Abstract Graph neural networks have achieved impressive results in predicting molecular properties, but they do not directly account for local and hidden structures the graph such as functional groups geometry. At each propagation step, aggregate only over first order neighbours can learn about important information contained subsequent well relationships between those higher connections—over m...

Relation prediction for knowledge graphs aims at predicting missing relationships between entities. Despite the importance of inductive relation prediction, most previous works are limited to a transductive setting and cannot process previously unseen The recent proposed subgraph-based reasoning models provided alternatives predict links from subgraph structure surrounding candidate triplet ind...

Resilience and fault tolerance are challenging tasks in the field of high performance computing (HPC) extreme scale systems. Components fail more often such systems, results application abort. Adopting fault–tolerance techniques can be consistently detect failures continue application’s execution even if exist. A prominent parallel programming specification, message passing interface (MPI), as ...