To address these issues, this paper proposes an adaptive temporal graph attention network (ATGAN), which is implemented in two steps: 1) An outlier time series filter (OTSF) technique is introduced to ...

Random geometric graphs (RGGs) are commonly used to model networked systems that depend on the underlying spatial embedding. We concern ourselves with the probability distribution of an RGG, which is ...

Trinity is a general purpose distributed graph system over a memory cloud. Memory cloud is a globally addressable, in-memory key-value store over a cluster of machines. Through the distributed ...

GAT-RWOS is a graph-based oversampling method that combines Graph Attention Networks (GATs) with random walk-based oversampling to address the class imbalance problem. By utilizing GAT's attention ...

Much work has been devoted to supporting RDF data. But state-of-the-art systems and methods still cannot handle web scale RDF data effectively. Furthermore, many useful and general purpose graph-based ...

A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its ...