Machine Learning for Graph Data

Outline

  • Deep Learning for graph data
  • Machine Learning with kernel methods for graph data
  • Molecule synthesis by solving the pre-image problem for graph data
  • Machine Learning for online social networks : application to spam detection on Twitter

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Deep Learning for graph data

Breaking the limits of message passing graph neural networks. Balcilar, M.; Héroux, P.; Gaüzère, B.; Vasseur, P.; Adam, S.; and Honeine, P. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning (ICML), volume 139 of Proceedings of Machine Learning Research, pages 599–608, Vienna, Austria, 18 – 24 July 2021. PMLR.
[paper] [poster] [slides]

Analyzing the expressive power of graph neural networks in a spectral perspective. Balcilar, M.; Renton, G.; Héroux, P.; Gaüzère, B.; Adam, S.; and Honeine, P. in Proceedings of Ninth International Conference on Learning Representations (ICLR 2021), Vienna, Austria, 4 – 7 May 2021.
[paper] [poster] [code]

When Spectral Domain Meets Spatial Domain in Graph Neural Networks. Balcilar, M.; Renton, G.; Héroux, P.; Gaüzère, B.; Adam, S.; and Honeine, P. In Proceedings of Thirty-seventh International Conference on Machine Learning (ICML 2020) – Workshop on Graph Representation Learning and Beyond (GRL+ 2020), Vienna, Austria, 12 – 18 July 2020.
[code] [paper] [presentationvideo]

Spectral-designed Depthwise Separable Graph Neural Networks. Balcilar, M.; Renton, G.; Héroux, P.; Gaüzère, B.; Adam, S.; and Honeine, P. In Proceedings of Thirty-seventh International Conference on Machine Learning (ICML 2020) – Workshop on Graph Representation Learning and Beyond (GRL+ 2020), Vienna, Austria, 12 – 18 July 2020.
[code] [paper] [presentationvideo]

Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks. Balcilar, M.; Renton, G.; Héroux, P.; Gaüzère, B.; Adam, S.; and Honeine, P. Technical Report HAL Normandie Université, 2020.
[code] [link] [paper]

Machine Learning with kernel methods for graph data

Graph Kernels based on Linear Patterns: Theoretical and Experimental Comparisons. Jia, L.; Gaüzère, B.; and Honeine, P. Expert Systems With Applications, 189:116095, March 2022.
[paper] [code]

graphkit-learn: A Python Library for Graph Kernels Based on Linear Patterns. Jia, L.; Gaüzère, B.; and Honeine, P. Pattern Recognition Letters, 143:113–121, March 2021.
[paper] [code]

A Metric Learning Approach to Graph Edit Costs for Regression. Jia, L.; Gaüzère, B.; Yger, F.; and Honeine, P. In Proceedings of the IAPR Joint International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (S+SSPR), Venice, Italy, 21 – 22 January 2021.
[paper] [slides]

See also :
A graph pre-image method based on graph edit distances. Jia, L.; Gaüzère, B.; and Honeine, P. In Proceedings of the IAPR Joint International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (S+SSPR), Venice, Italy, 21 – 22 January 2021.
[paper] [slides]

Molecule synthesis by solving the pre-image problem for graph data

A Graph Pre-image Method Based on Graph Edit Distances. Jia, L.; Gaüzère, B.; and Honeine, P. In Proceedings of the IAPR Joint International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (S+SSPR), Venice, Italy, 21 – 22 January 2021
[paper] [slides]

See also :
A Metric Learning Approach to Graph Edit Costs for Regression. Jia, L.; Gaüzère, B.; Yger, F.; and Honeine, P. In Proceedings of the IAPR Joint International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (S+SSPR), Venice, Italy, 21 – 22 January 2021.
[paper] [slides]

Machine Learning for online social networks : application to spam detection on Twitter

SimilCatch: Enhanced Social Spammers Detection on Twitter using Markov Random Fields. El-Mawass, N.; Honeine, P.; and Vercouter, L. Information Processing and Management, 57(6): 102317. 2020.
[link] [paper] [doi]

Supervised Classification of Social Spammers using a Similarity-based Markov Random Field Approach. El-Mawass, N.; Honeine, P.; and Vercouter, L. In Proc. the 5th multidisciplinary international social networks conference, of MISNC ’18, pages 14:1 – 14:8, New York, NY, USA, 16 – 18 July 2018. ACM 
[link] [paper] [doi]

Champ Aléatoire de Markov pour la Détection Supervisée des Comptes Malicieux sur Twitter. El-Mawass, N.; Honeine, P.; and Vercouter, L. In 20-ème Conférence d’Apprentissage automatique (CAp) – 20th annual meeting of the francophone Machine Learning community, Rouen, France, 20 – 22 June 2018. 
[paper]