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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 ...
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.
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.
Bryan Perozzi. Google Research, New York, NY, Emmanuel Müller. Technical University of Dortmund, Germany. The Journal of Machine Learning Research, Volume 24, Issue ...
8 de oct. de 2020 · 1 code implementation • 17 Jul 2023 • Mustafa Yasir, John Palowitch, Anton Tsitsulin, Long Tran-Thanh, Bryan Perozzi In this work we examine how two additional synthetic graph generators can improve GraphWorld's evaluation; LFR, a well-established model in the graph clustering literature and CABAM, a recent adaptation of the Barabasi-Albert model tailored for GNN benchmarking.
Projects. We introduce Normality, an extension of Newman's Assortativity which can be efficiently optimized for very high dimensional attribute vectors. DeepWalk uses deep learning techniques to learn representations of graphs for semi-supervised learning problems. Focused Clustering examines user-oriented clustering and anomaly detection in ...
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