GLAudio Listens to the Sound of the Graph
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911962173014016 |
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| author | Sulser, Aurelio Wenckstern, Johann Kuempel, Clara |
| author_facet | Sulser, Aurelio Wenckstern, Johann Kuempel, Clara |
| contents | We propose GLAudio: Graph Learning on Audio representation of the node features and the connectivity structure. This novel architecture propagates the node features through the graph network according to the discrete wave equation and then employs a sequence learning architecture to learn the target node function from the audio wave signal. This leads to a new paradigm of learning on graph-structured data, in which information propagation and information processing are separated into two distinct steps. We theoretically characterize the expressivity of our model, introducing the notion of the receptive field of a vertex, and investigate our model's susceptibility to over-smoothing and over-squashing both theoretically as well as experimentally on various graph datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14387 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GLAudio Listens to the Sound of the Graph Sulser, Aurelio Wenckstern, Johann Kuempel, Clara Machine Learning Artificial Intelligence We propose GLAudio: Graph Learning on Audio representation of the node features and the connectivity structure. This novel architecture propagates the node features through the graph network according to the discrete wave equation and then employs a sequence learning architecture to learn the target node function from the audio wave signal. This leads to a new paradigm of learning on graph-structured data, in which information propagation and information processing are separated into two distinct steps. We theoretically characterize the expressivity of our model, introducing the notion of the receptive field of a vertex, and investigate our model's susceptibility to over-smoothing and over-squashing both theoretically as well as experimentally on various graph datasets. |
| title | GLAudio Listens to the Sound of the Graph |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2407.14387 |