Methoden vergleichen

	Multimodales Graph-Neuronales Netz	Graph Neural Network
Fachgebiet	Deep Learning	Netzwerkanalyse
Familie	Machine learning	Process / pipeline
Entstehungsjahr	2019–2020	2017–2018 (major variants)
Urheber	Kipf & Welling (GNN foundation); extended to multimodal settings by multiple research groups c. 2019–2020	—
Typ	Graph-based deep learning with multimodal input fusion	Deep learning on graph-structured data
Wegweisende Quelle	Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).
Aliasnamen	MM-GNN, Multimodal GNN, Multi-modal Graph Network, Cross-modal Graph Neural Network	GNN, GCN, GAT, GraphSAGE
Verwandt	6	5
Zusammenfassung	A Multimodal Graph Neural Network (MM-GNN) combines data from multiple modalities — such as text, images, and structured features — into a unified graph structure and applies graph-based message passing to learn joint representations. It enables relational reasoning across heterogeneous data sources, going beyond what unimodal or simple concatenation approaches can capture.	A Graph Neural Network (GNN) is a deep learning architecture that operates directly on graph-structured data by combining node features with structural information through iterative neighborhood message passing. The three canonical variants — the Graph Convolutional Network (GCN) introduced by Kipf and Welling in 2017, the Graph Attention Network (GAT) introduced by Veličković et al. in 2018, and GraphSAGE — differ in how they aggregate neighbor information: GCN applies a spectral convolution over the full adjacency, GAT weights neighbors by learned attention scores, and GraphSAGE samples and aggregates local neighborhoods inductively, enabling generalization to unseen nodes.