mlsanityData to 14 Oct 2026

GraphSL: An Open-Source Library for Graph Source Localization Approaches and Benchmark Datasets

arXiv 2405.03724 ↗First posted 6 May 2024cs.LG cs.SIcited by 1

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Abstract (arXiv)

We introduce GraphSL, a new library for studying the graph source localization problem. graph diffusion and graph source localization are inverse problems in nature: graph diffusion predicts information diffusions from information sources, while graph source localization predicts information sources from information diffusions. GraphSL facilitates the exploration of various graph diffusion models for simulating information diffusions and enables the evaluation of cutting-edge source localization approaches on established benchmark datasets. The source code of GraphSL is made available at Github Repository (https://github.com/xianggebenben/GraphSL). Bug reports and feedback can be directed to the Github issues page (https://github.com/xianggebenben/GraphSL/issues).

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 19 references that are in the map. First posted on arXiv 6 May 2024.

Builds on

4 of 19 references.

References · 4

  1. 2022Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problemscited by 18
  2. 2016Catch'Em All: Locating Multiple Diffusion Sources in Networks with Partial Observationscited by 16
  3. 2020Assessing the risks of "infodemics" in response to COVID-19 epidemicscited by 14
  4. 2022An Invertible Graph Diffusion Neural Network for Source Localizationcited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Network World Models as Environments for Algorithm Design on Complex Systemscited by 0

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  1. 2025Structure-prior Informed Diffusion Model for Graph Source Localization with Limited Data
  2. 2023Two-stage Denoising Diffusion Model for Source Localization in Graph Inverse Problems
  3. 2024Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimization
  4. 2024Towards understanding Diffusion Models (on Graphs)
  5. 2023Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning
  6. 2025Graph-Aware Diffusion for Signal Generation
  7. 2023Generative Diffusion Models on Graphs: Methods and Applications
  8. 2024Diffusion-based Graph Generative Methods