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.

翻译：我们提出了一种为图卷积网络（GCN）中自连接边赋予权重的简单方法，并展示了该方法在基于转录临床访谈进行抑郁症检测中的有效性。为此，我们采用GCN对非连续和长距离语义进行建模，从而将转录文本分类为抑郁组或对照组。所提方法旨在缓解GCN中局部性假设的限制以及自连接与邻节点边权重相等的问题，同时保留低计算成本、数据无关性和可解释性等优良特性。我们在两个基准数据集上进行了全面评估。结果表明，我们的方法始终优于原始GCN模型及先前文献报道的结果，在两个数据集上均取得了F1=0.84%的性能。最后，定性分析展示了所提方法的可解释性能力，及其与心理学领域既往研究结论的一致性。