Reproducible graph Machine learning research, we introduce the Open Graph Benchmark —a diverse set of realistic graph datasets in terms of scales, domains, and task categories

We compare the results of our model trained to minimize the F1 score to a typical baseline algorithm used in particle physics - the Adaptive Vertex Reconstruction algorithm

We believe that our findings constitute a step towards establishing a hierarchy of models w.r.t. their expressive power and, in this sense, the Principal Neighbourhood Aggregation model appears to outperform the prior art in Graph Neural Networks layer design

Given a network with n users, we implement a GNN1 with the goal of predicting the ratings given by user 405, which is the user who has rated the most movies in the dataset

Studying the local symmetries of graphs, we propose a more general algorithm that uses different kernels on different edges, making the network equivariant to local and global graph isomorphisms and more expressive

We hope to investigate the interpretability of the edges learned by the Graph Finite-State Automaton layer to determine whether they correspond to useful general concepts, which might allow the GFSA edges to be shared between multiple tasks

We propose a path integral based Graph neural networks framework, which consists of self-consistent convolution and pooling units, the later is closely related to the subgraph centrality

We introduce the idea of Physical Scene Graphs, which represent scenes as hierarchical graphs, with nodes in the hierarchy corresponding intuitively to object parts at different scales, and edges to physical connections between parts

We introduced the multipole graph kernel network, a graph-based algorithm able to capture correlations in data at any length scale with a linear time complexity

The local subgraph approach is fundamentally different from prior work using entire graphs, which only captures broad structure at the loss of finer topological structure

Under the defined K-shot learning setting, Graph Extrapolation Networks learn to extrapolate the knowledge of a given graph to unseen entities, with a stochastic transductive layer to further propagate the knowledge between the unseen entities and model uncertainty in the link pr...

We proposed a novel Proxy-based deep Graph Metric Learning approach from the perspective of graph classification, which offers a new insight into deep metric learning

We have demonstrated the efficacy of Graph Information Bottleneck by evaluating the robustness of the Graph Attention Networks model trained under the GIB principle on adversarial attacks

To utilize the strength of both Euclidean and hyperbolic geometries, we develop a novel Geometry Interaction Learning method for graphs, a well-suited and efficient alternative for learning abundant geometric properties in graph