  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.
