1 code implementation • 15 Jun 2024 • Zhikai Chen, Haitao Mao, Jingzhe Liu, Yu Song, Bingheng Li, Wei Jin, Bahare Fatemi, Anton Tsitsulin, Bryan Perozzi, Hui Liu, Jiliang Tang

First, the absence of a comprehensive benchmark with unified problem settings hinders a clear understanding of the comparative effectiveness and practical value of different text-space GFMs.

no code implementations • 13 Jun 2024 • Bahare Fatemi, Mehran Kazemi, Anton Tsitsulin, Karishma Malkan, Jinyeong Yim, John Palowitch, Sungyong Seo, Jonathan Halcrow, Bryan Perozzi

Large language models (LLMs) have showcased remarkable reasoning capabilities, yet they remain susceptible to errors, particularly in temporal reasoning tasks involving complex temporal logic.

no code implementations • 28 May 2024 • Clayton Sanford, Bahare Fatemi, Ethan Hall, Anton Tsitsulin, Mehran Kazemi, Jonathan Halcrow, Bryan Perozzi, Vahab Mirrokni

Our novel representational hierarchy separates 9 algorithmic reasoning problems into classes solvable by transformers in different realistic parameter scaling regimes.

no code implementations • 28 May 2024 • Jialin Dong, Bahare Fatemi, Bryan Perozzi, Lin F. Yang, Anton Tsitsulin

Retrieval Augmented Generation (RAG) has greatly improved the performance of Large Language Model (LLM) responses by grounding generation with context from existing documents.

no code implementations • 8 Feb 2024 • Bryan Perozzi, Bahare Fatemi, Dustin Zelle, Anton Tsitsulin, Mehran Kazemi, Rami Al-Rfou, Jonathan Halcrow

How can we best encode structured data into sequential form for use in large language models (LLMs)?

no code implementations • 8 Dec 2023 • Anton Tsitsulin, Bryan Perozzi

Subsequently, we define the notion of a "winning ticket" for graph structure - an extremely sparse subset of edges that can deliver a robust approximation of the entire graph's performance.

no code implementations • 6 Oct 2023 • Bahare Fatemi, Jonathan Halcrow, Bryan Perozzi

Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications such as social networks, recommender systems, and computational finance.

1 code implementation • NeurIPS 2023 • Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao, Bahare Fatemi, Mike Burrows, Charith Mendis, Bryan Perozzi

TpuGraphs provides 25x more graphs than the largest graph property prediction dataset (with comparable graph sizes), and 770x larger graphs on average compared to existing performance prediction datasets on machine learning programs.

Ranked #2 on Runtime ranking on TpuGraphs Layout mean

1 code implementation • 21 Aug 2023 • Bahare Fatemi, Sami Abu-El-Haija, Anton Tsitsulin, Mehran Kazemi, Dustin Zelle, Neslihan Bulut, Jonathan Halcrow, Bryan Perozzi

We implement a wide range of existing models in our framework and conduct extensive analyses of the effectiveness of different components in the framework.

no code implementations • 26 Jul 2023 • Brandon Mayer, Anton Tsitsulin, Hendrik Fichtenberger, Jonathan Halcrow, Bryan Perozzi

A high-performance graph embedding architecture leveraging Tensor Processing Units (TPUs) with configurable amounts of high-bandwidth memory is presented that simplifies the graph embedding problem and can scale to graphs with billions of nodes and trillions of edges.

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.

no code implementations • 5 Jun 2023 • Qi Zhu, Yizhu Jiao, Natalia Ponomareva, Jiawei Han, Bryan Perozzi

Graph Neural Networks (GNNs) have shown remarkable performance on graph-structured data.

no code implementations • 26 May 2023 • Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi

Unsupervised learning has recently significantly gained in popularity, especially with deep learning-based approaches.

1 code implementation • NeurIPS 2023 • Kaidi Cao, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Dustin Zelle, Yanqi Zhou, Charith Mendis, Jure Leskovec, Bryan Perozzi

Here we propose Graph Segment Training (GST), a general framework that utilizes a divide-and-conquer approach to allow learning large graph property prediction with a constant memory footprint.

no code implementations • 18 Oct 2022 • Kimon Fountoulakis, Dake He, Silvio Lattanzi, Bryan Perozzi, Anton Tsitsulin, Shenghao Yang

In CSBM the nodes and edge features are obtained from a mixture of Gaussians and the edges from a stochastic block model.

1 code implementation • 14 Jul 2022 • Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong

Personalized PageRank (PPR) is a fundamental tool in unsupervised learning of graph representations such as node ranking, labeling, and graph embedding.

1 code implementation • 10 Jul 2022 • Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Ruslan Salakhutdinov

As the field of Graph Neural Networks (GNN) continues to grow, it experiences a corresponding increase in the need for large, real-world datasets to train and test new GNN models on challenging, realistic problems.

1 code implementation • 7 Jul 2022 • Oleksandr Ferludin, Arno Eigenwillig, Martin Blais, Dustin Zelle, Jan Pfeifer, Alvaro Sanchez-Gonzalez, Wai Lok Sibon Li, Sami Abu-El-Haija, Peter Battaglia, Neslihan Bulut, Jonathan Halcrow, Filipe Miguel Gonçalves de Almeida, Pedro Gonnet, Liangze Jiang, Parth Kothari, Silvio Lattanzi, André Linhares, Brandon Mayer, Vahab Mirrokni, John Palowitch, Mihir Paradkar, Jennifer She, Anton Tsitsulin, Kevin Villela, Lisa Wang, David Wong, Bryan Perozzi

TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow.

1 code implementation • 20 May 2022 • Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari, Mohammadhossein Bateni, Deepak Ramachandran, Bryan Perozzi, Vahab Mirrokni

Representative Selection (RS) is the problem of finding a small subset of exemplars from a dataset that is representative of the dataset.

1 code implementation • 4 Apr 2022 • Anton Tsitsulin, Benedek Rozemberczki, John Palowitch, Bryan Perozzi

This shockingly small sample size (~10) allows for only limited scientific insight into the problem.

1 code implementation • 3 Mar 2022 • Minji Yoon, John Palowitch, Dustin Zelle, Ziniu Hu, Ruslan Salakhutdinov, Bryan Perozzi

We propose a zero-shot transfer learning module for HGNNs called a Knowledge Transfer Network (KTN) that transfers knowledge from label-abundant node types to zero-labeled node types through rich relational information given in the HG.

1 code implementation • 28 Feb 2022 • John Palowitch, Anton Tsitsulin, Brandon Mayer, Bryan Perozzi

Using GraphWorld, a user has fine-grained control over graph generator parameters, and can benchmark arbitrary GNN models with built-in hyperparameter tuning.

1 code implementation • NeurIPS 2021 • Qi Zhu, Natalia Ponomareva, Jiawei Han, Bryan Perozzi

In this work we present a method, Shift-Robust GNN (SR-GNN), designed to account for distributional differences between biased training data and the graph's true inference distribution.

1 code implementation • ICLR 2021 • Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Bryan Perozzi, Greg Ver Steeg, Aram Galstyan

We propose Graph Traversal via Tensor Functionals(GTTF), a unifying meta-algorithm framework for easing the implementation of diverse graph algorithms and enabling transparent and efficient scaling to large graphs.

1 code implementation • 24 Oct 2020 • Benedek Rozemberczki, Peter Englert, Amol Kapoor, Martin Blais, Bryan Perozzi

Additional results from a challenging suite of node classification experiments show how PDNs can learn a wider class of functions than existing baselines.

no code implementations • 14 Oct 2020 • Ştefan Postăvaru, Anton Tsitsulin, Filipe Miguel Gonçalves de Almeida, Yingtao Tian, Silvio Lattanzi, Bryan Perozzi

In this paper, we introduce InstantEmbedding, an efficient method for generating single-node representations using local PageRank computations.

no code implementations • 23 Jul 2020 • Jonathan Halcrow, Alexandru Moşoi, Sam Ruth, Bryan Perozzi

Interestingly, there are often many types of similarity available to choose as the edges between nodes, and the choice of edges can drastically affect the performance of downstream semi-supervised learning systems.

1 code implementation • 6 Jul 2020 • Amol Kapoor, Xue Ben, Luyang Liu, Bryan Perozzi, Matt Barnes, Martin Blais, Shawn O'Banion

In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data.

2 code implementations • 3 Jul 2020 • Aleksandar Bojchevski, Johannes Gasteiger, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann

Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks.

no code implementations • NeurIPS 2023 • Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller

Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction.

1 code implementation • 7 May 2020 • Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy

The second, graph regularized neural networks, leverages graphs to augment neural network losses with a regularization objective for semi-supervised learning.

no code implementations • 3 Mar 2020 • Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi

Graph comparison is a fundamental operation in data mining and information retrieval.

no code implementations • 25 Sep 2019 • John Palowitch, Bryan Perozzi

In this paper, we show that when metadata is correlated with the formation of node neighborhoods, unsupervised node embedding dimensions learn this metadata.

2 code implementations • 6 May 2019 • Alessandro Epasto, Bryan Perozzi

Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph.

3 code implementations • 30 Apr 2019 • Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan

Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships.

1 code implementation • 21 Apr 2019 • Rami Al-Rfou, Dustin Zelle, Bryan Perozzi

Second, for each pair of graphs, we train a cross-graph attention network which uses the node representations of an anchor graph to reconstruct another graph.

Ranked #3 on Graph Classification on D&D

1 code implementation • WWW 2019 • Alessandro Epasto, Bryan Perozzi

Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph.

1 code implementation • 13 Sep 2018 • Haochen Chen, Xiaofei Sun, Yingtao Tian, Bryan Perozzi, Muhao Chen, Steven Skiena

Network embedding methods aim at learning low-dimensional latent representation of nodes in a network.

Social and Information Networks Physics and Society

2 code implementations • 8 Aug 2018 • Haochen Chen, Bryan Perozzi, Rami Al-Rfou, Steven Skiena

We further demonstrate the applications of network embeddings, and conclude the survey with future work in this area.

Social and Information Networks

1 code implementation • 24 Feb 2018 • Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee

Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data.

Ranked #41 on Node Classification on Pubmed

no code implementations • ICLR 2018 • Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee

Graph Convolutional Networks (GCNs) are a recently proposed architecture which has had success in semi-supervised learning on graph-structured data.

2 code implementations • NeurIPS 2018 • Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alex Alemi

Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e. g. by sampling random walks).

Ranked #67 on Node Classification on Citeseer

3 code implementations • 23 Jun 2017 • 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.

Social and Information Networks

1 code implementation • 16 May 2017 • Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou

Individually, both of these contributions improve the learned representations, especially when there are memory constraints on the total size of the embeddings.

no code implementations • 12 May 2016 • Yingtao Tian, Vivek Kulkarni, Bryan Perozzi, Steven Skiena

Do word embeddings converge to learn similar things over different initializations?

2 code implementations • 6 May 2016 • Bryan Perozzi, Vivek Kulkarni, Haochen Chen, Steven Skiena

We present Walklets, a novel approach for learning multiscale representations of vertices in a network.

Social and Information Networks Physics and Society

2 code implementations • 22 Oct 2015 • Vivek Kulkarni, Bryan Perozzi, Steven Skiena

Our analysis of British and American English over a period of 100 years reveals that semantic variation between these dialects is shrinking.

no code implementations • 12 Nov 2014 • Vivek Kulkarni, Rami Al-Rfou, Bryan Perozzi, Steven Skiena

We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words.

no code implementations • 14 Oct 2014 • Rami Al-Rfou, Vivek Kulkarni, Bryan Perozzi, Steven Skiena

We describe a system that builds Named Entity Recognition (NER) annotators for 40 major languages using Wikipedia and Freebase.

no code implementations • 5 Apr 2014 • Vivek Kulkarni, Rami Al-Rfou', Bryan Perozzi, Steven Skiena

We evaluate the performance of training the model on the GPU and present optimizations that boost the performance on the GPU. One of the key optimizations, we propose increases the performance of a function involved in calculating and updating the gradient by approximately 50 times on the GPU for sufficiently large batch sizes.

13 code implementations • 26 Mar 2014 • Bryan Perozzi, Rami Al-Rfou, Steven Skiena

We present DeepWalk, a novel approach for learning latent representations of vertices in a network.

Ranked #1 on Link Property Prediction on ogbl-ppa

no code implementations • 6 Mar 2014 • Bryan Perozzi, Rami Al-Rfou, Vivek Kulkarni, Steven Skiena

Recent advancements in unsupervised feature learning have developed powerful latent representations of words.

no code implementations • WS 2013 • Rami Al-Rfou, Bryan Perozzi, Steven Skiena

We quantitatively demonstrate the utility of our word embeddings by using them as the sole features for training a part of speech tagger for a subset of these languages.

no code implementations • 15 Jan 2013 • Yanqing Chen, Bryan Perozzi, Rami Al-Rfou, Steven Skiena

We seek to better understand the difference in quality of the several publicly released embeddings.

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