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

January 28, 2023

Continual Graph Learning: A Survey
Qiao Yuan, Sheng-Uei Guan, Pin Ni, Tianlun Luo, Ka Lok Man, Prudence Wong, Victor Chang
Timely Survey Continual LEArning Graph Structured Data Continual Graph Graph Learning Model

December 11, 2022

Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges
Jing Ren, Feng Xia, Azadeh Noori Hoshyar, Charu C. Aggarwal
Anomaly Detection Technical Challenge Financial Application Practical Algorithm Graph Learning Graph Attention Network Graph Learning Model Graph Based Anomaly

October 31, 2022

Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks
Tiehua Zhang, Yuze Liu, Yao Yao, Youhua Xia, Xin Chen, Xiaowei Huang, Jiong Jin
Graph Representation Learning Direct Convolution Heterogeneous Graph Graph Learning Model Heterogeneous Graph Learning

July 24, 2022

Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning
Haoran Yang, Xiangyu Zhao, Muyang Li, Hongxu Chen, Guandong Xu
Differential Privacy Graph Learning Trade Offs Graph Learning Model Differentially Private Federated Learning Proximity Graph

June 18, 2022

MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning
Namyong Park, Ryan Rossi, Nesreen Ahmed, Christos Faloutsos
Meta Learning Graph Learning View Selection Graph Benchmark Graph Learning Model Graph Meta Learning Meta Tree

May 30, 2022

Spectral Maps for Learning on Subgraphs
Marco Pegoraro, Riccardo Marin, Arianna Rampini, Simone Melzi, Luca Cosmo, Emanuele Rodolà
Graph Learning Random Node Subgraph Structure Graph Learning Model Spectral Space

March 17, 2022

Graph Augmentation Learning
Shuo Yu, Huafei Huang, Minh N. Dao, Feng Xia
Graph Augmentation Social Network Analysis Graph Learning Model

February 22, 2022

Physics-Informed Graph Learning
Ciyuan Peng, Feng Xia, Vidya Saikrishna, Huan Liu
Graph Learning Graph Data Graph Learning Model Physic Informed Graph