Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews

• URL: http://arxiv.org/abs/2307.00920v2
• Date: Mon, 11 Mar 2024 14:56:47 GMT
• Title: Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews
• Authors: Sergio Burdisso, Esa\'u Villatoro-Tello, Srikanth Madikeri, Petr Motlicek
• Abstract summary: We propose a simple approach for weighting self-connecting edges in a Graph Convolutional Network (GCN) We show its impact on depression detection from transcribed clinical interviews.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose a simple approach for weighting self-connecting edges in a Graph Convolutional Network (GCN) and show its impact on depression detection from transcribed clinical interviews. To this end, we use a GCN for modeling non-consecutive and long-distance semantics to classify the transcriptions into depressed or control subjects. The proposed method aims to mitigate the limiting assumptions of locality and the equal importance of self-connections vs. edges to neighboring nodes in GCNs, while preserving attractive features such as low computational cost, data agnostic, and interpretability capabilities. We perform an exhaustive evaluation in two benchmark datasets. Results show that our approach consistently outperforms the vanilla GCN model as well as previously reported results, achieving an F1=0.84 on both datasets. Finally, a qualitative analysis illustrates the interpretability capabilities of the proposed approach and its alignment with previous findings in psychology.

Related papers

• Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks [11.090533263502937]
  We tackle the challenge of semi-supervised node classification on the Contextual Block Model (CSBM) dataset. We design an optimal spectral estimator inspired by Principal Component Analysis (PCA) with the training labels and essential data. We also evaluate the efficacy of graph ridge regression and Graph Convolutional Networks (GCN) on this dataset.
  arXiv  Detail & Related papers  (2024-12-18T11:44:19Z)
• Node Classification With Integrated Reject Option [6.0497759658090775]
  We propose NCwR, a novel approach to node classification in Graph Neural Networks (GNNs) with an integrated reject option. We perform experiments using our method on three standard citation network datasets Cora, Citeseer and Pubmed.
  arXiv  Detail & Related papers  (2024-12-04T10:22:34Z)
• A Topological Perspective on Demystifying GNN-Based Link Prediction Performance [72.06314265776683]
  Topological Concentration (TC) is based on the intersection of the local subgraph of each node with the ones of its neighbors. We show that TC has a higher correlation with LP performance than other node-level topological metrics like degree and subgraph density. We propose Approximated Topological Concentration (ATC) and theoretically/empirically justify its efficacy in approximating TC and reducing the complexity.
  arXiv  Detail & Related papers  (2023-10-06T22:07:49Z)
• SEM-GAT: Explainable Semantic Pose Estimation using Learned Graph Attention [10.883346969896621]
  This paper proposes a Graph Neural Network(GNN)-based method for exploiting semantics and local geometry to guide the identification of reliable pointcloud registration candidates. Semantic and morphological features of the environment serve as key reference points for registration, enabling accurate lidar-based pose estimation. We test our method on the KITTI odometry dataset, achieving competitive accuracy compared to benchmark methods and a higher track smoothness while relying on significantly fewer network parameters.
  arXiv  Detail & Related papers  (2023-08-07T16:43:46Z)
• Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration [11.709808788756966]
  GraphAC is a principled, task-agnostic, and stable framework for evaluating Graph Neural Network (GNN) research for molecular representation learning. We introduce a novel objective function: the Competitive Barlow Twins, that allow two GNNs to jointly update themselves from direct competitions against each other.
  arXiv  Detail & Related papers  (2023-01-27T03:33:11Z)
• Interpolation-based Correlation Reduction Network for Semi-Supervised Graph Learning [49.94816548023729]
  We propose a novel graph contrastive learning method, termed Interpolation-based Correlation Reduction Network (ICRN) In our method, we improve the discriminative capability of the latent feature by enlarging the margin of decision boundaries. By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discnative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• Tackling Oversmoothing of GNNs with Contrastive Learning [35.88575306925201]
  Graph neural networks (GNNs) integrate the comprehensive relation of graph data and representation learning capability. Oversmoothing makes the final representations of nodes indiscriminative, thus deteriorating the node classification and link prediction performance. We propose the Topology-guided Graph Contrastive Layer, named TGCL, which is the first de-oversmoothing method maintaining all three mentioned metrics.
  arXiv  Detail & Related papers  (2021-10-26T15:56:16Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• CatGCN: Graph Convolutional Networks with Categorical Node Features [99.555850712725]
  CatGCN is tailored for graph learning when the node features are categorical. We train CatGCN in an end-to-end fashion and demonstrate it on semi-supervised node classification.
  arXiv  Detail & Related papers  (2020-09-11T09:25:17Z)
• Unifying Graph Convolutional Neural Networks and Label Propagation [73.82013612939507]
  We study the relationship between LPA and GCN in terms of two aspects: feature/label smoothing and feature/label influence. Based on our theoretical analysis, we propose an end-to-end model that unifies GCN and LPA for node classification. Our model can also be seen as learning attention weights based on node labels, which is more task-oriented than existing feature-based attention models.
  arXiv  Detail & Related papers  (2020-02-17T03:23:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.