REVIEW 7 cited by
SIGN: Scalable Inception Graph Neural Networks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media. The popularity of graph neural networks has sparked interest, both in academia and in industry, in developing methods that scale to very large graphs such as Facebook or Twitter social networks. In most of these approaches, the computational cost is alleviated by a sampling strategy retaining a subset of node neighbors or subgraphs at training time. In this paper we propose a new, efficient and scalable graph deep learning architecture which sidesteps the need for graph sampling by using graph convolutional filters of different size that are amenable to efficient precomputation, allowing extremely fast training and inference. Our architecture allows using different local graph operators (e.g. motif-induced adjacency matrices or Personalized Page Rank diffusion matrix) to best suit the task at hand. We conduct extensive experimental evaluation on various open benchmarks and show that our approach is competitive with other state-of-the-art architectures, while requiring a fraction of the training and inference time. Moreover, we obtain state-of-the-art results on ogbn-papers100M, the largest public graph dataset, with over 110 million nodes and 1.5 billion edges.
Forward citations
Cited by 7 Pith papers
-
When does distribution shift break graph neural networks calibration?
GNN calibration under distribution shift is governed by a single closed-form slope κ(hs, ht, ρ) that sets the optimal global temperature T⋆=1/κ and explains when node-wise recalibration cannot help.
-
No Need to Train Your RDB Foundation Model
Column-wise, parameter-free JUICE encodings let single-table ICL models solve multi-table RDB prediction tasks with no training or fine-tuning.
-
FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting
A semi-asynchronous federated graph learning framework with soft-label clustering, staleness-weighted aggregation, and cluster broadcasting reports higher accuracy and faster convergence than ten baselines.
-
HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representations
HOPSE encodes higher-order topological data by applying graph positional and structural encoders to Hasse graph decompositions, matching or exceeding message-passing models on benchmarks with up to 7x faster training.
-
Efficiently Learning Branching Networks for Multitask Algorithmic Reasoning
AutoBRANE learns tree-structured branching networks for multitask algorithmic reasoning via gradient-based task affinities and convex relaxation.
-
Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach
EFGNN fuses per-depth evidential opinions from a multi-hop GNN into one final Dirichlet-based prediction whose uncertainty is lower than that of any single propagation depth.
-
Transaction Categorization with Relational Deep Learning in QuickBooks
Rel-Cat predicts transaction categories by converting the QuickBooks relational database into a heterogeneous graph and treating categorization as link prediction, beating production baselines on a private dataset.
Discussion (0). Sign in to comment.