Publications

(2022). The Infinite Contextual Graph Markov Model. ICML 2022.

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(2022). Towards Learning Trustworthily, Automatically, and with Guarantees on Graphs: an Overview. In Neurocomputing.

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(2022). Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark. In Frontiers in Artificial Intelligence.

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(2021). Robust Malware Classification via Deep Graph Networks on Call Graph Topologies. ESANN-2021.

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(2021). Graph Mixture Density Networks. ICML 2021.

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(2021). Modeling Edge Features with Deep Bayesian Graph Networks. IJCNN-2021.

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(2021). Concept Matching for Low-Resource Classification. IJCNN-2021.

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(2021). A deep graph network-enhanced sampling approach to efficiently explore the space of reduced representations of proteins. In Frontiers Molecular Biosciences.

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(2021). Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification. The Web Conference 2021, Workshop on Graph Learning Benchmarks.

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(2020). Theoretically Expressive and Edge-aware Graph Learning. ESANN-2020.

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(2020). Accelerating the identification of informative reduced representations of proteins with deep learning for graphs. preprint.

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(2020). Probabilistic Learning on Graphs via Contextual Architectures. In JMLR.

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(2020). A Gentle Introduction to Deep Learning for Graphs. In Neural Networks.

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(2020). A Fair Comparison of Graph Neural Networks for Graph Classification. In ICLR-2020.

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(2018). Contextual Graph Markov Model: a Deep and Generative Approach to Graph Processing. In ICML-2018.

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