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Heterophily, or the tendency of connected nodes in networks to have different class labels or dissimilar features, has been identified as challenging for many Graph Neural Network (GNN) models.
An information flow model for conflict and fission in small groups
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Friends and neighbors on the web
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Embedding entities and relations for learning and inference in knowledge bases
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
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William L Hamilton, Rex Ying, and Jure Leskovec. 2017b · 2017
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Semi-Supervised Classification with Graph Convolutional Networks. In
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Predicting multicellular function through multi-layer tissue networks
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Pitfalls of Graph Neural Network Evaluation
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MoleculeNet: a benchmark for molecular machine learning
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Link prediction based on graph neural networks
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Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In
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Applications of link prediction in social networks: A review
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Jonathan Halcrow, Alexandru Mosoi, Sam Ruth, and Bryan Perozzi. 2020 · 2020
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Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020 · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann. 2020 · 2020
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Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020 · 2020
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Revisiting graph neural networks for link prediction
Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, and Long Jin. 2020 · 2020
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Graph Neural Networks with Heterophily
Jiong Zhu, Ryan A Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen K Ahmed, and Danai Koutra. 2020a · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020b · 2020
Shopping queries dataset: A large-scale ESCI benchmark for improving product search
Chandan K Reddy, Lluís Màrquez, Fran Valero, Nikhil Rao, Hugo Zaragoza, Sambaran Bandyopadhyay, Arnab Biswas, Anlu Xing, and Karthik Subbian. 2022 · 2022
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Flashlight: Scalable link prediction with effective decoders. In
Yiwei Wang, Bryan Hooi, Yozen Liu, Tong Zhao, Zhichun Guo, and Neil Shah. 2022 · 2022
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Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for link prediction
Seongjun Yun, Seoyoon Kim, Junhyun Lee, Jaewoo Kang, and Hyunwoo J Kim. 2022 · 2022
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Graph neural networks: link prediction
Muhan Zhang. 2022 · 2022
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Graph neural networks for graphs with heterophily: A survey
Xin Zheng, Yi Wang, Yixin Liu, Ming Li, Miao Zhang, Di Jin, Philip S Yu, and Shirui Pan. 2022 · 2022
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Bernnet: Learning arbitrary graph spectral filters via bernstein approximation
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Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods. In
Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, and Ser Nam Lim. 2021 · 2021
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Knowledge graph embedding for link prediction: A comparative analysis
Andrea Rossi, Denilson Barbosa, Donatella Firmani, Antonio Matinata, and Paolo Merialdo. 2021 · 2021
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Multi-scale attributed node embedding
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A hybrid recommender system based-on link prediction for movie baskets analysis
Mohammadsadegh Vahidi Farashah, Akbar Etebarian, Reza Azmi, and Reza Ebrahimzadeh Dastjerdi. 2021 · 2021
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Labeling trick: A theory of using graph neural networks for multi-node representation learning
Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, and Long Jin. 2021 · 2021
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Neural bellman-ford networks: A general graph neural network framework for link prediction
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Link prediction on heterophilic graphs via disentangled representation learning
Shijie Zhou, Zhimeng Guo, Charu Aggarwal, Xiang Zhang, and Suhang Wang. 2022 · 2022
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Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. 2023 · 2023
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Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking
Juanhui Li, Harry Shomer, Haitao Mao, Shenglai Zeng, Yao Ma, Neil Shah, Jiliang Tang, and Dawei Yin. 2023 · 2023
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When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability. In
Sitao Luan, Chenqing Hua, Minkai Xu, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Jie Fu, Jure Leskovec, and Doina Precup. 2023 · 2023
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Revisiting link prediction: A data perspective
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Neural common neighbor with completion for link prediction
Xiyuan Wang, Haotong Yang, and Muhan Zhang. 2023 · 2023
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PANE: scalable and effective attributed network embedding
Renchi Yang, Jieming Shi, Xiaokui Xiao, Yin Yang, Sourav S Bhowmick, and Juncheng Liu. 2023 · 2023
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Link Prediction under Heterophily: A Physics-Inspired Graph Neural Network Approach
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The Faiss library
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NetInfoF Framework: Measuring and Exploiting Network Usable Information. In
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Characterizing graph datasets for node classification: Homophily-heterophily dichotomy and beyond
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Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices. In
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User preference-aware fake news detection. In
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