different ways to predict where information comes from based on how it spreads. This can help with tasks like tracing the origins of rumors or understanding how ideas spread through social networks. |
Keywords
» Artificial intelligence » Diffusion
