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Graph topology inference benchmarks for machine learning

C. Lassance, V. Gripon et G. Mateos, "Graph topology inference benchmarks for machine learning," dans IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1--6, 2020.

Graphs are nowadays ubiquitous in the fields of signal processing and machine learning. As a tool used to express relationships between objects, graphs can be deployed to various ends: (i) clustering of vertices, (ii) semi-supervised classification of vertices, (iii) supervised classification of graph signals, and (iv) denoising of graph signals. However, in many practical cases graphs are not explicitly available and must therefore be inferred from data. Validation is a challenging endeavor that naturally depends on the downstream task for which the graph is learnt. Accordingly, it has often been difficult to compare the efficacy of different algorithms. In this work, we introduce several ease-to-use and publicly released benchmarks specifically designed to reveal the relative merits and limitations of graph inference methods. We also contrast some of the most prominent techniques in the literature.

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Bibtex
@inproceedings{LasGriMat2020,
  author = {Carlos Lassance and Vincent Gripon and
Gonzalo Mateos},
  title = {Graph topology inference benchmarks for
machine learning},
  booktitle = {IEEE 30th International Workshop on
Machine Learning for Signal Processing (MLSP)},
  year = {2020},
  pages = {1--6},
}




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