arXiv:2305.06315v3 Announce Type: replace-cross Abstract: For deep learning problems on graph-structured data, pooling layers are important for down sampling, reducing computational cost, and to minimize overfitting. We define a pooling layer, nervePool, for data structured as simplicial complexes, which are generalizations of graphs that include higher-dimensional simplices beyond vertices and edges; this structure allows for greater flexibility in modeling higher-order relationships. The proposed simplicial coarsening scheme is built upon partitions of vertices, which allow us to generate hi
This research is emerging as deep learning models for graph-structured data encounter limitations and researchers seek more sophisticated ways to represent complex relationships.
Simplicial pooling layers can abstract higher-order relationships in data, leading to more flexible and potentially more powerful AI models, particularly for complex systems.
The ability to model higher-dimensional data structures with custom pooling layers moves graph neural networks towards more nuanced and accurate representations beyond simple nodes and edges.
- · AI researchers using GNNs
- · Fields with complex, relational data (e.g., drug discovery, social networks)
- · Deep learning framework developers
- · Simpler graph pooling methods
- · Organizations reliant on less sophisticated AI for complex graph data
Improved performance and efficiency in deep learning models operating on graph-structured data.
New applications and insights in areas requiring advanced relational modeling, like material science or brain connectomics.
The development of a new class of 'simplicial AI' that specializes in hyper-relational data, potentially altering how certain types of problems are approached.
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Read at arXiv cs.LG