Informatiqs engineers detail a serverless architecture for real-time AI inference on Google Cloud Platform, addressing the cold start problem through asynchronous event-driven orchestration with Event…
Informatiqs argues that Graph Neural Networks (GNNs) are the strategic choice for enterprise generative AI on Google Cloud Platform (GCP), emphasizing the shift from flat vector embeddings to graph-ba…
Researchers propose a logic-based framework for node classification in Simple Graph Convolution (SGC) networks that extracts global logical rules from minimal abductive explanations, achieving compact…
Researchers introduced XIGL, an architecture-agnostic human-in-the-loop strategy that uses active explanation guidance to remove shortcut learning from Graph Neural Networks (GNNs), improving their re…
Researchers propose EGRL, a graph neural network framework for RNA-protein interaction prediction that uses implicit meta-path learning, multi-relation-aware attention, and a graph generator to handle…
Promograph uses Graph Neural Networks (GNNs) to model retail promotion waste by representing SKUs as nodes and co-purchase patterns as edges, enabling the propagation of discount impacts across the pr…
Researchers have developed a new approach to 1-WL stable coloring for Graph Neural Networks that breaks traditional scalability bottlenecks by using a probabilistically backed refinement algorithm. A …
A new study compares 19 graph neural network layer types for driving trajectory prediction, finding that ARMA, Chebyshev, and topology-aware layers outperform others. The research provides design prin…
This article summarizes research by Tasfin Mahmud on addressing observational bias in recommender systems using Graph Neural Networks (GNNs) and Causal Reinforcement Learning. The author implemented a…