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no code implementations • 22 Feb 2024 • Rex Ying, Tianyu Fu, Andrew Wang, Jiaxuan You, Yu Wang, Jure Leskovec

SPMiner combines graph neural networks, order embedding space, and an efficient search strategy to identify network subgraph patterns that appear most frequently in the target graph.

1 code implementation • 10 Jan 2024 • Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric Xing, Furong Huang, Hao liu, Heng Ji, Hongyi Wang, huan zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, John Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, Yue Zhao

This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.

no code implementations • 7 Dec 2023 • Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec

The core idea is to view relational databases as a temporal, heterogeneous graph, with a node for each row in each table, and edges specified by primary-foreign key links.

no code implementations • 2 Dec 2023 • Weikang Qiu, Huangrui Chu, Selena Wang, Haolan Zuo, Xiaoxiao Li, Yize Zhao, Rex Ying

In response to this gap, we propose a novel method named HYBRID which aims to extract MIMR high-order relationships from fMRI data.

no code implementations • 9 Nov 2023 • Jialin Chen, Kenza Amara, Junchi Yu, Rex Ying

Graph Neural Networks (GNNs) achieve state-of-the-art performance in various graph-related tasks.

no code implementations • 9 Nov 2023 • Jialin Chen, Yuelin Wang, Cristian Bodnar, Rex Ying, Pietro Lio, Yu Guang Wang

However, recursively aggregating neighboring information with graph convolutions leads to indistinguishable node features in deep layers, which is known as the over-smoothing issue.

1 code implementation • 30 Oct 2023 • Jialin Chen, Shirley Wu, Abhijit Gupta, Rex Ying

The objective of GNN explainability is to discern the underlying graph structures that have the most significant impact on model predictions.

1 code implementation • NeurIPS 2023 • Jialin Chen, Rex Ying

Temporal graphs are widely used to model dynamic systems with time-varying interactions.

no code implementations • 26 Oct 2023 • Charles Xu, Laney Goldman, Valentina Guo, Benjamin Hollander-Bodie, Maedee Trank-Greene, Ian Adelstein, Edward De Brouwer, Rex Ying, Smita Krishnaswamy, Michael Perlmutter

We make several crucial changes to the original geometric scattering architecture which we prove increase the ability of our network to capture information about the input signal and show that BLIS-Net achieves superior performance on both synthetic and real-world data sets based on traffic flow and fMRI data.

no code implementations • 6 Oct 2023 • Junchi Yu, Ran He, Rex Ying

These analogous problems are related to the input one, with reusable solutions and problem-solving strategies.

1 code implementation • NeurIPS 2023 • Tianyu Liu, Yuge Wang, Rex Ying, Hongyu Zhao

Discovering genes with similar functions across diverse biomedical contexts poses a significant challenge in gene representation learning due to data heterogeneity.

no code implementations • 28 Sep 2023 • Kenza Amara, Mennatallah El-Assady, Rex Ying

Diverse explainability methods of graph neural networks (GNN) have recently been developed to highlight the edges and nodes in the graph that contribute the most to the model predictions.

1 code implementation • 16 Aug 2023 • Tianyu Fu, Chiyue Wei, Yu Wang, Rex Ying

We introduce DeSCo, a scalable neural deep subgraph counting pipeline, designed to accurately predict both the count and occurrence position of queries on target graphs post single training.

no code implementations • 15 Jun 2023 • Menglin Yang, Min Zhou, Rex Ying, Yankai Chen, Irwin King

To address this, we propose a simple yet effective method, hyperbolic informed embedding (HIE), by incorporating cost-free hierarchical information deduced from the hyperbolic distance of the node to origin (i. e., induced hyperbolic norm) to advance existing \hlms.

1 code implementation • 6 Jun 2023 • Zhen Yang, Tinglin Huang, Ming Ding, Yuxiao Dong, Rex Ying, Yukuo Cen, Yangliao Geng, Jie Tang

To make each mini-batch have fewer false negatives, we design the proximity graph of randomly-selected instances.

1 code implementation • 5 May 2023 • Irene Li, Aosong Feng, Dragomir Radev, Rex Ying

Encoding long sequences in Natural Language Processing (NLP) is a challenging problem.

no code implementations • 28 Apr 2023 • Chenqing Hua, Sitao Luan, Minkai Xu, Rex Ying, Jie Fu, Stefano Ermon, Doina Precup

Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.

no code implementations • CVPR 2023 • Beini Xie, Heng Chang, Ziwei Zhang, Xin Wang, Daixin Wang, Zhiqiang Zhang, Rex Ying, Wenwu Zhu

To tackle these challenges, we propose a novel Robust Neural Architecture search framework for GNNs (G-RNA).

no code implementations • Learning on Graphs 2022 • Michal Pándy, Weikang Qiu, Gabriele Corso, Petar Veličković, Rex Ying, Jure Leskovec, Pietro Liò

At training time, we aggregate datasets of search trajectories and ground-truth shortest path distances, which we use to train a specialized graph neural network-based heuristic function using backpropagation through steps of the pathfinding process.

1 code implementation • 21 Oct 2022 • Aosong Feng, Irene Li, Yuang Jiang, Rex Ying

Efficient Transformers have been developed for long sequence modeling, due to their subquadratic memory and time complexity.

1 code implementation • 21 Oct 2022 • Shirley Wu, Jiaxuan You, Jure Leskovec, Rex Ying

FALCON features 1) a task-agnostic module, which performs message passing on the design graph via a Graph Neural Network (GNN), and 2) a task-specific module, which conducts label propagation of the known model performance information on the design graph.

no code implementations • 17 Oct 2022 • Syed Asad Rizvi, Nhi Nguyen, Haoran Lyu, Benjamin Christensen, Josue Ortega Caro, Antonio H. O. Fonseca, Emanuele Zappala, Maryam Bagherian, Christopher Averill, Chadi G. Abdallah, Rex Ying, Maria Brbic, Rahul Madhav Dhodapkar, David van Dijk

Graph Neural Networks (GNNs) have emerged as a powerful representation learning framework for graph-structured data.

1 code implementation • 30 Sep 2022 • Songtao Liu, Zhengkai Tu, Minkai Xu, Zuobai Zhang, Lu Lin, Rex Ying, Jian Tang, Peilin Zhao, Dinghao Wu

Current strategies use a decoupled approach of single-step retrosynthesis models and search algorithms, taking only the product as the input to predict the reactants for each planning step and ignoring valuable context information along the synthetic route.

no code implementations • 29 Sep 2022 • Songtao Liu, Rex Ying, Hanze Dong, Lu Lin, Jinghui Chen, Dinghao Wu

However, the analysis of implicit denoising effect in graph neural networks remains open.

1 code implementation • 20 Jun 2022 • Kenza Amara, Rex Ying, Zitao Zhang, Zhihao Han, Yinan Shan, Ulrik Brandes, Sebastian Schemm, Ce Zhang

As GNN models are deployed to more mission-critical applications, we are in dire need for a common evaluation protocol of explainability methods of GNNs.

no code implementations • 15 Jun 2022 • Tailin Wu, Qinchen Wang, Yinan Zhang, Rex Ying, Kaidi Cao, Rok Sosič, Ridwan Jalali, Hassan Hamam, Marko Maucec, Jure Leskovec

To model complex reservoir dynamics at both local and global scale, HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure.

1 code implementation • 6 Apr 2022 • Zhen Xu, Lanning Wei, Huan Zhao, Rex Ying, Quanming Yao, Wei-Wei Tu, Isabelle Guyon

Researchers naturally adopt Automated Machine Learning on Graph Learning, aiming to reduce the human effort and achieve generally top-performing GNNs, but their methods focus more on the architecture search.

1 code implementation • NeurIPS 2021 • Yushi Bai, Rex Ying, Hongyu Ren, Jure Leskovec

Here we present ConE (Cone Embedding), a KG embedding model that is able to simultaneously model multiple hierarchical as well as non-hierarchical relations in a knowledge graph.

Ranked #1 on Ancestor-descendant prediction on WN18RR

Ancestor-descendant prediction
Knowledge Graph Completion
**+2**

1 code implementation • NeurIPS 2021 • Gabriele Corso, Rex Ying, Michal Pándy, Petar Veličković, Jure Leskovec, Pietro Liò

The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research.

1 code implementation • 8 Sep 2021 • Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu

To address this, we propose a simple and efficient data augmentation strategy, local augmentation, to learn the distribution of the node features of the neighbors conditioned on the central node's feature and enhance GNN's expressive power with generated features.

1 code implementation • 24 Jun 2021 • Jiaqing Xie, Rex Ying

In this paper, we introuduce graph feature to feature (Fea2Fea) prediction pipelines in a low dimensional space to explore some preliminary results on structural feature correlation, which is based on graph neural network.

Ranked #1 on Graph Classification on Pubmed

no code implementations • NAACL 2021 • Xiaochen Hou, Peng Qi, Guangtao Wang, Rex Ying, Jing Huang, Xiaodong He, BoWen Zhou

Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks(GNN), but these approaches are usually vulnerable to parsing errors.

1 code implementation • 25 Jan 2021 • Jiaxuan You, Jonathan Gomes-Selman, Rex Ying, Jure Leskovec

However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test, which means GNNs that are not able to predict node clustering coefficients and shortest path distances, and cannot differentiate between different d-regular graphs.

2 code implementations • NeurIPS 2020 • Jiaxuan You, Rex Ying, Jure Leskovec

However, current research focuses on proposing and evaluating specific architectural designs of GNNs, as opposed to studying the more general design space of GNNs that consists of a Cartesian product of different design dimensions, such as the number of layers or the type of the aggregation function.

1 code implementation • 29 Sep 2020 • Guangtao Wang, Rex Ying, Jing Huang, Jure Leskovec

Currently, at every layer, attention is computed between connected pairs of nodes and depends solely on the representation of the two nodes.

12 code implementations • ICML 2020 • Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter W. Battaglia

Here we present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and deformable materials interacting with one another.

3 code implementations • NeurIPS 2019 • Ines Chami, Rex Ying, Christopher Ré, Jure Leskovec

Here we propose Hyperbolic Graph Convolutional Neural Network (HGCN), the first inductive hyperbolic GCN that leverages both the expressiveness of GCNs and hyperbolic geometry to learn inductive node representations for hierarchical and scale-free graphs.

Ranked #1 on Link Prediction on PPI (Accuracy metric)

no code implementations • 25 Oct 2019 • Guangtao Wang, Rex Ying, Jing Huang, Jure Leskovec

Graph Attention Networks (GATs) are the state-of-the-art neural architecture for representation learning with graphs.

no code implementations • ICLR 2020 • Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell

Graph Neural Networks (GNNs) are a powerful representational tool for solving problems on graph-structured inputs.

2 code implementations • 11 Jun 2019 • Jiaxuan You, Rex Ying, Jure Leskovec

Learning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs.

no code implementations • 9 Jun 2019 • Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, Alex Aiken

Graph Neural Networks (GNNs) are based on repeated aggregations of information across nodes' neighbors in a graph.

10 code implementations • NeurIPS 2019 • Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec

We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.

BIG-bench Machine Learning
Explainable artificial intelligence
**+2**

14 code implementations • NeurIPS 2018 • Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.

Ranked #1 on Graph Classification on REDDIT-MULTI-12K

2 code implementations • NeurIPS 2018 • Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec

Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.

5 code implementations • 6 Jun 2018 • Rex Ying, Ruining He, Kai-Feng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec

We develop a data-efficient Graph Convolutional Network (GCN) algorithm PinSage, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i. e., items) that incorporate both graph structure as well as node feature information.

3 code implementations • ICML 2018 • Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec

Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences.

no code implementations • 17 Sep 2017 • William L. Hamilton, Rex Ying, Jure Leskovec

Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks.

18 code implementations • NeurIPS 2017 • William L. Hamilton, Rex Ying, Jure Leskovec

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions.

Ranked #1 on Link Property Prediction on ogbl-ddi

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