EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neural networks in the non-Euclidean domain, particularly graphs. With the success of these graph neural networks (GNN) in the static setting, we approach further practical scenarios where the graph dynamically evolves. Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics. These methods require the knowledge of a node in the full time span (including both training and testing) and are less applicable to the frequent change of the node set. In some extreme scenarios, the node sets at different time steps may completely differ. To resolve this challenge, we propose EvolveGCN, which adapts the graph convolutional network (GCN) model along the temporal dimension without resorting to node embeddings. The proposed approach captures the dynamism of the graph sequence through using an RNN to evolve the GCN parameters. Two architectures are considered for the parameter evolution. We evaluate the proposed approach on tasks including link prediction, edge classification, and node classification. The experimental results indicate a generally higher performance of EvolveGCN compared with related approaches. The code is available at \url{https://github.com/IBM/EvolveGCN}.Comment: AAAI 2020. The code is available at https://github.com/IBM/EvolveGC

Development of an acetylacetonate-modified silica-zirconia composite membrane applicable to gas separation

In this work, an acetylacetonate-modified equimolar SiO2–ZrO2 composite-derived membrane with molecular sieving properties was successfully fabricated. The sol-gel method was successfully employed to chemically modify zirconium tetrabutoxide prior to co-hydrolysis and -condensation with tetraethoxysilane, the resulting sol then used to fabricate a gas separation layer. An acetylacetonate-modified SiO2–ZrO2-derived membrane prepared at 300 °C showed H2 permeance of 9.9 × 10 −7 mol m−2 s−1 Pa−1 with a H2/SF6 permeance ratio of 7,600, which was a significant improvement over pure SiO2–ZrO2-derived membranes (H2 permeance: 1.4 × 10 −6 mol m−2 s−1 Pa−1, H2/SF6 permeance ratio: 11). Heat-treatment of an acac−-modified SiO2–ZrO2-derived membrane prepared at temperatures that ranged from 250 °C (H2 permeance: 4.5 × 10−8 mol m−2 s−1 Pa−1, H2/CH4: 100, CO2/CH4: 60, H2/SF6: >18,000 at 50 °C) to 550 °C resulted in an improved H2 permeance of 3.4 × 10−6 mol m−2 s−1 Pa−1 with reduced permeance ratios (H2/CH4: 3, H2/SF6: 9) at 50 °C. A membrane prepared by heat-treating a 250 °C-fired membrane at 300 °C showed the best trade-off with H2 permeance-H2/SF6 permeance ratios above the trade-off line compared with membranes prepared at other temperatures