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@INPROCEEDINGS{Campbell:268890,
      author       = {Campbell, Alexander and Spasov, Simeon and Toschi, Nicola
                      and Lio, Pietro},
      title        = {{DBGDGM}: {D}ynamic {B}rain {G}raph {D}eep {G}enerative
                      {M}odel},
      volume       = {227},
      reportid     = {DZNE-2024-00389},
      pages        = {1346-1371},
      year         = {2024},
      note         = {ISSN 2640-3498: Proceedings of Machine Learning Research},
      abstract     = {Graphs are a natural representation of brain activity
                      derived from functional magnetic imaging (fMRI) data. It is
                      well known that clusters of anatomical brain regions, known
                      as functional connectivity networks (FCNs), encode temporal
                      relationships which can serve as useful biomarkers for
                      understanding brain function and dysfunction. Previous
                      works, however, ignore the temporal dynamics of the brain
                      and focus on static graphs. In this paper, we propose a
                      dynamic brain graph deep generative model (DBGDGM) which
                      simultaneously clusters brain regions into temporally
                      evolving communities and learns dynamic unsupervised node
                      embeddings. Specifically, DBGDGM represents brain graph
                      nodes as embeddings sampled from a distribution over
                      communities that evolve over time. We parameterise this
                      community distribution using neural networks that learn from
                      subject and node embeddings as well as past community
                      assignments. Experiments demonstrate DBGDGM outperforms
                      baselines in graph generation, dynamic link prediction, and
                      is comparable for graph classification. Finally, an analysis
                      of the learnt community distributions reveals overlap with
                      known FCNs reported in neuroscience literature.},
      month         = {Jul},
      date          = {2023-07-10},
      organization  = {Medical Imaging with Deep Learning,
                       Nashville, Tenn. (USA), 10 Jul 2023 -
                       12 Jul 2023},
      cin          = {AG Mukherjee},
      cid          = {I:(DE-2719)1013030},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)8},
      url          = {https://pub.dzne.de/record/268890},
}