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A survey of imbalanced learning on graphs: Problems, techniques, and future directions
Graphs represent interconnected structures prevalent in a myriad of real-world scenarios.
Effective graph analytics, such as graph learning methods, enables users to gain profound …
Effective graph analytics, such as graph learning methods, enables users to gain profound …
A survey of inductive knowledge graph completion
X Liang, G Si, J Li, P Tian, Z An, F Zhou - Neural Computing and …, 2024 - Springer
Abstract Knowledge graph completion (KGC) can enhance the completeness of the
knowledge graph (KG). Traditional transductive KGC assumes that all entities and relations …
knowledge graph (KG). Traditional transductive KGC assumes that all entities and relations …
Normalizing flow-based neural process for few-shot knowledge graph completion
Knowledge graphs (KGs), as a structured form of knowledge representation, have been
widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC) …
widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC) …
Task-adaptive few-shot node classification
Node classification is of great importance among various graph mining tasks. In practice,
real-world graphs generally follow the long-tail distribution, where a large number of classes …
real-world graphs generally follow the long-tail distribution, where a large number of classes …
Few-shot learning on graphs
Graph representation learning has attracted tremendous attention due to its remarkable
performance in many real-world applications. However, prevailing supervised graph …
performance in many real-world applications. However, prevailing supervised graph …
Federated few-shot learning
Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning
model without exchanging their own local data. In this way, the server can exploit the …
model without exchanging their own local data. In this way, the server can exploit the …
Integrating entity attributes for error-aware knowledge graph embedding
Knowledge graphs (KGs) can structurally organize large-scale information in the form of
triples and significantly support many real-world applications. While most KG embedding …
triples and significantly support many real-world applications. While most KG embedding …
Scene-driven multimodal knowledge graph construction for embodied ai
Embodied AI is one of the most popular studies in artificial intelligence and robotics, which
can effectively improve the intelligence of real-world agents (ie robots) serving human …
can effectively improve the intelligence of real-world agents (ie robots) serving human …
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 …
Contrastive meta-learning for few-shot node classification
Few-shot node classification, which aims to predict labels for nodes on graphs with only
limited labeled nodes as references, is of great significance in real-world graph mining …
limited labeled nodes as references, is of great significance in real-world graph mining …