Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study.

Training deep graph neural networks (GNNs) is notoriously hard. Besides the standard plights in training deep architectures such as vanishing gradients and overfitting, it also uniquely suffers from over-smoothing, information squashing, and so on, which limits their potential power for encoding the high-order neighbor structure in large-scale graphs. Although numerous efforts are proposed to address these limitations, such as various forms of skip connections, graph normalization, and random dropping, it is difficult to disentangle the advantages brought by a deep GNN architecture from those "tricks" necessary to train such an architecture.

Microwave-assisted synthesis of defective CaAgTiCoO with high photoelectrocatalytic activity for organic pollutant removal from water.

CaTiO is considered to be one of the most promising catalysts for the degradation of organic pollutants, but its application is limited by the wide band gap and low catalytic activity. Element doping is an effective strategy to solve these problems. Herein, a novel CaTiO co-doped with Ag and Co (CaAgTiCoO) was synthesized by combining co-precipitation and the microwave hydrothermal method for the first time.