Brandon Asher Mayer
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HUGE: Huge Unsupervised Graph Embeddings with TPUs
SIGKDD Conference on Knowledge Discovery and Data Mining, ACM (2023) (to appear)
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Graphs are a representation of structured data that captures the relationships between sets of objects. With the ubiquity of available network data, there is increasing industrial and academic need to quickly analyze graphs with billions of nodes and trillions of edges. A common first step for network understanding is Graph Embedding, the process of creating a continuous representation of nodes in a graph. A continuous representation is often more amenable, especially at scale, for solving downstream machine learning tasks such as classification, link prediction, and clustering. A high-performance graph embedding architecture leveraging Tensor Processing Units (TPUs) with configurable amounts of high-bandwidth memory is presented that simplifies the graph embedding problem and can scale to graphs with billions of nodes and trillions of edges. We verify the embedding space quality on real and synthetic large-scale datasets.
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GraphWorld: Fake Graphs Bring Real Insights for GNNs
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022)
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The continuing maturity of the deep learning subfield of graph neural networks (GNNs) has motivated recent studies into the standard datasets used to benchmark GNNs. While important improvements have been made to GNN datasets and experimental design, any one dataset provides only a singular, potentially spurious insight into the performance of any GNN being tested. We show that state-of-the-art GNN task datasets do not cover the distribution of graphs in a much larger real-data graph repository, with respect to several key graph metrics. Motivated by this finding, we introduce GraphWorld, a novel distributed framework and software package for testing GNN models on an arbitrarily-large population of \emph{synthetic} task datasets.
GraphWorld allows a user to efficiently generate a \emph{world} of millions of graph datasets, with fine-grained control over graph generator parameters, and benchmark arbitrary GNN models, with built-in hyperparameter tuning. Using GraphWorld to generate diverse graph worlds corresponding to node classification, graph classification, and link prediction tasks, we provide insight into the sensitivity of 10,000+ GNN models to various parameters of graphs and node features and} show comparisons between models that have not been possible to make in any previous work. We also introduce a novel metric with which to explore each models' performance on the graph world, conditioning on graph metrics and graph generator parameters.
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