… -source benchmarking framework for Graph Neural Networks that … The benchmark led us
to propose graph PE that has … the first release of our benchmark. We also perform additional …

… A general graph neural network architecture is constructed, taking in graphs containing
nodes, edges, node attributes, and edge attributes, inputted into an embedding layer, GC blocks, …

… information, connecting the novel and effective graph-learning algorithms to the noisy and
… Heterogeneous Graph Benchmark (HGB). HGB currently contains 11 heterogeneous graph …

… In this work, we propose Brain Graph Neural Network Benchmark (BrainGB)—a novel
attempt to benchmark brain network analysis with GNNs to the best of our knowledge. The …

… In this section, we summarize the benchmark graph data sets, evaluation methods, and
opensource implementation, respectively. We detail practical applications of GNNs in various …

… show how GraphXAI enables systematic benchmarking of eight state-of-the-… graph datasets.
We explore the utility of the ShapeGGen generator to benchmark GNN explainers on graphs …

… graph models. In this work, we focus on graph neural networks (GNNs) as the graph
models and extend the emerging studies on federated learning (FL) over neural network …

… from Wikipedia for benchmarking Graph Neural Networks. The … benchmarks. The dataset
is publicly available, along with the implementation of the data pipeline and the benchmark …

… on only a portion of edges of a graph. A flurry of methods have been introduced in recent
years that attempt to make use of graph neural networks (GNNs) for this task. Furthermore, new …

… A practical guideline on leveraging graph neural networks (GNNs) for realizing … Benchmark
study. We evaluate the aforementioned GNNs on eight datasets and provide the benchmark …