graph-convolutional-network

This paper proposes a joint adversarial framework that uses graph convolutional networks to enrich sparse scene graphs before generating semantically richer visual content. The approach makes scene enrichment explicit at the representation level, producing visually plausible and structurally coherent images.

Proposes a bi-level chaotic fusion based graph convolutional network for stock market prediction intervals, achieving significant improvements in Winkler score, PIAW, and PICP over baselines on NSE data from 2016-2026.