Patra-RGCN: Missing Link Prediction in Model Card Graphs through Node Property Encodings

Distributed frameworks for edgeAI span the edge to cloud continuum in support of AI through services for deployment, monitoring, and optimization. However, edge networks are unreliable, raising monitoring issues including missing events, network interruptions, and storage limitations all of which can lead to information loss that undermines decision-making and analytics. We address information loss through Patra Relational Graph Convolutional Networks (Patra-RGCN), an extension of Relational Graph Convolutional Networks (R-GCN), that uses both structural and content information about a graph (including time and Model Card subgraphs) to infer missing edges and suggest new connections. Experimental evaluation demonstrates that by effectively capturing both structural and attribute-level information, the proposed model significantly improves the detection of information loss in heterogeneous graphs.