Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
Conference on Neural Information Processing Systems (NeurIPS)
In this paper, we theoretically characterize graph neural network’s representation power for high-order node set prediction problems (where a prediction is made over a set of more than 1 node). In particular, we focus on one most important second-order task—link prediction. There are two representative classes of GNN methods for link prediction: GAE and SEAL. GAE (Graph Autoencoder) first applies a GNN to the whole graph, and then aggregates the representations of the source and target nodes as their link representation. SEAL extracts a subgraph around the source and target nodes, labels the nodes in the subgraph, and then uses a GNN to learn a link representation from the labeled subgraph. At first glance, both GAE and SEAL use a GNN. However, their performance gap can be very large. On the recent Open Graph Benchmark datasets, SEAL achieved 3 first places out of 4 datasets, outperforming the best GAE method by up to 195% in Hits@100. In this paper, by studying this performance gap between GAE and SEAL, we first point out a key limitation of GAE caused by directly aggregating two node representations as a link representation. To address this limitation, we propose the labeling trick. Labeling trick unifies several recent successes to improve GNNs’ representation power, such as SEAL, Distance Encoding, and Identity-aware GNN, into a single and most basic form. We prove that with labeling trick a sufficiently expressive GNN can learn the most expressive structural representations for node sets. Our work establishes a theoretical foundation for using GNNs for high-order node set prediction.
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka, Kiwan Maeng, Manoj Chakkaravarthy, Udit Gupta, David Brooks, Carole-Jean Wu
Ilkan Esiyok, Pascal Berrang, Katriel Cohn-Gordon, Robert Künnemann