10 citations · 31 across the 13 of their papers we have counts for
14 papers
On Calibration of Graph Neural Networks for Node Classification
Tong Liu, Yushan Liu, Marcel Hildebrandt +3
Graphs can model real-world, complex systems by representing entities and their interactions in terms of nodes and edges. To better exploit the graph structure, graph neural networ…
Combining Sub-Symbolic and Symbolic Methods for Explainability
Anna Himmelhuber, Stephan Grimm, Sonja Zillner +3
Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making. A number of sub-symbolic approaches have been developed to provide…
TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs
Yushan Liu, Yunpu Ma, Marcel Hildebrandt +2
Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types. However, information and knowledge evolve continuous…
Demystifying Graph Neural Network Explanations
Anna Himmelhuber, Mitchell Joblin, Martin Ringsquandl +1
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-m…
Generating Table Vector Representations
Aneta Koleva, Martin Ringsquandl, Mitchell Joblin +1
High-quality Web tables are rich sources of information that can be used to populate Knowledge Graphs (KG). The focus of this paper is an evaluation of methods for table-to-class a…
Power to the Relational Inductive Bias: Graph Neural Networks in Electrical Power Grids
Martin Ringsquandl, Houssem Sellami, Marcel Hildebrandt +4
The application of graph neural networks (GNNs) to the domain of electrical power grids has high potential impact on smart grid monitoring. Even though there is a natural correspon…