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Bryan Perozzi
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2020 – today
- 2024
- [c45]Bahare Fatemi, Jonathan Halcrow, Bryan Perozzi:
Talk like a Graph: Encoding Graphs for Large Language Models. ICLR 2024 - [c44]Anton Tsitsulin
, Bryan Perozzi
Graph Reasoning with LLMs (GReaL). KDD 2024: 6424-6425 - [i56]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 abs/2402.05862 (2024) - [i55]Jialin Dong, Bahare Fatemi, Bryan Perozzi, Lin F. Yang, Anton Tsitsulin:
Don't Forget to Connect! Improving RAG with Graph-based Reranking. CoRR abs/2405.18414 (2024) - [i54]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 abs/2405.18512 (2024) - [i53]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 abs/2406.09170 (2024) - [i52]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 abs/2406.10727 (2024) - 2023
- [j6]Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller:
Graph Clustering with Graph Neural Networks. J. Mach. Learn. Res. 24: 127:1-127:21 (2023) - [j5]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) - [c43]Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Russ Salakhutdinov:
Graph Generative Model for Benchmarking Graph Neural Networks. ICML 2023: 40175-40198 - [c42]Brandon A. Mayer
HUGE: Huge Unsupervised Graph Embeddings with TPUs. KDD 2023: 4638-4648 - [c41]Bryan Perozzi
Graph Neural Networks in TensorFlow. KDD 2023: 5786-5787 - [c40]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 - [c39]Phitchaya 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 - [c38]Kaize Ding
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer. SIGIR 2023: 2062-2066 - [c37]Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi:
Unsupervised Embedding Quality Evaluation. TAG-ML 2023: 169-188 - [c36]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: 1-10 - [i51]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 abs/2305.12322 (2023) - [i50]Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi:
Unsupervised Embedding Quality Evaluation. CoRR abs/2305.16562 (2023) - [i49]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. CoRR abs/2305.17386 (2023) - [i48]Qi Zhu, Yizhu Jiao, Natalia Ponomareva, Jiawei Han, Bryan Perozzi:
Explaining and Adapting Graph Conditional Shift. CoRR abs/2306.03256 (2023) - [i47]Mustafa Yasir, John Palowitch, Anton Tsitsulin, Long Tran-Thanh, Bryan Perozzi:
Examining the Effects of Degree Distribution and Homophily in Graph Learning Models. CoRR abs/2307.08881 (2023) - [i46]Brandon A. Mayer, Anton Tsitsulin, Hendrik Fichtenberger, Jonathan Halcrow, Bryan Perozzi:
HUGE: Huge Unsupervised Graph Embeddings with TPUs. CoRR abs/2307.14490 (2023) - [i45]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 abs/2308.10737 (2023) - [i44]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 abs/2308.13490 (2023) - [i43]Bahare Fatemi, Jonathan Halcrow, Bryan Perozzi:
Talk like a Graph: Encoding Graphs for Large Language Models. CoRR abs/2310.04560 (2023) - [i42]Anton Tsitsulin, Bryan Perozzi:
The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure. CoRR abs/2312.04762 (2023) - 2022
- [j4]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: 89:1-89:64 (2022) - [c35]John Palowitch, Anton Tsitsulin, Brandon A. Mayer, Bryan Perozzi:
GraphWorld: Fake Graphs Bring Real Insights for GNNs. KDD 2022: 3691-3701 - [c34]Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong:
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. NeurIPS 2022 - [c33]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 - [i41]John Palowitch, Anton Tsitsulin, Brandon A. Mayer, Bryan Perozzi:
GraphWorld: Fake Graphs Bring Real Insights for GNNs. CoRR abs/2203.00112 (2022) - [i40]Minji Yoon, John Palowitch, Dustin Zelle, Ziniu Hu, Ruslan Salakhutdinov, Bryan Perozzi:
Zero-shot Domain Adaptation of Heterogeneous Graphs via Knowledge Transfer Networks. CoRR abs/2203.02018 (2022) - [i39]Anton Tsitsulin, Benedek Rozemberczki, John Palowitch, Bryan Perozzi:
Synthetic Graph Generation to Benchmark Graph Learning. CoRR abs/2204.01376 (2022) - [i38]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 abs/2205.10403 (2022) - [i37]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 A. 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 abs/2207.03522 (2022) - [i36]Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Ruslan Salakhutdinov:
Scalable Privacy-enhanced Benchmark Graph Generative Model for Graph Convolutional Networks. CoRR abs/2207.04396 (2022) - [i35]Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong:
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. CoRR abs/2207.06944 (2022) - [i34]Kimon Fountoulakis, Dake He, Silvio Lattanzi, Bryan Perozzi, Anton Tsitsulin, Shenghao Yang:
On Classification Thresholds for Graph Attention with Edge Features. CoRR abs/2210.10014 (2022) - 2021
- [c32]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 - [c31]Qi Zhu, Natalia Ponomareva, Jiawei Han, Bryan Perozzi:
Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data. NeurIPS 2021: 27965-27977 - [c30]Benedek Rozemberczki, Peter Englert, Amol Kapoor, Martin Blais, Bryan Perozzi:
Pathfinder Discovery Networks for Neural Message Passing. WWW 2021: 2547-2558 - [i33]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 abs/2102.04350 (2021) - [i32]Qi Zhu, Natalia Ponomareva, Jiawei Han, Bryan Perozzi:
Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data. CoRR abs/2108.01099 (2021) - 2020
- [c29]John Palowitch, Bryan Perozzi:
Debiasing Graph Representations via Metadata-Orthogonal Training. ASONAM 2020: 435-442 - [c28]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: 2464-2473 - [c27]Jonathan Halcrow, Alexandru Mosoi, Sam Ruth, Bryan Perozzi:
Grale: Designing Networks for Graph Learning. KDD 2020: 2523-2532 - [c26]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: 655-661 - [c25]Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi:
Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs. WWW 2020: 2697-2703 - [i31]Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi:
Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs. CoRR abs/2003.01282 (2020) - [i30]Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy:
Machine Learning on Graphs: A Model and Comprehensive Taxonomy. CoRR abs/2005.03675 (2020) - [i29]Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller:
Graph Clustering with Graph Neural Networks. CoRR abs/2006.16904 (2020) - [i28]Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann:
Scaling Graph Neural Networks with Approximate PageRank. CoRR abs/2007.01570 (2020) - [i27]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 abs/2007.03113 (2020) - [i26]Jonathan Halcrow, Alexandru Mosoi, Sam Ruth, Bryan Perozzi:
Grale: Designing Networks for Graph Learning. CoRR abs/2007.12002 (2020) - [i25]Stefan Postavaru, Anton Tsitsulin, Filipe Miguel Gonçalves de Almeida, Yingtao Tian, Silvio Lattanzi, Bryan Perozzi:
InstantEmbedding: Efficient Local Node Representations. CoRR abs/2010.06992 (2020) - [i24]Benedek Rozemberczki, Peter Englert, Amol Kapoor, Martin Blais, Bryan Perozzi:
Pathfinder Discovery Networks for Neural Message Passing. CoRR abs/2010.12878 (2020)
2010 – 2019
- 2019
- [c24]Yingtao Tian, Haochen Chen, Bryan Perozzi, Muhao Chen, Xiaofei Sun, Steven Skiena:
Social Relation Inference via Label Propagation. ECIR (1) 2019: 739-746 - [c23]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: 21-29 - [c22]Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee:
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. UAI 2019: 841-851 - [c21]Rami Al-Rfou, Bryan Perozzi, Dustin Zelle:
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels. WWW 2019: 37-48 - [c20]Alessandro Epasto
Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts. WWW 2019: 394-404 - [i23]Rami Al-Rfou, Dustin Zelle, Bryan Perozzi:
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels. CoRR abs/1904.09671 (2019) - [i22]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 abs/1905.00067 (2019) - [i21]Alessandro Epasto, Bryan Perozzi:
Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts. CoRR abs/1905.02138 (2019) - [i20]John Palowitch, Bryan Perozzi:
MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit. CoRR abs/1909.11793 (2019) - 2018
- [j3]Bryan Perozzi, Leman Akoglu:
Discovering Communities and Anomalies in Attributed Graphs: Interactive Visual Exploration and Summarization. ACM Trans. Knowl. Discov. Data 12(2): 24:1-24:40 (2018) - [c19]Haochen Chen, Bryan Perozzi, Yifan Hu, Steven Skiena
HARP: Hierarchical Representation Learning for Networks. AAAI 2018: 2127-2134 - [c18]Haochen Chen, Xiaofei Sun, Yingtao Tian, Bryan Perozzi, Muhao Chen, Steven Skiena
Enhanced Network Embeddings via Exploiting Edge Labels. CIKM 2018: 1579-1582 - [c17]Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alexander A. Alemi:
Watch Your Step: Learning Node Embeddings via Graph Attention. NeurIPS 2018: 9198-9208 - [i19]Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee:
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. CoRR abs/1802.08888 (2018) - [i18]Haochen Chen, Bryan Perozzi, Rami Al-Rfou, Steven Skiena:
A Tutorial on Network Embeddings. CoRR abs/1808.02590 (2018) - [i17]Haochen Chen, Xiaofei Sun, Yingtao Tian, Bryan Perozzi, Muhao Chen, Steven Skiena:
Enhanced Network Embeddings via Exploiting Edge Labels. CoRR abs/1809.05124 (2018) - 2017
- [j2]Yanqing Chen, Bryan Perozzi, Steven Skiena
Vector-based similarity measurements for historical figures. Inf. Syst. 64: 163-174 (2017) - [c16]Bryan Perozzi, Vivek Kulkarni, Haochen Chen, Steven Skiena:
Don't Walk, Skip!: Online Learning of Multi-scale Network Embeddings. ASONAM 2017: 258-265 - [c15]Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou:
Learning Edge Representations via Low-Rank Asymmetric Projections. CIKM 2017: 1787-1796 - [c14]Aria Rezaei, Bryan Perozzi, Leman Akoglu:
Ties That Bind: Characterizing Classes by Attributes and Social Ties. WWW (Companion Volume) 2017: 973-981 - [i16]Aria Rezaei, Bryan Perozzi, Leman Akoglu:
Ties That Bind - Characterizing Classes by Attributes and Social Ties. CoRR abs/1701.09039 (2017) - [i15]Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou:
Learning Edge Representations via Low-Rank Asymmetric Projections. CoRR abs/1705.05615 (2017) - [i14]Haochen Chen, Bryan Perozzi, Yifan Hu, Steven Skiena:
HARP: Hierarchical Representation Learning for Networks. CoRR abs/1706.07845 (2017) - [i13]Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alex Alemi:
Watch Your Step: Learning Graph Embeddings Through Attention. CoRR abs/1710.09599 (2017) - [i12]Eduardo Fleury, Silvio Lattanzi, Vahab S. Mirrokni, Bryan Perozzi:
ASYMP: Fault-tolerant Mining of Massive Graphs. CoRR abs/1712.09731 (2017) - 2016
- [c13]Vivek Kulkarni, Bryan Perozzi, Steven Skiena:
Freshman or Fresher? Quantifying the Geographic Variation of Language in Online Social Media. ICWSM 2016: 615-618 - [c12]Bryan Perozzi, Michael Schueppert, Jack Saalweachter, Mayur Thakur:
When Recommendation Goes Wrong: Anomalous Link Discovery in Recommendation Networks. KDD 2016: 569-578 - [c11]Bryan Perozzi, Leman Akoglu:
Scalable Anomaly Ranking of Attributed Neighborhoods. SDM 2016: 207-215 - [i11]Bryan Perozzi, Leman Akoglu:
Scalable Anomaly Ranking of Attributed Neighborhoods. CoRR abs/1601.06711 (2016) - [i10]Bryan Perozzi, Vivek Kulkarni, Steven Skiena:
Walklets: Multiscale Graph Embeddings for Interpretable Network Classification. CoRR abs/1605.02115 (2016) - [i9]Yingtao Tian, Vivek Kulkarni, Bryan Perozzi, Steven Skiena:
On the Convergent Properties of Word Embedding Methods. CoRR abs/1605.03956 (2016) - 2015
- [c10]Rami Al-Rfou, Vivek Kulkarni, Bryan Perozzi, Steven Skiena:
POLYGLOT-NER: Massive Multilingual Named Entity Recognition. SDM 2015: 586-594 - [c9]Yanqing Chen, Bryan Perozzi, Steven Skiena:
Vector-Based Similarity Measurements for Historical Figures. SISAP 2015: 179-190 - [c8]Bryan Perozzi, Steven Skiena:
Exact Age Prediction in Social Networks. WWW (Companion Volume) 2015: 91-92 - [c7]Vivek Kulkarni, Rami Al-Rfou, Bryan Perozzi, Steven Skiena:
Statistically Significant Detection of Linguistic Change. WWW 2015: 625-635 - [i8]Vivek Kulkarni, Bryan Perozzi, Steven Skiena:
Freshman or Fresher? Quantifying the Geographic Variation of Internet Language. CoRR abs/1510.06786 (2015) - 2014
- [j1]Bryan Perozzi, Christopher McCubbin, James T. Halbert:
Scalable graph clustering with parallel approximate PageRank. Soc. Netw. Anal. Min. 4(1): 179 (2014) - [c6]Bryan Perozzi, Rami Al-Rfou', Vivek Kulkarni, Steven Skiena:
Inducing Language Networks from Continuous Space Word Representations. CompleNet 2014: 261-273 - [c5]Bryan Perozzi, Rami Al-Rfou, Steven Skiena:
DeepWalk: online learning of social representations. KDD 2014: 701-710 - [c4]Bryan Perozzi, Leman Akoglu, Patricia Iglesias Sánchez, Emmanuel Müller
Focused clustering and outlier detection in large attributed graphs. KDD 2014: 1346-1355 - [i7]Bryan Perozzi, Rami Al-Rfou, Vivek Kulkarni, Steven Skiena:
Inducing Language Networks from Continuous Space Word Representations. CoRR abs/1403.1252 (2014) - [i6]Bryan Perozzi, Rami Al-Rfou, Steven Skiena:
DeepWalk: Online Learning of Social Representations. CoRR abs/1403.6652 (2014) - [i5]Vivek Kulkarni, Rami Al-Rfou', Bryan Perozzi, Steven Skiena:
Exploring the power of GPU's for training Polyglot language models. CoRR abs/1404.1521 (2014) - [i4]Rami Al-Rfou, Vivek Kulkarni, Bryan Perozzi, Steven Skiena:
POLYGLOT-NER: Massive Multilingual Named Entity Recognition. CoRR abs/1410.3791 (2014) - [i3]Vivek Kulkarni, Rami Al-Rfou, Bryan Perozzi, Steven Skiena:
Statistically Significant Detection of Linguistic Change. CoRR abs/1411.3315 (2014) - 2013
- [c3]Bryan Perozzi, Christopher McCubbin, Spencer Beecher, James T. Halbert:
Scalable Graph Clustering with Pregel. CompleNet 2013: 133-144 - [c2]Rami Al-Rfou', Bryan Perozzi, Steven Skiena:
Polyglot: Distributed Word Representations for Multilingual NLP. CoNLL 2013: 183-192 - [i2]Yanqing Chen, Bryan Perozzi, Rami Al-Rfou', Steven Skiena:
The Expressive Power of Word Embeddings. CoRR abs/1301.3226 (2013) - [i1]Rami Al-Rfou, Bryan Perozzi, Steven Skiena:
Polyglot: Distributed Word Representations for Multilingual NLP. CoRR abs/1307.1662 (2013) - 2011
- [c1]Christopher McCubbin, Bryan Perozzi, Andrew Levine, Abdul Rahman:
Finding the 'Needle': Locating Interesting Nodes Using the K-shortest Paths Algorithm in MapReduce. ICDM Workshops 2011: 180-187
Coauthor Index
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