Knowledge graph embedding: A survey from the perspective of representation spaces
Knowledge graph embedding (KGE) is an increasingly popular technique that aims to
represent entities and relations of knowledge graphs into low-dimensional semantic spaces …
represent entities and relations of knowledge graphs into low-dimensional semantic spaces …
An Efficient Subgraph GNN with Provable Substructure Counting Power
We investigate the enhancement of graph neural networks'(GNNs) representation power
through their ability in substructure counting. Recent advances have seen the adoption of …
through their ability in substructure counting. Recent advances have seen the adoption of …
Multi-view Graph Structural Representation Learning via Graph Coarsening
X Qi, Q Bai, Y Wen, H Zhang, X Yuan - arXiv preprint arXiv:2404.11869, 2024 - arxiv.org
Graph Transformers (GTs) have made remarkable achievements in graph-level tasks.
However, most existing works regard graph structures as a form of guidance or bias for …
However, most existing works regard graph structures as a form of guidance or bias for …
Distance Recomputator and Topology Reconstructor for Graph Neural Networks
This paper introduces novel methodologies, the Distance Recomputator and Topology
Reconstructor, aimed at enhancing Graph Neural Networks (GNNs). The Distance …
Reconstructor, aimed at enhancing Graph Neural Networks (GNNs). The Distance …
Graph Neural Network-based Multi-agent Reinforcement Learning for Resilient Distributed Coordination of Multi-Robot Systems
Existing multi-agent coordination techniques are often fragile and vulnerable to anomalies
such as agent attrition and communication disturbances, which are quite common in the real …
such as agent attrition and communication disturbances, which are quite common in the real …
Hybrid Focal and Full-Range Attention Based Graph Transformers
M Zhu, Z Zhao, W Cai - arXiv preprint arXiv:2311.04653, 2023 - arxiv.org
The paradigm of Transformers using the self-attention mechanism has manifested its
advantage in learning graph-structured data. Yet, Graph Transformers are capable of …
advantage in learning graph-structured data. Yet, Graph Transformers are capable of …
Designing Big Data Frameworks for Quality-of-Data Controlling in Large-Scale Knowledge Graphs
H Baalbaki - 2023 - theses.hal.science
Knowledge Graphs (KGs) are the most used representation of structured information about a
particular domain consisting of billions of facts in the form of entities (nodes) and relations …
particular domain consisting of billions of facts in the form of entities (nodes) and relations …