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6 de may. de 2016 · Don't Walk, Skip! Online Learning of Multi-scale Network Embeddings. Bryan Perozzi, Vivek Kulkarni, Haochen Chen, Steven Skiena. We present Walklets, a novel approach for learning multiscale representations of vertices in a network. In contrast to previous works, these representations explicitly encode multiscale vertex relationships in a way ...
24 de ago. de 2014 · R. Al-Rfou, B. Perozzi, and S. Skiena. Polyglot: Distributed word representations for multilingual nlp. In Proceedings of the Seventeenth Conference on Computational Natural Language Learning, pages 183--192, Sofia, Bulgaria, August 2013.
Bryan Perozzi Stony Brook University Department of Computer Science Rami Al-Rfou Stony Brook University Department of Computer Science Steven Skiena Stony Brook University Department of Computer Science {bperozzi, ralrfou, skiena}@cs.stonybrook.edu ABSTRACT We present DeepWalk, a novel approach for learning la-tent representations of vertices ...
9 de may. de 2016 · Bryan Perozzi, Leman Akoglu May 9, 2016 Awards: Best Paper Runner-up, SDM’16! Overview. Given a graph with node attributes, what neighborhoods are anomalous? To answer this question, one needs a quality score that utilizes both structure and attributes.
Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, and Stephan Günne-mann. 2020. Scaling Graph Neural Networks with Approximate PageRank. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery andDataMining(KDD’20),August23–27,2020,VirtualEvent,CA,USA.ACM,
Polyglot: Distributed Word Representations for Multilingual NLP. Rami Al-Rfou, Bryan Perozzi, Steven Skiena. August 2013. PDF. Cite. Type. Conference paper. Publication. Proceedings of the Seventeenth Conference on Computational Natural Language Learning.
Graph Clustering with Graph Neural Networks . Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller; 24(127):1−21, 2023.. Abstract. Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction.
