Bryan Perozzi

ORCID

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 87

Top Venues

Publications

Bahare Fatemi, Jonathan Halcrow, Bryan Perozzi
Talk like a Graph: Encoding Graphs for Large Language Models. ICLR (2024)
Clayton Sanford, Bahare Fatemi, Ethan Hall, Anton Tsitsulin, Seyed Mehran Kazemi, Jonathan Halcrow, Bryan Perozzi, Vahab Mirrokni
Understanding Transformer Reasoning Capabilities via Graph Algorithms. CoRR (2024)
Bahare Fatemi, Mehran Kazemi, Anton Tsitsulin, Karishma Malkan, Jinyeong Yim, John Palowitch, Sungyong Seo, Jonathan Halcrow, Bryan Perozzi
Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning. CoRR (2024)
Bryan Perozzi, Bahare Fatemi, Dustin Zelle, Anton Tsitsulin, Seyed Mehran Kazemi, Rami Al-Rfou, Jonathan Halcrow
Let Your Graph Do the Talking: Encoding Structured Data for LLMs. CoRR (2024)
Anton Tsitsulin, Bryan Perozzi, Bahare Fatemi, Jonathan J. Halcrow
Graph Reasoning with LLMs (GReaL). KDD (2024)
Zhikai Chen, Haitao Mao, Jingzhe Liu, Yu Song, Bingheng Li, Wei Jin, Bahare Fatemi, Anton Tsitsulin, Bryan Perozzi, Hui Liu, Jiliang Tang
Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights. CoRR (2024)
Jialin Dong, Bahare Fatemi, Bryan Perozzi, Lin F. Yang, Anton Tsitsulin
Don't Forget to Connect! Improving RAG with Graph-based Reranking. CoRR (2024)
Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi
Unsupervised Embedding Quality Evaluation. CoRR (2023)
Bahare Fatemi, Sami Abu-El-Haija, Anton Tsitsulin, Seyed Mehran Kazemi, Dustin Zelle, Neslihan Bulut, Jonathan Halcrow, Bryan Perozzi
UGSL: A Unified Framework for Benchmarking Graph Structure Learning. CoRR (2023)
Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Russ Salakhutdinov
Graph Generative Model for Benchmarking Graph Neural Networks. ICML (2023)
Bahare Fatemi, Jonathan Halcrow, Bryan Perozzi
Talk like a Graph: Encoding Graphs for Large Language Models. CoRR (2023)
Brandon A. Mayer, Anton Tsitsulin, Hendrik Fichtenberger, Jonathan Halcrow, Bryan Perozzi
HUGE: Huge Unsupervised Graph Embeddings with TPUs. KDD (2023)
Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller
Graph Clustering with Graph Neural Networks. J. Mach. Learn. Res. 24 (2023)
Kaize Ding, Albert Jiongqian Liang, Bryan Perozzi, Ting Chen, Ruoxi Wang, Lichan Hong, Ed H. Chi, Huan Liu, Derek Zhiyuan Cheng
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer. SIGIR (2023)
Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi
Unsupervised Embedding Quality Evaluation. TAG-ML (2023)
Anton Tsitsulin, Bryan Perozzi
The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure. CoRR (2023)
Bryan Perozzi, Sami Abu-El-Haija, Anton Tsitsulin
Graph Neural Networks in TensorFlow. KDD (2023)
Sami Abu-El-Haija, Joshua V. Dillon, Bahare Fatemi, Kyriakos Axiotis, Neslihan Bulut, Johannes Gasteiger, Bryan Perozzi, MohammadHossein Bateni
SubMix: Learning to Mix Graph Sampling Heuristics. UAI (2023)
Kaidi Cao, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Dustin Zelle, Yanqi Zhou, Charith Mendis, Jure Leskovec, Bryan Perozzi
Learning Large Graph Property Prediction via Graph Segment Training. CoRR (2023)
Mustafa Yasir, John Palowitch, Anton Tsitsulin, Long Tran-Thanh, Bryan Perozzi
Examining the Effects of Degree Distribution and Homophily in Graph Learning Models. CoRR (2023)
Mangpo Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao, Bahare Fatemi, Michael Burrows, Charith Mendis, Bryan Perozzi
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs. NeurIPS (2023)
Brandon A. Mayer, Anton Tsitsulin, Hendrik Fichtenberger, Jonathan Halcrow, Bryan Perozzi
HUGE: Huge Unsupervised Graph Embeddings with TPUs. CoRR (2023)
Kaidi Cao, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Dustin Zelle, Yanqi Zhou, Charith Mendis, Jure Leskovec, Bryan Perozzi
Learning Large Graph Property Prediction via Graph Segment Training. NeurIPS (2023)
Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari, MohammadHossein Bateni, Deepak Ramachandran, Bryan Perozzi, Vahab Mirrokni
Tackling Provably Hard Representative Selection via Graph Neural Networks. Trans. Mach. Learn. Res. 2023 (2023)
Qi Zhu, Yizhu Jiao, Natalia Ponomareva, Jiawei Han, Bryan Perozzi
Explaining and Adapting Graph Conditional Shift. CoRR (2023)
Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao, Bahare Fatemi, Charith Mendis, Bryan Perozzi
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs. CoRR (2023)
Minji Yoon, John Palowitch, Dustin Zelle, Ziniu Hu, Ruslan Salakhutdinov, Bryan Perozzi
Zero-shot Domain Adaptation of Heterogeneous Graphs via Knowledge Transfer Networks. CoRR (2022)
Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. CoRR (2022)
Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Ruslan Salakhutdinov
Scalable Privacy-enhanced Benchmark Graph Generative Model for Graph Convolutional Networks. CoRR (2022)
Minji Yoon, John Palowitch, Dustin Zelle, Ziniu Hu, Ruslan Salakhutdinov, Bryan Perozzi
Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks. NeurIPS (2022)
Seyed Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari, MohammadHossein Bateni, Deepak Ramachandran, Bryan Perozzi, Vahab S. Mirrokni
Tackling Provably Hard Representative Selection via Graph Neural Networks. CoRR (2022)
Anton Tsitsulin, Benedek Rozemberczki, John Palowitch, Bryan Perozzi
Synthetic Graph Generation to Benchmark Graph Learning. CoRR (2022)
Kimon Fountoulakis, Dake He, Silvio Lattanzi, Bryan Perozzi, Anton Tsitsulin, Shenghao Yang
On Classification Thresholds for Graph Attention with Edge Features. CoRR (2022)
John Palowitch, Anton Tsitsulin, Brandon Mayer, Bryan Perozzi
GraphWorld: Fake Graphs Bring Real Insights for GNNs. KDD (2022)
Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy
Machine Learning on Graphs: A Model and Comprehensive Taxonomy. J. Mach. Learn. Res. 23 (2022)
Oleksandr Ferludin, Arno Eigenwillig, Martin Blais, Dustin Zelle, Jan Pfeifer, Alvaro Sanchez-Gonzalez, Wai Lok Sibon Li, Sami Abu-El-Haija, Peter W. Battaglia, Neslihan Bulut, Jonathan Halcrow, Filipe Miguel Gonçalves de Almeida, Silvio Lattanzi, André Linhares, Brandon Mayer, Vahab S. Mirrokni, John Palowitch, Mihir Paradkar, Jennifer She, Anton Tsitsulin, Kevin Villela, Lisa Wang, David Wong, Bryan Perozzi
TF-GNN: Graph Neural Networks in TensorFlow. CoRR (2022)
Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. NeurIPS (2022)
John Palowitch, Anton Tsitsulin, Brandon Mayer, Bryan Perozzi
GraphWorld: Fake Graphs Bring Real Insights for GNNs. CoRR (2022)
Qi Zhu, Natalia Ponomareva, Jiawei Han, Bryan Perozzi
Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data. NeurIPS (2021)
Elan Sopher Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Bryan Perozzi, Greg Ver Steeg, Aram Galstyan
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning. ICLR (2021)
Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Bryan Perozzi, Greg Ver Steeg, Aram Galstyan
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning. CoRR (2021)
Benedek Rozemberczki, Peter Englert, Amol Kapoor, Martin Blais, Bryan Perozzi
Pathfinder Discovery Networks for Neural Message Passing. WWW (2021)
Qi Zhu, Natalia Ponomareva, Jiawei Han, Bryan Perozzi
Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data. CoRR (2021)
Stefan Postavaru, Anton Tsitsulin, Filipe Miguel Gonçalves de Almeida, Yingtao Tian, Silvio Lattanzi, Bryan Perozzi
InstantEmbedding: Efficient Local Node Representations. CoRR (2020)
Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann
Scaling Graph Neural Networks with Approximate PageRank. CoRR (2020)
Amol Kapoor, Xue Ben, Luyang Liu, Bryan Perozzi, Matt Barnes, Martin Blais, Shawn O'Banion
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks. CoRR (2020)
Jonathan Halcrow, Alexandru Mosoi, Sam Ruth, Bryan Perozzi
Grale: Designing Networks for Graph Learning. CoRR (2020)
Benedek Rozemberczki, Peter Englert, Amol Kapoor, Martin Blais, Bryan Perozzi
Pathfinder Discovery Networks for Neural Message Passing. CoRR (2020)
Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi
Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs. WWW (2020)
Di Huang, Zihao He, Yuzhong Huang, Kexuan Sun, Sami Abu-El-Haija, Bryan Perozzi, Kristina Lerman, Fred Morstatter, Aram Galstyan
Graph Embedding with Personalized Context Distribution. WWW (Companion Volume) (2020)
Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller
Graph Clustering with Graph Neural Networks. CoRR (2020)
Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi
Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs. CoRR (2020)
John Palowitch, Bryan Perozzi
Debiasing Graph Representations via Metadata-Orthogonal Training. ASONAM (2020)
Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy
Machine Learning on Graphs: A Model and Comprehensive Taxonomy. CoRR (2020)
Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann
Scaling Graph Neural Networks with Approximate PageRank. KDD (2020)
Jonathan Halcrow, Alexandru Mosoi, Sam Ruth, Bryan Perozzi
Grale: Designing Networks for Graph Learning. KDD (2020)
Rami Al-Rfou, Dustin Zelle, Bryan Perozzi
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels. CoRR (2019)
Alessandro Epasto, Bryan Perozzi
Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts. WWW (2019)
Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. UAI (2019)
Rami Al-Rfou, Bryan Perozzi, Dustin Zelle
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels. WWW (2019)
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. ICML (2019)
Alessandro Epasto, Bryan Perozzi
Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts. CoRR (2019)
John Palowitch, Bryan Perozzi
MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit. CoRR (2019)
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Hrayr Harutyunyan, Nazanin Alipourfard, Kristina Lerman, Greg Ver Steeg, Aram Galstyan
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. CoRR (2019)
Yingtao Tian, Haochen Chen, Bryan Perozzi, Muhao Chen, Xiaofei Sun, Steven Skiena
Social Relation Inference via Label Propagation. ECIR (1) (2019)
Haochen Chen, Bryan Perozzi, Rami Al-Rfou, Steven Skiena
A Tutorial on Network Embeddings. CoRR (2018)
Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. CoRR (2018)
Bryan Perozzi, Leman Akoglu
Discovering Communities and Anomalies in Attributed Graphs: Interactive Visual Exploration and Summarization. ACM Trans. Knowl. Discov. Data 12 (2) (2018)
Haochen Chen, Bryan Perozzi, Yifan Hu, Steven Skiena
HARP: Hierarchical Representation Learning for Networks. AAAI (2018)
Haochen Chen, Xiaofei Sun, Yingtao Tian, Bryan Perozzi, Muhao Chen, Steven Skiena
Enhanced Network Embeddings via Exploiting Edge Labels. CoRR (2018)
Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alexander A. Alemi
Watch Your Step: Learning Node Embeddings via Graph Attention. NeurIPS (2018)
Haochen Chen, Xiaofei Sun, Yingtao Tian, Bryan Perozzi, Muhao Chen, Steven Skiena
Enhanced Network Embeddings via Exploiting Edge Labels. CIKM (2018)
Bryan Perozzi, Vivek Kulkarni, Haochen Chen, Steven Skiena
Don't Walk, Skip!: Online Learning of Multi-scale Network Embeddings. ASONAM (2017)
Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou
Learning Edge Representations via Low-Rank Asymmetric Projections. CoRR (2017)
Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alex Alemi
Watch Your Step: Learning Graph Embeddings Through Attention. CoRR (2017)
Eduardo Fleury, Silvio Lattanzi, Vahab S. Mirrokni, Bryan Perozzi
ASYMP: Fault-tolerant Mining of Massive Graphs. CoRR (2017)
Yanqing Chen, Bryan Perozzi, Steven Skiena
Vector-based similarity measurements for historical figures. Inf. Syst. 64 (2017)
Aria Rezaei, Bryan Perozzi, Leman Akoglu
Ties That Bind - Characterizing Classes by Attributes and Social Ties. CoRR (2017)
Aria Rezaei, Bryan Perozzi, Leman Akoglu
Ties That Bind: Characterizing Classes by Attributes and Social Ties. WWW (Companion Volume) (2017)
Haochen Chen, Bryan Perozzi, Yifan Hu, Steven Skiena
HARP: Hierarchical Representation Learning for Networks. CoRR (2017)
Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou
Learning Edge Representations via Low-Rank Asymmetric Projections. CIKM (2017)
Bryan Perozzi, Michael Schueppert, Jack Saalweachter, Mayur Thakur
When Recommendation Goes Wrong: Anomalous Link Discovery in Recommendation Networks. KDD (2016)
Vivek Kulkarni, Bryan Perozzi, Steven Skiena
Freshman or Fresher? Quantifying the Geographic Variation of Language in Online Social Media. ICWSM (2016)
Bryan Perozzi, Leman Akoglu
Scalable Anomaly Ranking of Attributed Neighborhoods. CoRR (2016)
Yingtao Tian, Vivek Kulkarni, Bryan Perozzi, Steven Skiena
On the Convergent Properties of Word Embedding Methods. CoRR (2016)
Bryan Perozzi, Leman Akoglu
Scalable Anomaly Ranking of Attributed Neighborhoods. SDM (2016)
Bryan Perozzi, Vivek Kulkarni, Steven Skiena
Walklets: Multiscale Graph Embeddings for Interpretable Network Classification. CoRR (2016)
Yanqing Chen, Bryan Perozzi, Steven Skiena
Vector-Based Similarity Measurements for Historical Figures. SISAP (2015)
Vivek Kulkarni, Rami Al-Rfou, Bryan Perozzi, Steven Skiena
Statistically Significant Detection of Linguistic Change. WWW (2015)
Bryan Perozzi, Steven Skiena
Exact Age Prediction in Social Networks. WWW (Companion Volume) (2015)
Vivek Kulkarni, Bryan Perozzi, Steven Skiena
Freshman or Fresher? Quantifying the Geographic Variation of Internet Language. CoRR (2015)
Rami Al-Rfou, Vivek Kulkarni, Bryan Perozzi, Steven Skiena
POLYGLOT-NER: Massive Multilingual Named Entity Recognition. SDM (2015)
Bryan Perozzi, Rami Al-Rfou, Steven Skiena
DeepWalk: Online Learning of Social Representations. CoRR (2014)
Rami Al-Rfou, Vivek Kulkarni, Bryan Perozzi, Steven Skiena
POLYGLOT-NER: Massive Multilingual Named Entity Recognition. CoRR (2014)
Bryan Perozzi, Rami Al-Rfou, Steven Skiena
DeepWalk: online learning of social representations. KDD (2014)
Vivek Kulkarni, Rami Al-Rfou', Bryan Perozzi, Steven Skiena
Exploring the power of GPU's for training Polyglot language models. CoRR (2014)
Bryan Perozzi, Christopher McCubbin, James T. Halbert
Scalable graph clustering with parallel approximate PageRank. Soc. Netw. Anal. Min. 4 (1) (2014)
Bryan Perozzi, Rami Al-Rfou', Vivek Kulkarni, Steven Skiena
Inducing Language Networks from Continuous Space Word Representations. CompleNet (2014)
Vivek Kulkarni, Rami Al-Rfou, Bryan Perozzi, Steven Skiena
Statistically Significant Detection of Linguistic Change. CoRR (2014)
Bryan Perozzi, Leman Akoglu, Patricia Iglesias Sánchez, Emmanuel Müller
Focused clustering and outlier detection in large attributed graphs. KDD (2014)

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Publication Activity (10 Years)

Top Topics

Top Venues

Bryan Perozzi