• Corpus ID: 239998578

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

@inproceedings{Lim2021LargeSL,
  title={Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods},
  author={Derek Lim and Felix Hohne and Xiuyu Li and Sijia Huang and Vaishnavi Gupta and Omkar Bhalerao and Ser-Nam Lim},
  booktitle={NeurIPS},
  year={2021}
}
Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other. Recently, new Graph Neural Networks (GNNs) have been developed that move beyond the homophily regime; however, their evaluation has often been conducted on small graphs with limited application domains. We collect and introduce diverse nonhomophilous datasets from a variety of application areas that have up to 384x more nodes and 1398x more edges… 
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6 Citations
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Citation Type

Graph Neural Networks for Graphs with Heterophily: A Survey
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A systematic taxonomy that essentially governs existing heterophilic GNN models is proposed, along with a general summary and detailed analysis, to facilitate robust and fair evaluations of these graph neural networks.
Unsupervised Heterophilous Network Embedding via r-Ego Network Discrimination
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The first empirical study on the impact of homophily ratio on the performance of existing unsupervised NE methods and reveals their limitations is introduced and a SELf-supErvised Network Embedding (Selene) framework is developed for learning useful node representations for both homophilous and heterophILous networks.
Augmentation-Free Graph Contrastive Learning
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TLDR
This work proposes Subgraph-To-Node (S2N) translation, which is a novel formulation to efficiently learn representations of subgraphs, and demonstrates that models with S2N translation are more ef-cient than state-of-the-art models without substantial performance decrease.
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
We investigate graph neural networks on graphs with heterophily. Some existing methods amplify a node’s neighborhood with multi-hop neighbors to include more nodes with homophily. How-ever, it is a…
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
TLDR
This work theoretically characterize the connections between heterophily and oversmoothing, and designs a model that addresses the discrepancy in features and degrees between neighbors by incorporating signed messages and learned degree corrections.

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