A survey of graph neural networks in various learning paradigms: methods, applications, and challenges
In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …
many problems in computer vision, speech recognition, natural language processing, and …
A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions
Graphs are structured data that models complex relations between real-world entities.
Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar …
Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar …
Empower text-attributed graphs learning with large language models (llms)
Text-attributed graphs have recently garnered significant attention due to their wide range of
applications in web domains. Existing methodologies employ word embedding models for …
applications in web domains. Existing methodologies employ word embedding models for …
Transductive linear probing: A novel framework for few-shot node classification
Few-shot node classification is tasked to provide accurate predictions for nodes from novel
classes with only few representative labeled nodes. This problem has drawn tremendous …
classes with only few representative labeled nodes. This problem has drawn tremendous …
[PDF][PDF] Local and Global: Temporal Question Answering via Information Fusion.
Many models that leverage knowledge graphs (KGs) have recently demonstrated
remarkable success in question answering (QA) tasks. In the real world, many facts …
remarkable success in question answering (QA) tasks. In the real world, many facts …
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
Y Liu, M Li, X Li, L Huang, F Giunchiglia… - ACM Transactions on …, 2024 - dl.acm.org
Node classification is an essential problem in graph learning. However, many models
typically obtain unsatisfactory performance when applied to few-shot scenarios. Some …
typically obtain unsatisfactory performance when applied to few-shot scenarios. Some …
A simple but effective approach for unsupervised few-shot graph classification
Graphs, as a fundamental data structure, have proven efficacy in modeling complex
relationships between objects and are therefore found in wide web applications. Graph …
relationships between objects and are therefore found in wide web applications. Graph …
Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning
Short text classification, as a research subtopic in natural language processing, is more
challenging due to its semantic sparsity and insufficient labeled samples in practical …
challenging due to its semantic sparsity and insufficient labeled samples in practical …
Question Calibration and Multi-Hop Modeling for Temporal Question Answering
Many models that leverage knowledge graphs (KGs) have recently demonstrated
remarkable success in question answering (QA) tasks. In the real world, many facts …
remarkable success in question answering (QA) tasks. In the real world, many facts …
Unifying Token-and Span-level Supervisions for Few-shot Sequence Labeling
Few-shot sequence labeling aims to identify novel classes based on only a few labeled
samples. Existing methods solve the data scarcity problem mainly by designing token-level …
samples. Existing methods solve the data scarcity problem mainly by designing token-level …