Spectral Estimation of Conditional Random Graph Models for Large-Scale Network Data

Autor: Freno, Antonino, Keller, Mikaela, Garriga, Gemma C., Tommasi, Marc

Generative models for graphs have been typically committed to strong prior assumptions concerning the form of the modeled distributions. Moreover, the vast majority of currently available models are either only suitable for characterizing some partic

Externí odkaz: http://arxiv.org/abs/1210.4860

Probability Estimation over Large-Scale Random Networks via the Fiedler Delta Statistic

Autor: Freno, Antonino, Keller, Mikaela, Tommasi, Marc

Statistical models for networks have been typically committed to strong prior assumptions concerning the form of the modeled distributions. Moreover, the vast majority of currently available models are explicitly designed for capturing some specific

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______165::4b3de51968b0930bdec39a3e2378e3b1
https://inria.hal.science/hal-00922432

Learning to Recommend Links using Graph Structure and Node Content

Autor: Freno, Antonino, Garriga, Gemma, Keller, Mikaela

Publikováno v: Neural Information Processing Systems Workshop on Choice Models and Preference Learning
Neural Information Processing Systems Workshop on Choice Models and Preference Learning, Dec 2011, Granada, Spain

International audience; The link prediction problem for graphs is a binary classification task that estimates the presence or absence of a link between two nodes in the graph. Links absent from the training set, however, cannot be directly considered

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::9b9ed097ca5d8c8a7f2ef2f70ec6119d
https://hal.inria.fr/hal-00641419

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Probabilistic Interpretation of Neural Networks for the Classification of Vectors, Sequences and Graphs.

Autor: Trentin, Edmondo, Freno, Antonino

Publikováno v: Innovations in Neural Information Paradigms & Applications; 2009, p155-182, 28p