Detailansicht

M. Sc. Clara Holzhüter

Graphs in Artificial Intelligence and Neural Networks (GAIN)

Fax: +49 561 804-6022
Email: clara.holzhueter[at]uni-kassel[dot]de

Fraunhofer IEE: Clara Holzhüter

Location: Wilhelmshöher Allee 67
34121 Kassel

Room: 2107

Research group "Graphs in Artificial Intelligence and Neural Networks" (GAIN) (opens in a new window)

Fraunhofer Institute for Energy Economics and Energy System Technology (IEE)

clara.juliane.holzhueter[at]iee.fraunhofer[dot]de

Publications

[ 2023 ]
[ 2022 ]

2023[ to top ]

Power flow forecasts at transmission grid nodes using Graph Neural Networks. Beinert, Dominik; Holzhüter, Clara; Thomas, Josephine; Vogt, Stephan. In Energy and AI, 14(1), bl 100262. Elsevier, 2023.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
The increasing share of renewable energy in the electricity grid and progressing changes in power consumption have led to fluctuating, and weather-dependent power flows. To ensure grid stability, grid operators rely on power forecasts which are crucial for grid calculations and planning. In this paper, a Multi-Task Learning approach is combined with a Graph Neural Network (GNN) to predict vertical power flows at transformers connecting high and extra-high voltage levels. The proposed method accounts for local differences in power flow characteristics by using an Embedding Multi-Task Learning approach. The use of a Bayesian embedding to capture the latent node characteristics allows to share the weights across all transformers in the subsequent node-invariant GNN while still allowing the individual behavioral patterns of the transformers to be distinguished. At the same time, dependencies between transformers are considered by the GNN architecture which can learn relationships between different transformers and thus take into account that power flows in an electricity network are not independent from each other. The effectiveness of the proposed method is demonstrated through evaluation on two real-world data sets provided by two of four German Transmission System Operators, comprising large portions of the operated German transmission grid. The results show that the proposed Multi-Task Graph Neural Network is a suitable representation learner for electricity networks with a clear advantage provided by the preceding embedding layer. It is able to capture interconnections between correlated transformers and indeed improves the performance in power flow prediction compared to standard Neural Networks. A sign test shows that the proposed model reduces the test RMSE on both data sets compared to the benchmark models significantly.
@article{beinert2023power, abstract = {The increasing share of renewable energy in the electricity grid and progressing changes in power consumption have led to fluctuating, and weather-dependent power flows. To ensure grid stability, grid operators rely on power forecasts which are crucial for grid calculations and planning. In this paper, a Multi-Task Learning approach is combined with a Graph Neural Network (GNN) to predict vertical power flows at transformers connecting high and extra-high voltage levels. The proposed method accounts for local differences in power flow characteristics by using an Embedding Multi-Task Learning approach. The use of a Bayesian embedding to capture the latent node characteristics allows to share the weights across all transformers in the subsequent node-invariant GNN while still allowing the individual behavioral patterns of the transformers to be distinguished. At the same time, dependencies between transformers are considered by the GNN architecture which can learn relationships between different transformers and thus take into account that power flows in an electricity network are not independent from each other. The effectiveness of the proposed method is demonstrated through evaluation on two real-world data sets provided by two of four German Transmission System Operators, comprising large portions of the operated German transmission grid. The results show that the proposed Multi-Task Graph Neural Network is a suitable representation learner for electricity networks with a clear advantage provided by the preceding embedding layer. It is able to capture interconnections between correlated transformers and indeed improves the performance in power flow prediction compared to standard Neural Networks. A sign test shows that the proposed model reduces the test RMSE on both data sets compared to the benchmark models significantly.}, author = {Beinert, Dominik and Holzhüter, Clara and Thomas, Josephine and Vogt, Stephan}, journal = {Energy and AI}, keywords = {itegpub}, number = 1, pages = 100262, publisher = {Elsevier}, title = {Power flow forecasts at transmission grid nodes using Graph Neural Networks}, volume = 14, year = 2023 }
%0 Journal Article %1 beinert2023power %A Beinert, Dominik %A Holzhüter, Clara %A Thomas, Josephine %A Vogt, Stephan %D 2023 %I Elsevier %J Energy and AI %N 1 %P 100262 %R 10.1016/j.egyai.2023.100262 %T Power flow forecasts at transmission grid nodes using Graph Neural Networks %U https://www.sciencedirect.com/science/article/pii/S2666546823000344?via%3Dihub %V 14 %X The increasing share of renewable energy in the electricity grid and progressing changes in power consumption have led to fluctuating, and weather-dependent power flows. To ensure grid stability, grid operators rely on power forecasts which are crucial for grid calculations and planning. In this paper, a Multi-Task Learning approach is combined with a Graph Neural Network (GNN) to predict vertical power flows at transformers connecting high and extra-high voltage levels. The proposed method accounts for local differences in power flow characteristics by using an Embedding Multi-Task Learning approach. The use of a Bayesian embedding to capture the latent node characteristics allows to share the weights across all transformers in the subsequent node-invariant GNN while still allowing the individual behavioral patterns of the transformers to be distinguished. At the same time, dependencies between transformers are considered by the GNN architecture which can learn relationships between different transformers and thus take into account that power flows in an electricity network are not independent from each other. The effectiveness of the proposed method is demonstrated through evaluation on two real-world data sets provided by two of four German Transmission System Operators, comprising large portions of the operated German transmission grid. The results show that the proposed Multi-Task Graph Neural Network is a suitable representation learner for electricity networks with a clear advantage provided by the preceding embedding layer. It is able to capture interconnections between correlated transformers and indeed improves the performance in power flow prediction compared to standard Neural Networks. A sign test shows that the proposed model reduces the test RMSE on both data sets compared to the benchmark models significantly.
Graph Neural Networks Designed for Different Graph Types: A Survey. Thomas, Josephine; Moallemy-Oureh, Alice; Beddar-Wiesing, Silvia; Holzhüter, Clara. In Transactions on Machine Learning Research. 2023.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. For this purpose, they can be defined as many different types which suitably reflect the individual contexts of the represented problem. To address cutting-edge problems based on graph data, the research field of Graph Neural Networks (GNNs) has emerged. Despite the field’s youth and the speed at which new models are developed, many recent surveys have been published to keep track of them. Nevertheless, it has not yet been gathered which GNN can process what kind of graph types. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static and dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover, we distinguish between GNN models for discrete-time or continuous-time dynamic graphs and group the models according to their architecture. We find that there are still graph types that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.
@article{thomas2023graph, abstract = {Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. For this purpose, they can be defined as many different types which suitably reflect the individual contexts of the represented problem. To address cutting-edge problems based on graph data, the research field of Graph Neural Networks (GNNs) has emerged. Despite the field’s youth and the speed at which new models are developed, many recent surveys have been published to keep track of them. Nevertheless, it has not yet been gathered which GNN can process what kind of graph types. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static and dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover, we distinguish between GNN models for discrete-time or continuous-time dynamic graphs and group the models according to their architecture. We find that there are still graph types that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.}, author = {Thomas, Josephine and Moallemy-Oureh, Alice and Beddar-Wiesing, Silvia and Holzhüter, Clara}, journal = {Transactions on Machine Learning Research}, keywords = {itegpub}, title = {Graph Neural Networks Designed for Different Graph Types: A Survey}, year = 2023 }
%0 Journal Article %1 thomas2023graph %A Thomas, Josephine %A Moallemy-Oureh, Alice %A Beddar-Wiesing, Silvia %A Holzhüter, Clara %D 2023 %J Transactions on Machine Learning Research %T Graph Neural Networks Designed for Different Graph Types: A Survey %U https://openreview.net/pdf?id=h4BYtZ79uy %X Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. For this purpose, they can be defined as many different types which suitably reflect the individual contexts of the represented problem. To address cutting-edge problems based on graph data, the research field of Graph Neural Networks (GNNs) has emerged. Despite the field’s youth and the speed at which new models are developed, many recent surveys have been published to keep track of them. Nevertheless, it has not yet been gathered which GNN can process what kind of graph types. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static and dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover, we distinguish between GNN models for discrete-time or continuous-time dynamic graphs and group the models according to their architecture. We find that there are still graph types that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.

2022[ to top ]

Segmentation Improves 3D Object Classification in Graph Convolutional Networks. Holzhüter, Clara; Teich, Florian; Wörgötter, Florentin. In International Conference on Computer Vision Theory and Applications (VISAPP), bll 290–298. 2022.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
3D object classification is involved in many computer vision pipelines such as autonomous driving or robotics. However, the irregular format of 3D data makes it challenging to develop suitable deep learning architectures. This paper proposes CompointNet, a graph convolutional network architecture, which performs 3D object classification by means of part decomposition. Our model consumes a 3D point cloud in the form of a part graph which is constructed from segmented 3D shapes. The model learns a global descriptor by hierarchically aggregating neighbourhood information using simple graph convolutions. To capture both local and global information, a global classification method processing each point separately is combined with our part graph based approach into a hybrid version of CompointNet. We compare our approach to several state-of-the art methods and demonstrate competitive performance. Particularly, in terms of per class accuracy, our hybrid approach outperforms the compared methods. The proposed hybrid variants achieve a high classification accuracy, while being much more efficient than those benchmark models with a comparable performance. The conducted experiments show that part based approaches levering structural information about a 3D object, indeed, can improve the classification performance of 3D deep learning models.
@inproceedings{holzhuter2022segmentation, abstract = {3D object classification is involved in many computer vision pipelines such as autonomous driving or robotics. However, the irregular format of 3D data makes it challenging to develop suitable deep learning architectures. This paper proposes CompointNet, a graph convolutional network architecture, which performs 3D object classification by means of part decomposition. Our model consumes a 3D point cloud in the form of a part graph which is constructed from segmented 3D shapes. The model learns a global descriptor by hierarchically aggregating neighbourhood information using simple graph convolutions. To capture both local and global information, a global classification method processing each point separately is combined with our part graph based approach into a hybrid version of CompointNet. We compare our approach to several state-of-the art methods and demonstrate competitive performance. Particularly, in terms of per class accuracy, our hybrid approach outperforms the compared methods. The proposed hybrid variants achieve a high classification accuracy, while being much more efficient than those benchmark models with a comparable performance. The conducted experiments show that part based approaches levering structural information about a 3D object, indeed, can improve the classification performance of 3D deep learning models.}, author = {Holzhüter, Clara and Teich, Florian and Wörgötter, Florentin}, booktitle = {International Conference on Computer Vision Theory and Applications (VISAPP)}, keywords = {not_ies}, pages = {290–298}, title = {Segmentation Improves 3D Object Classification in Graph Convolutional Networks}, year = 2022 }
%0 Conference Paper %1 holzhuter2022segmentation %A Holzhüter, Clara %A Teich, Florian %A Wörgötter, Florentin %B International Conference on Computer Vision Theory and Applications (VISAPP) %D 2022 %P 290--298 %R 10.5220/0010778100003124 %T Segmentation Improves 3D Object Classification in Graph Convolutional Networks %U https://www.scitepress.org/Papers/2022/107781/ %X 3D object classification is involved in many computer vision pipelines such as autonomous driving or robotics. However, the irregular format of 3D data makes it challenging to develop suitable deep learning architectures. This paper proposes CompointNet, a graph convolutional network architecture, which performs 3D object classification by means of part decomposition. Our model consumes a 3D point cloud in the form of a part graph which is constructed from segmented 3D shapes. The model learns a global descriptor by hierarchically aggregating neighbourhood information using simple graph convolutions. To capture both local and global information, a global classification method processing each point separately is combined with our part graph based approach into a hybrid version of CompointNet. We compare our approach to several state-of-the art methods and demonstrate competitive performance. Particularly, in terms of per class accuracy, our hybrid approach outperforms the compared methods. The proposed hybrid variants achieve a high classification accuracy, while being much more efficient than those benchmark models with a comparable performance. The conducted experiments show that part based approaches levering structural information about a 3D object, indeed, can improve the classification performance of 3D deep learning models.
Graph Neural Networks Designed for Different Graph Types: A Survey. Thomas, Josephine M.; Moallemy-Oureh, Alice; Beddar-Wiesing, Silvia; Holzhüter, Clara. In arXiv e-prints, bl arXiv:2204.03080. 2022.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
- [ URL ]
Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. Based on this, the young research field of Graph Neural Networks (GNNs) has emerged. Despite the youth of the field and the speed in which new models are developed, many good surveys have been published in the last years. Nevertheless, an overview on which graph types can be modeled by GNNs is missing. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static as well as on dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover in the dynamic case, we separate the models in discrete-time and continuous-time dynamic graphs based on their architecture. While ordering the existing GNN models, we find, that there are still graph types, that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.
@article{thomas2022graph, abstract = {Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. Based on this, the young research field of Graph Neural Networks (GNNs) has emerged. Despite the youth of the field and the speed in which new models are developed, many good surveys have been published in the last years. Nevertheless, an overview on which graph types can be modeled by GNNs is missing. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static as well as on dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover in the dynamic case, we separate the models in discrete-time and continuous-time dynamic graphs based on their architecture. While ordering the existing GNN models, we find, that there are still graph types, that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.}, author = {Thomas, Josephine M. and Moallemy-Oureh, Alice and Beddar-Wiesing, Silvia and Holzhüter, Clara}, journal = {arXiv e-prints}, keywords = {itegpub}, pages = {arXiv:2204.03080}, title = {Graph Neural Networks Designed for Different Graph Types: A Survey}, year = 2022 }
%0 Journal Article %1 thomas2022graph %A Thomas, Josephine M. %A Moallemy-Oureh, Alice %A Beddar-Wiesing, Silvia %A Holzhüter, Clara %D 2022 %J arXiv e-prints %P arXiv:2204.03080 %T Graph Neural Networks Designed for Different Graph Types: A Survey %U https://arxiv.org/abs/2204.03080 %X Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. Based on this, the young research field of Graph Neural Networks (GNNs) has emerged. Despite the youth of the field and the speed in which new models are developed, many good surveys have been published in the last years. Nevertheless, an overview on which graph types can be modeled by GNNs is missing. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types and properties. We consider GNNs operating on static as well as on dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover in the dynamic case, we separate the models in discrete-time and continuous-time dynamic graphs based on their architecture. While ordering the existing GNN models, we find, that there are still graph types, that are not or only rarely covered by existing GNN models. We point out where models are missing and give potential reasons for their absence.

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Detailansicht

M. Sc. Clara Holzhüter

Graphs in Artificial Intelligence and Neural Networks (GAIN)

Fraunhofer Institute for Energy Economics and Energy System Technology (IEE)

Pu­bli­ca­ti­ons

Publications