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Bryan won an IACS Junior Researcher Award for 2 years, was the recipient of the Catacosinos Fellowship for Excellence in Computer Science, and received the Best Paper (runner-up) award at the SIAM Conference on Data Mining in 2016 for his work on anomaly detection in graphs.
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MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan. Proceedings of the 36th International Conference on Machine Learning , PMLR 97:21-29, 2019.
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 ...
26 de ago. de 2014 · Bryan Perozzi DeepWalk: Online Learning of Social Representations Consider the graph vertices as leaves of a balanced binary tree. Maximizing is equivalent to maximizing the probability of the path from the root to the node. specifically, maximizing C 1 C 2 C 3 Each of {C 1, C 2, C 3} is a logistic binary classifier.
23 de jun. de 2017 · HARP: Hierarchical Representation Learning for Networks. Haochen Chen, Bryan Perozzi, Yifan Hu, Steven Skiena. We present HARP, a novel method for learning low dimensional embeddings of a graph's nodes which preserves higher-order structural features. Our proposed method achieves this by compressing the input graph prior to embedding it ...
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.
