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Found 10 papers, 4 papers with code
On Calibration of Graph Neural Networks for Node Classification
We investigate the calibration of graph neural networks for node classification, study the effect of existing post-processing calibration methods, and analyze the influence of model capacity, graph density, and a new loss function on calibration.
TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs
Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types.
Combining Sub-Symbolic and Symbolic Methods for Explainability
Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making.
Demystifying Graph Neural Network Explanations
Graph neural networks (GNNs) are quickly becoming the standard approach for learning on graph structured data across several domains, but they lack transparency in their decision-making.
Generating Table Vector Representations
High-quality Web tables are rich sources of information that can be used to populate Knowledge Graphs (KG).
Power to the Relational Inductive Bias: Graph Neural Networks in Electrical Power Grids
The application of graph neural networks (GNNs) to the domain of electrical power grids has high potential impact on smart grid monitoring.
Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs
Biomedical knowledge graphs permit an integrative computational approach to reasoning about biological systems.
Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing
The graph structure of biomedical data differs from those in typical knowledge graph benchmark tasks.
Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs
The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to justify the fact being true (thesis) or the fact being false (antithesis), respectively.
Reasoning on Knowledge Graphs with Debate Dynamics
The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively.
