Graph convolutional neural netwoks (GCNNs) have been emerged to handle graph-structured data in recent years. Most existing GCNNs are either spatial approaches working on neighborhood of each node, or ...

Graph convolutional network (GCN) outputs powerful representation by considering the structure information of the data to conduct representation learning, but its robustness is sensitive to the ...

Both graph databases and knowledge graphs “have similarities but serve different purposes,” said Shalvi Singh, senior product manager at Amazon AI. “Graph databases serve as the underlying ...

Predicting pathologic complete response (pCR) from clinicopathologic variables and HER2DX genomic test in stage II/III HER2+ breast cancer treated with taxane, trastuzumab, and pertuzumab (THP): ...

The Graph, an indexing and query protocol, has integrated Chainlink’s interoperability standard to enable cross-chain transfer of its native token. Tapping into Chainlink’s cross-chain ...

Because of the genetically modified organisms (GMOs) labeling policies issued in many countries and areas, polymerase chain reaction (PCR) methods were developed for the execution of GMO labeling ...

Causal chain graphs model the dependency structure between individuals when the assumption of individual independence in causal inference is violated. However, causal chain graphs are often unknown in ...

I have a graph where I have hierarchical agents. One top level supervisor and then mid level supervisor and then leaf node agents. When I try to print the Ascii graph from the compiled graph from top ...

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward ...