Finding the 'Needle': Locating Interesting Nodes Using the K-shortest Paths Algorithm in MapReduce. - Christopher McCubbin; Bryan Perozzi; Andrew Levine; Abdul Rahman (ICDM Workshops, 2011)

Scalable Graph Clustering with Pregel. - Bryan Perozzi; Christopher McCubbin; Spencer Beecher; James T. Halbert (CompleNet, 2013)

Polyglot: Distributed Word Representations for Multilingual NLP. - Rami Al-Rfou'; Bryan Perozzi; Steven Skiena (CoNLL, 2013)

The Expressive Power of Word Embeddings - Yanqing Chen; Bryan Perozzi; Rami Al-Rfou'; Steven Skiena (CoRR, 2013)

Inducing Language Networks from Continuous Space Word Representations. - Bryan Perozzi; Rami Al-Rfou'; Vivek Kulkarni; Steven Skiena (CompleNet, 2014)

DeepWalk: online learning of social representations. - Bryan Perozzi; Rami Al-Rfou; Steven Skiena (KDD, 2014)

Focused clustering and outlier detection in large attributed graphs. - Bryan Perozzi; Leman Akoglu; Patricia Iglesias Sánchez; Emmanuel Müller (KDD, 2014)

Exploring the power of GPU's for training Polyglot language models. - Vivek Kulkarni; Rami Al-Rfou'; Bryan Perozzi; Steven Skiena (CoRR, 2014)

Scalable graph clustering with parallel approximate PageRank. - Bryan Perozzi; Christopher McCubbin; James T. Halbert (Soc. Netw. Anal. Min., 2014)

POLYGLOT-NER: Massive Multilingual Named Entity Recognition. - Rami Al-Rfou; Vivek Kulkarni; Bryan Perozzi; Steven Skiena (SDM, 2015)

Statistically Significant Detection of Linguistic Change. - Vivek Kulkarni; Rami Al-Rfou; Bryan Perozzi; Steven Skiena (WWW, 2015)

Exact Age Prediction in Social Networks. - Bryan Perozzi; Steven Skiena (WWW (Companion Volume), 2015)

Freshman or Fresher? Quantifying the Geographic Variation of Internet Language. - Vivek Kulkarni; Bryan Perozzi; Steven Skiena (CoRR, 2015)

Freshman or Fresher? Quantifying the Geographic Variation of Language in Online Social Media. - Vivek Kulkarni; Bryan Perozzi; Steven Skiena (ICWSM, 2016)

When Recommendation Goes Wrong: Anomalous Link Discovery in Recommendation Networks. - Bryan Perozzi; Michael Schueppert; Jack Saalweachter; Mayur Thakur (KDD, 2016)

Scalable Anomaly Ranking of Attributed Neighborhoods. - Bryan Perozzi; Leman Akoglu (SDM, 2016)

Walklets: Multiscale Graph Embeddings for Interpretable Network Classification. - Bryan Perozzi; Vivek Kulkarni; Steven Skiena (CoRR, 2016)

On the Convergent Properties of Word Embedding Methods. - Yingtao Tian; Vivek Kulkarni; Bryan Perozzi; Steven Skiena (CoRR, 2016)

Don't Walk, Skip!: Online Learning of Multi-scale Network Embeddings. - Bryan Perozzi; Vivek Kulkarni; Haochen Chen; Steven Skiena (ASONAM, 2017)

Learning Edge Representations via Low-Rank Asymmetric Projections. - Sami Abu-El-Haija; Bryan Perozzi; Rami Al-Rfou (CIKM, 2017)

Ties That Bind: Characterizing Classes by Attributes and Social Ties. - Aria Rezaei; Bryan Perozzi; Leman Akoglu (WWW (Companion Volume), 2017)

Watch Your Step: Learning Graph Embeddings Through Attention. - Sami Abu-El-Haija; Bryan Perozzi; Rami Al-Rfou; Alex Alemi (CoRR, 2017)

ASYMP: Fault-tolerant Mining of Massive Graphs. - Eduardo Fleury; Silvio Lattanzi; Vahab S. Mirrokni; Bryan Perozzi (CoRR, 2017)

Vector-based similarity measurements for historical figures. - Yanqing Chen; Bryan Perozzi; Steven Skiena (Inf. Syst., 2017)

HARP: Hierarchical Representation Learning for Networks. - Haochen Chen; Bryan Perozzi; Yifan Hu 001; Steven Skiena (AAAI, 2018)

Enhanced Network Embeddings via Exploiting Edge Labels. - Haochen Chen; Xiaofei Sun; Yingtao Tian; Bryan Perozzi; Muhao Chen 001; Steven Skiena (CIKM, 2018)

Watch Your Step: Learning Node Embeddings via Graph Attention. - Sami Abu-El-Haija; Bryan Perozzi; Rami Al-Rfou; Alexander A. Alemi (NeurIPS, 2018)

A Tutorial on Network Embeddings. - Haochen Chen; Bryan Perozzi; Rami Al-Rfou; Steven Skiena (CoRR, 2018)

Discovering Communities and Anomalies in Attributed Graphs: Interactive Visual Exploration and Summarization. - Bryan Perozzi; Leman Akoglu (ACM Trans. Knowl. Discov. Data, 2018)

Social Relation Inference via Label Propagation. - Yingtao Tian; Haochen Chen; Bryan Perozzi; Muhao Chen 001; Xiaofei Sun; Steven Skiena (ECIR (1), 2019)

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 (ICML, 2019)

N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. - Sami Abu-El-Haija; Amol Kapoor; Bryan Perozzi; Joonseok Lee (UAI, 2019)

DDGK: Learning Graph Representations for Deep Divergence Graph Kernels. - Rami Al-Rfou; Bryan Perozzi; Dustin Zelle (WWW, 2019)

Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts. - Alessandro Epasto; Bryan Perozzi (WWW, 2019)

MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit. - John Palowitch; Bryan Perozzi (CoRR, 2019)

Debiasing Graph Representations via Metadata-Orthogonal Training. - John Palowitch; Bryan Perozzi (ASONAM, 2020)

Scaling Graph Neural Networks with Approximate PageRank. - Aleksandar Bojchevski; Johannes Klicpera; Bryan Perozzi; Amol Kapoor; Martin Blais; Benedek Rózemberczki; Michal Lukasik; Stephan Günnemann (KDD, 2020)

Grale: Designing Networks for Graph Learning. - Jonathan Halcrow; Alexandru Mosoi; Sam Ruth; Bryan Perozzi (KDD, 2020)

Graph Embedding with Personalized Context Distribution. - Di Huang; Zihao He; Yuzhong Huang; Kexuan Sun 002; Sami Abu-El-Haija; Bryan Perozzi; Kristina Lerman; Fred Morstatter; Aram Galstyan (WWW (Companion Volume), 2020)

Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs. - Anton Tsitsulin; Marina Munkhoeva; Bryan Perozzi (WWW, 2020)

Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks. - Amol Kapoor; Xue Ben; Luyang Liu; Bryan Perozzi; Matt Barnes 001; Martin Blais; Shawn O'Banion (CoRR, 2020)

InstantEmbedding: Efficient Local Node Representations. - Stefan Postavaru; Anton Tsitsulin; Filipe Miguel Gonçalves de Almeida; Yingtao Tian; Silvio Lattanzi; Bryan Perozzi (CoRR, 2020)

Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning. - Elan Sopher Markowitz; Keshav Balasubramanian; Mehrnoosh Mirtaheri; Sami Abu-El-Haija; Bryan Perozzi; Greg Ver Steeg; Aram Galstyan (ICLR, 2021)

Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data. - Qi Zhu 008; Natalia Ponomareva 001; Jiawei Han 001; Bryan Perozzi (NeurIPS, 2021)

Pathfinder Discovery Networks for Neural Message Passing. - Benedek Rozemberczki; Peter Englert; Amol Kapoor; Martin Blais; Bryan Perozzi (WWW, 2021)

GraphWorld: Fake Graphs Bring Real Insights for GNNs. - John Palowitch; Anton Tsitsulin; Brandon A. Mayer; Bryan Perozzi (KDD, 2022)

Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. - Alessandro Epasto; Vahab Mirrokni; Bryan Perozzi; Anton Tsitsulin; Peilin Zhong (NeurIPS, 2022)

Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks. - Minji Yoon; John Palowitch; Dustin Zelle; Ziniu Hu; Ruslan Salakhutdinov; Bryan Perozzi (NeurIPS, 2022)

Zero-shot Domain Adaptation of Heterogeneous Graphs via Knowledge Transfer Networks. - Minji Yoon; John Palowitch; Dustin Zelle; Ziniu Hu; Ruslan Salakhutdinov; Bryan Perozzi (CoRR, 2022)

Synthetic Graph Generation to Benchmark Graph Learning. - Anton Tsitsulin; Benedek Rozemberczki; John Palowitch; Bryan Perozzi (CoRR, 2022)

TF-GNN: Graph Neural Networks in TensorFlow. - 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; et al. (CoRR, 2022)

Scalable Privacy-enhanced Benchmark Graph Generative Model for Graph Convolutional Networks. - Minji Yoon; Yue Wu 001; John Palowitch; Bryan Perozzi; Ruslan Salakhutdinov (CoRR, 2022)

On Classification Thresholds for Graph Attention with Edge Features. - Kimon Fountoulakis; Dake He; Silvio Lattanzi; Bryan Perozzi; Anton Tsitsulin; Shenghao Yang 002 (CoRR, 2022)

Machine Learning on Graphs: A Model and Comprehensive Taxonomy. - Ines Chami; Sami Abu-El-Haija; Bryan Perozzi; Christopher Ré; Kevin Murphy 002 (J. Mach. Learn. Res., 2022)

Graph Generative Model for Benchmarking Graph Neural Networks. - Minji Yoon; Yue Wu 001; John Palowitch; Bryan Perozzi; Russ Salakhutdinov (ICML, 2023)

HUGE: Huge Unsupervised Graph Embeddings with TPUs. - Brandon A. Mayer; Anton Tsitsulin; Hendrik Fichtenberger; Jonathan Halcrow; Bryan Perozzi (KDD, 2023)

Graph Neural Networks in TensorFlow. - Bryan Perozzi; Sami Abu-El-Haija; Anton Tsitsulin (KDD, 2023)

Learning Large Graph Property Prediction via Graph Segment Training. - Kaidi Cao; Phitchaya Mangpo Phothilimthana; Sami Abu-El-Haija; Dustin Zelle; Yanqi Zhou; Charith Mendis; Jure Leskovec; Bryan Perozzi (NeurIPS, 2023)

TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs. - Phitchaya Mangpo Phothilimthana; Sami Abu-El-Haija; Kaidi Cao; Bahare Fatemi; Michael Burrows; Charith Mendis; Bryan Perozzi (NeurIPS, 2023)

HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer. - Kaize Ding; Albert Jiongqian Liang; Bryan Perozzi; Ting Chen; Ruoxi Wang; Lichan Hong; Ed H. Chi; Huan Liu 001; Derek Zhiyuan Cheng (SIGIR, 2023)

Unsupervised Embedding Quality Evaluation. - Anton Tsitsulin; Marina Munkhoeva; Bryan Perozzi (TAG-ML, 2023)

SubMix: Learning to Mix Graph Sampling Heuristics. - Sami Abu-El-Haija; Joshua V. Dillon; Bahare Fatemi; Kyriakos Axiotis; Neslihan Bulut; Johannes Gasteiger; Bryan Perozzi; MohammadHossein Bateni (UAI, 2023)

Explaining and Adapting Graph Conditional Shift. - Qi Zhu 008; Yizhu Jiao; Natalia Ponomareva 001; Jiawei Han 001; Bryan Perozzi (CoRR, 2023)

Examining the Effects of Degree Distribution and Homophily in Graph Learning Models. - Mustafa Yasir; John Palowitch; Anton Tsitsulin; Long Tran-Thanh; Bryan Perozzi (CoRR, 2023)

UGSL: A Unified Framework for Benchmarking Graph Structure Learning. - Bahare Fatemi; Sami Abu-El-Haija; Anton Tsitsulin; Seyed Mehran Kazemi; Dustin Zelle; Neslihan Bulut; Jonathan Halcrow; Bryan Perozzi (CoRR, 2023)

TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs. - Phitchaya Mangpo Phothilimthana; Sami Abu-El-Haija; Kaidi Cao; Bahare Fatemi; Charith Mendis; Bryan Perozzi (CoRR, 2023)

The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure. - Anton Tsitsulin; Bryan Perozzi (CoRR, 2023)

Graph Clustering with Graph Neural Networks. - Anton Tsitsulin; John Palowitch; Bryan Perozzi; Emmanuel Müller (J. Mach. Learn. Res., 2023)

Tackling Provably Hard Representative Selection via Graph Neural Networks. - Mehran Kazemi; Anton Tsitsulin; Hossein Esfandiari; MohammadHossein Bateni; Deepak Ramachandran; Bryan Perozzi; Vahab Mirrokni (Trans. Mach. Learn. Res., 2023)

Talk like a Graph: Encoding Graphs for Large Language Models. - Bahare Fatemi; Jonathan Halcrow; Bryan Perozzi (ICLR, 2024)

Graph Reasoning with LLMs (GReaL). - Anton Tsitsulin; Bryan Perozzi; Bahare Fatemi; Jonathan J. Halcrow (KDD, 2024)

Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights. - Zhikai Chen; Haitao Mao; Jingzhe Liu; Yu Song; Bingheng Li; Wei Jin 009; Bahare Fatemi; Anton Tsitsulin; Bryan Perozzi; Hui Liu 031; Jiliang Tang (NeurIPS, 2024)

Understanding Transformer Reasoning Capabilities via Graph Algorithms. - Clayton Sanford; Bahare Fatemi; Ethan Hall; Anton Tsitsulin; Mehran Kazemi; Jonathan Halcrow; Bryan Perozzi; Vahab Mirrokni (NeurIPS, 2024)

Let Your Graph Do the Talking: Encoding Structured Data for LLMs. - Bryan Perozzi; Bahare Fatemi; Dustin Zelle; Anton Tsitsulin; Seyed Mehran Kazemi; Rami Al-Rfou; Jonathan Halcrow (CoRR, 2024)

Don't Forget to Connect! Improving RAG with Graph-based Reranking. - Jialin Dong; Bahare Fatemi; Bryan Perozzi; Lin F. Yang; Anton Tsitsulin (CoRR, 2024)

General Geospatial Inference with a Population Dynamics Foundation Model. - Mohit Agarwal; Mimi Sun; Chaitanya Kamath; Arbaaz Muslim; Prithul Sarker; Joydeep Paul; Hector Yee; Marcin Sieniek; Kim Philipp Jablonski; Yael Mayer; David Fork; Sheila de Guia; et al. (CoRR, 2024)

Best of Both Worlds: Advantages of Hybrid Graph Sequence Models. - Ali Behrouz; Ali Parviz; Mahdi Karami; Clayton Sanford; Bryan Perozzi; Vahab Mirrokni (CoRR, 2024)

Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning. - Bahare Fatemi; Mehran Kazemi; Anton Tsitsulin; Karishma Malkan; Jinyeong Yim; John Palowitch; Sungyong Seo; Jonathan Halcrow; Bryan Perozzi (ICLR, 2025)

Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks. - Maya Bechler-Speicher; Ben Finkelshtein; Fabrizio Frasca; Luis Müller; Jan Tönshoff; Antoine Siraudin; Viktor Zaverkin; Michael M. Bronstein; Mathias Niepert; Bryan Perozzi; Mikhail Galkin 001; Christopher Morris 001 (CoRR, 2025)

AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs. - Florian Grötschla; Luis Müller; Jan Tönshoff; Mikhail Galkin 001; Bryan Perozzi (CoRR, 2025)

Large-Scale Graph Building in Dynamic Environments: Low Latency and High Quality. - Filipe Miguel Gonçalves de Almeida; CJ Carey; Hendrik Fichtenberger; Jonathan Halcrow; Silvio Lattanzi; André Linhares; Tao Meng; Ashkan Norouzi-Fard; Nikos Parotsidis; Bryan Perozzi; David Simcha (CoRR, 2025)