Machine learning frameworks such as graph neural networks typically rely on a given, fixed
graph to exploit relational inductive biases and thus effectively learn from network data.
However, when said graphs are (partially) unobserved, noisy, or dynamic, the problem
of inferring graph structure from data becomes relevant. In this paper, we postulate a
graph convolutional relationship between the observed and latent graphs, and formulate
the graph structure learning task as a network inverse (deconvolution) problem. In lieu of
eigendecomposition-based spectral methods or iterative optimization solutions, we unroll and
truncate proximal gradient iterations to arrive at a parameterized neural network architecture
that we call a Graph Deconvolution Network (GDN). GDNs can learn a distribution of graphs
in a supervised fashion, perform link prediction or edge-weight regression tasks by adapting
the loss function, and they are inherently inductive as well as node permutation equivariant.
We corroborate GDN’s superior graph learning performance and its generalization to larger
graphs using synthetic data in supervised settings. Moreover, we demonstrate the robustness and representation power of GDNs on real world neuroimaging and social network datasets.
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This content will become publicly available on December 1, 2024
Exploiting social graph networks for emotion prediction
Abstract Emotion prediction plays an essential role in mental healthcare and emotion-aware computing. The complex nature of emotion resulting from its dependency on a person’s physiological health, mental state, and his surroundings makes its prediction a challenging task. In this work, we utilize mobile sensing data to predict self-reported happiness and stress levels. In addition to a person’s physiology, we also incorporate the environment’s impact through weather and social network. To this end, we leverage phone data to construct social networks and develop a machine learning architecture that aggregates information from multiple users of the graph network and integrates it with the temporal dynamics of data to predict emotion for all users. The construction of social networks does not incur additional costs in terms of ecological momentary assessments or data collection from users and does not raise privacy concerns. We propose an architecture that automates the integration of the user’s social network in affect prediction and is capable of dealing with the dynamic distribution of real-life social networks, making it scalable to large-scale networks. The extensive evaluation highlights the prediction performance improvement provided by the integration of social networks. We further investigate the impact of graph topology on the model’s performance.
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- NSF-PAR ID:
- 10456034
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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