SlepNet: Spectral Subgraph Representation Learning for Neural Dynamics

• URL: http://arxiv.org/abs/2506.16602v1
• Date: Thu, 19 Jun 2025 21:03:52 GMT
• Title: SlepNet: Spectral Subgraph Representation Learning for Neural Dynamics
• Authors: Siddharth Viswanath, Rahul Singh, Yanlei Zhang, J. Adam Noah, Joy Hirsch, Smita Krishnaswamy,
• Abstract summary: Graph neural networks have been useful in machine learning on graph-structured data.<n>They have had limited use in representing patterning signals over graphs.<n>SlepNet is a novel GCN that uses Slepian bases rather than graph harmonics.
• Score: 5.424636897130673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph neural networks have been useful in machine learning on graph-structured data, particularly for node classification and some types of graph classification tasks. However, they have had limited use in representing patterning of signals over graphs. Patterning of signals over graphs and in subgraphs carries important information in many domains including neuroscience. Neural signals are spatiotemporally patterned, high dimensional and difficult to decode. Graph signal processing and associated GCN models utilize the graph Fourier transform and are unable to efficiently represent spatially or spectrally localized signal patterning on graphs. Wavelet transforms have shown promise here, but offer non-canonical representations and cannot be tightly confined to subgraphs. Here we propose SlepNet, a novel GCN architecture that uses Slepian bases rather than graph Fourier harmonics. In SlepNet, the Slepian harmonics optimally concentrate signal energy on specifically relevant subgraphs that are automatically learned with a mask. Thus, they can produce canonical and highly resolved representations of neural activity, focusing energy of harmonics on areas of the brain which are activated. We evaluated SlepNet across three fMRI datasets, spanning cognitive and visual tasks, and two traffic dynamics datasets, comparing its performance against conventional GNNs and graph signal processing constructs. SlepNet outperforms the baselines in all datasets. Moreover, the extracted representations of signal patterns from SlepNet offers more resolution in distinguishing between similar patterns, and thus represent brain signaling transients as informative trajectories. Here we have shown that these extracted trajectory representations can be used for other downstream untrained tasks. Thus we establish that SlepNet is useful both for prediction and representation learning in spatiotemporal data.

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