Graph Learning Model

Graph learning models aim to extract meaningful information from data structured as graphs, focusing on tasks like node classification and link prediction. Current research emphasizes developing robust and generalizable models, including Graph Neural Networks (GNNs) and Graph Transformers, as well as exploring the integration of large language models (LLMs) to enhance performance and adaptability. This field is significant due to the prevalence of graph-structured data across diverse domains, promising advancements in areas such as drug discovery, social network analysis, and anomaly detection.

Papers

November 4, 2024

Do graph neural network states contain graph properties?
Tom Pelletreau-Duris, Ruud van Bakel, Michael Cochez
Graph Neural Network Graph Drawing Inherent Interpretability Graph Feature Graph Learning Model Neural State Model Agnostic Explainability

October 26, 2024

Just Propagate: Unifying Matrix Factorization, Network Embedding, and LightGCN for Link Prediction
Haoxin Liu
Link Prediction Matrix Factorization Graph Learning Model Graph Analysis

October 3, 2024

Language Models are Graph Learners
Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong
Language Model Graph Neural Network Graph Transformer Graph Learning Graph Learning Task Graph Learning Model

August 20, 2024

AnyGraph: Graph Foundation Model in the Wild
Lianghao Xia, Chao Huang
Wild Challenge Graph Representation Graph Domain Graph Learning Model Graph Foundation Model Graph Element

August 18, 2024

Federated Graph Learning with Structure Proxy Alignment
Xingbo Fu, Zihan Chen, Binchi Zhang, Chen Chen, Jundong Li
Graph Neural Network Federated Learning Federated Graph Learning Graph Learning Model Structure Alignment

July 13, 2024

Learning a Mini-batch Graph Transformer via Two-stage Interaction Augmentation
Wenda Li, Kaixuan Chen, Shunyu Liu, Tongya Zheng, Wenjie Huang, Mingli Song
Graph Transformer Semi Supervised Node Classification Graph Learning Model Interaction Transformer

July 3, 2024

Foundations and Frontiers of Graph Learning Theory
Yu Huang, Min Zhou, Menglin Yang, Zhen Wang, Muhan Zhang, Jie Wang, Hong Xie, Hao Wang, Defu Lian, Enhong Chen
Graph Neural Network Neural Architecture Graph Representation Graph Learning Flawed Foundation New Frontier Graph Learning Model

May 31, 2024

Learning on Large Graphs using Intersecting Communities
Ben Finkelshtein, İsmail İlkan Ceylan, Michael Bronstein, Ron Levie
LeArning Abstract Graph Machine Learning Sparse Graph Message Passing Neural Network Graph Learning Model Network Community Social Group

April 23, 2024

A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications
Wenbo Shang, Xin Huang
LeArning Abstract Financial Application Graph Learning Query Information Graph Learning Model Graph Generative Model

April 2, 2024

GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
Namyong Park, Ryan Rossi, Xing Wang, Antoine Simoulin, Nesreen Ahmed, Christos Faloutsos
New Benchmark Graph Learning Graph Structure Learning Graph Learning Model

January 6, 2024

GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs
Zhongshu Zhu, Bin Jing, Xiaopei Wan, Zhizhen Liu, Lei Liang, Jun zhou
Graph Neural Network Graph Drawing Graph Partitioning Graph Learning Model Graph Cut Structural Characterization Graph Inference Scalable GNN

November 24, 2023

Cycle Invariant Positional Encoding for Graph Representation Learning
Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen, Yusu Wang
Graph Representation Learning Graph Learning Graph Structured Data Graph Learning Model

November 5, 2023

ELEGANT: Certified Defense on the Fairness of Graph Neural Networks
Yushun Dong, Binchi Zhang, Hanghang Tong, Jundong Li
Graph Neural Network Procedural Fairness GNN Architecture Graph Learning Model

October 24, 2023

Deceptive Fairness Attacks on Graphs via Meta Learning
Jian Kang, Yinglong Xia, Ross Maciejewski, Jiebo Luo, Hanghang Tong
Graph Drawing Adversarial Robustness Graph Learning Model Fair Graph Fairness Attack

October 9, 2023

GraphLLM: Boosting Graph Reasoning Ability of Large Language Model
Ziwei Chai, Tianjie Zhang, Liang Wu, Kaiqiao Han, Xiaohai Hu, Xuanwen Huang, Yang Yang
Large Language Model Graph Reasoning Graph Learning Model

October 2, 2023

Deep Insights into Noisy Pseudo Labeling on Graph Data
Botao Wang, Jia Li, Yang Liu, Jiashun Cheng, Yu Rong, Wenjia Wang, Fugee Tsung
Pseudo Labeling Graph Data Training Graph Noisy Pseudo Label Graph Learning Model

August 27, 2023

Class-Imbalanced Graph Learning without Class Rebalancing
Zhining Liu, Ruizhong Qiu, Zhichen Zeng, Hyunsik Yoo, David Zhou, Zhe Xu, Yada Zhu, Kommy Weldemariam, Jingrui He, Hanghang Tong
Class Imbalance Graph Learning Model Graph Topology Class Balancing Imbalanced Graph

July 17, 2023

Examining the Effects of Degree Distribution and Homophily in Graph Learning Models
Mustafa Yasir, John Palowitch, Anton Tsitsulin, Long Tran-Thanh, Bryan Perozzi
Mixed Effect Synthetic Graph Graph Learning Model Benchmarking Deep Learning GNN Application Degree Distribution

June 8, 2023

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction
Xuan Lin, Lichang Dai, Yafang Zhou, Zu-Guo Yu, Wen Zhang, Jian-Yu Shi, Dong-Sheng Cao, Li Zeng, Haowen Chen, Bosheng Song, Philip S. Yu, Xiangxiang Zeng
Deep Learning Graph Learning Drug Discovery Molecular Representation Comprehensive Evaluation Drug Drug Interaction Graph Learning Model

May 2, 2023

LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees
Shangyuan Liu, Linglingzhi Zhu, Anthony Man-Cho So
Graph Signal Processing Graph Based Learning New Formulation Graph Learning Model Stationary Distribution Recovery Guarantee