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The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
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Semi-supervised classification with graph convolutional networks
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Using fuzzy logic to leverage html markup for web page representation
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Deep residual learning for image recognition
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Convolutional neural networks on graphs with fast localized spectral filtering
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node2vec: Scalable feature learning for networks
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Variational graph auto-encoders
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Overcoming egfr (t790m) and egfr (c797s) resistance with mutant-selective allosteric inhibitors
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A survey of heterogeneous information network analysis
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Attention is all you need
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Disinformation and social bot operations in the run up to the 2017 french presidential election
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Zoobp: Belief propagation for heterogeneous networks
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Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning
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Semi-supervised clustering in attributed heterogeneous information networks
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The spread of low-credibility content by social bots
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Deeper insights into graph convolutional networks for semi-supervised learning
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Representation learning on graphs with jumping knowledge networks
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Graph signal processing: Overview, challenges, and applications
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Predict then propagate: Graph neural networks meet personalized pagerank
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Monophily in social networks introduces similarity among friends-of-friends
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Link prediction based on graph neural networks
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Adversarial attacks and defences: A survey
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Adversarial attacks on neural networks for graph data
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Adversarial attack on graph structured data
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Graph r-cnn for scene graph generation
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Review of drug repositioning approaches and resources
Xue H, Li J, Xie H, Wang Y · 2018
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A review of network-based approaches to drug repositioning
Lotfi Shahreza M, Ghadiri N, Mousavi S R, Varshosaz J, Green J R · 2018
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Conditional molecular design with deep generative models
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Dynamic hyper-graph inference framework for computer-assisted diagnosis of neurodegenerative diseases
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The physics of brain network structure, function and control
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Local smoothness of graph signals
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Strategies for pre-training graph neural networks
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Deepgcns: Can gcns go as deep as cnns?
Li G, Muller M, Thabet A, Ghanem B · 2019
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Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
Abu-El-Haija S, Perozzi B, Kapoor A, Alipourfard N, Lerman K, Harutyunyan H, Ver Steeg G, Galstyan A · 2019
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Revisiting graph neural networks: All we have is low-pass filters
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Label efficient semi-supervised learning via graph filtering
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Diffusion improves graph learning
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Optimizing generalized pagerank methods for seed-expansion community detection
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Toward a spectral theory of cellular sheaves
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Deep learning in spiking neural networks
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Information gerrymandering in social networks skews collective decision-making, 2019
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Perils and challenges of social media and election manipulation analysis: The 2018 us midterms
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Neural graph collaborative filtering
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Graph-based global reasoning networks
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Graph attention convolution for point cloud semantic segmentation
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Network-based prediction of drug combinations
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Xu B, Shen H, Cao Q, Qiu Y, Cheng X · 2019
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Adversarial learning on heterogeneous information networks
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Graph neural networks exponentially lose expressive power for node classification
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Meta-gnn: On few-shot node classification in graph meta-learning
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On the bottleneck of graph neural networks and its practical implications
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Principal neighbourhood aggregation for graph nets
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Beyond homophily in graph neural networks: Current limitations and effective designs
Zhu J, Yan Y, Zhao L, Heimann M, Akoglu L, Koutra D · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, others · 2020
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Adaptive structural fingerprints for graph attention networks
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Luan S, Zhao M, Hua C, Chang X W, Precup D · 2020
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Geom-gcn: Geometric graph convolutional networks
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Characteristic functions on graphs: Birds of a feather, from statistical descriptors to parametric models
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Open graph benchmark: Datasets for machine learning on graphs
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Deepergcn: All you need to train deeper gcns
Li G, Xiong C, Thabet A, Ghanem B · 2020
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Simple and deep graph convolutional networks
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Adaptive universal generalized pagerank graph neural network
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A survey on contrastive self-supervised learning
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Self-supervised visual feature learning with deep neural networks: A survey
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Graph contrastive learning with augmentations
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Deep graph contrastive representation learning
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Opportunities and challenges in deep learning adversarial robustness: A survey
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Fraud detection: A systematic literature review of graph-based anomaly detection approaches
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Lightgcn: Simplifying and powering graph convolution network for recommendation
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Learning graph convolutional network for skeleton-based human action recognition by neural searching
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A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions
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Conditional constrained graph variational autoencoders for molecule design
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Just-in-time defect identification and localization: A two-phase framework
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Jito: a tool for just-in-time defect identification and localization
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Towards deeper graph neural networks
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Swin transformer: Hierarchical vision transformer using shifted windows
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Do transformers really perform badly for graph representation?
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Graph neural networks with learnable structural and positional representations
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Rethinking graph transformers with spectral attention
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Self-supervised learning: Generative or contrastive
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New benchmarks for learning on non-homophilous graphs
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Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
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Diverse message passing for attribute with heterophily
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Is homophily a necessity for graph neural networks?
Ma Y, Liu X, Shah N, Tang J · 2021
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Expertise and dynamics within crowdsourced musical knowledge curation: A case study of the genius platform
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Rozemberczki B, Sarkar R · 2021
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Datasets: A community library for natural language processing
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Graphnorm: A principled approach to accelerating graph neural network training
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Bernnet: Learning arbitrary graph spectral filters via bernstein approximation
He M, Wei Z, Xu H, others · 2021
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Universal graph convolutional networks
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Node similarity preserving graph convolutional networks
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Breaking the limit of graph neural networks by improving the assortativity of graphs with local mixing patterns
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Non-local graph neural networks
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Beyond low-frequency information in graph convolutional networks
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Pde-gcn: Novel architectures for graph neural networks motivated by partial differential equations
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Grand: Graph neural diffusion
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Beltrami flow and neural diffusion on graphs
Chamberlain B, Rowbottom J, Eynard D, Di Giovanni F, Dong X, Bronstein M · 2021
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Finetuned language models are zero-shot learners
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Scaling up visual and vision-language representation learning with noisy text supervision
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Afec: Active forgetting of negative transfer in continual learning
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Gcn-sl: Graph convolutional networks with structure learning for graphs under heterophily
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Graph structure estimation neural networks
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Multi-view contrastive graph clustering
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Graph debiased contrastive learning with joint representation clustering
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Robustness of deep learning models on graphs: A survey
Xu J, Chen J, You S, Xiao Z, Yang Y, Lu J · 2021
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Node-level membership inference attacks against graph neural networks
He X, Wen R, Wu Y, Backes M, Shen Y, Zhang Y · 2021
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Information obfuscation of graph neural networks
Liao P, Zhao H, Xu K, Jaakkola T, Gordon G J, Jegelka S, Salakhutdinov R · 2021
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Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information
Dai E, Wang S · 2021
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On dyadic fairness: Exploring and mitigating bias in graph connections
Li P, Wang Y, Zhao H, Hong P, Liu H · 2021
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Deep graph structure learning for robust representations: A survey
Zhu Y, Xu W, Zhang J, Liu Q, Wu S, Wang L · 2021
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Sparse graph attention networks
Ye Y, Ji S · 2021
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Rumor detection on social media with graph structured adversarial learning
Yang X, Lyu Y, Tian T, Liu Y, Liu Y, Zhang X · 2021
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Self-supervised graph learning for recommendation
Wu J, Wang X, Feng F, He X, Chen L, Lian J, Xie X · 2021
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A comprehensive survey of scene graphs: Generation and application
Chang X, Ren P, Xu P, Li Z, Chen X, Hauptmann A · 2021
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Deep just-in-time defect localization
Qiu F, Gao Z, Xia X, Lo D, Grundy J, Wang X · 2021
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Leveraging meta-path contexts for classification in heterogeneous information networks
Li X, Ding D, Kao B, Sun Y, Mamoulis N · 2021
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Heterogeneous graph structure learning for graph neural networks
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Heterogeneous graph propagation network
Ji H, Wang X, Shi C, Wang B, Philip S Y · 2021
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Understanding over-squashing and bottlenecks on graphs via curvature
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An interpretable graph generative model with heterophily
Chanpuriya S, Rossi R, Rao A, Mai T, Lipka N, Song Z, Musco C N · 2021
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Graph condensation for graph neural networks
Jin W, Zhao L, Zhang S, Liu Y, Tang J, Shah N · 2021
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Contrastive graph poisson networks: Semi-supervised learning with extremely limited labels
Wan S, Zhan Y, Liu L, Yu B, Pan S, Gong C · 2021
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Graphsmote: Imbalanced node classification on graphs with graph neural networks
Zhao T, Zhang X, Wang S · 2021
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A survey on multi-task learning
Zhang Y, Yang Q · 2021
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Deep graph convolutional reinforcement learning for financial portfolio management–deeppocket
Soleymani F, Paquet E · 2021
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Graph neural networks for graphs with heterophily: A survey
Zheng X, Liu Y, Pan S, Zhang M, Jin D, Yu P S · 2022
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A survey on vision transformer
Han K, Wang Y, Chen H, Chen X, Guo J, Liu Z, Tang Y, Xiao A, Xu C, Xu Y, others · 2022
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Transformers in vision: A survey
Khan S, Naseer M, Hayat M, Zamir S W, Khan F S, Shah M · 2022
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Transformer for graphs: An overview from architecture perspective
Min E, Chen R, Bian Y, Xu T, Zhao K, Huang W, Zhao P, Huang J, Ananiadou S, Rong Y · 2022
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Improving graph neural network expressivity via subgraph isomorphism counting
Bouritsas G, Frasca F, Zafeiriou S, Bronstein M M · 2022
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Graph self-supervised learning: A survey
Liu Y, Jin M, Pan S, Zhou C, Zheng Y, Xia F, Philip S Y · 2022
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Cavallo A, Grohnfeldt C, Russo M, Lovisotto G, Vassio L · 2022
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Revisiting heterophily for graph neural networks
Luan S, Hua C, Lu Q, Zhu J, Zhao M, Zhang S, Chang X W, Precup D · 2022
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Raw-gnn: Random walk aggregation based graph neural network
Jin D, Wang R, Ge M, He D, Li X, Lin W, Zhang W · 2022
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Understanding dropout for graph neural networks
Shu J, Xi B, Li Y, Wu F, Kamhoua C, Ma J · 2022
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Simplified graph convolution with heterophily
Chanpuriya S, Musco C · 2022
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Convolutional neural networks on graphs with chebyshev approximation, revisited
He M, Wei Z, Wen J R · 2022
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Powerful graph convolutional networks with adaptive propagation mechanism for homophily and heterophily
Wang T, Jin D, Wang R, He D, Huang Y · 2022
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G 2 cn: Graph gaussian convolution networks with concentrated graph filters
Mgnn: Graph neural networks inspired by distance geometry problem
Cui G, Wei Z · 2023
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Seedgnn: graph neural network for supervised seeded graph matching
Yu L, Xu J, Lin X · 2023
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Deep graph representation learning and optimization for influence maximization
Ling C, Jiang J, Wang J, Thai M T, Xue R, Song J, Qiu M, Zhao L · 2023
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Few-shot node classification with extremely weak supervision
Wang S, Dong Y, Ding K, Chen C, Li J · 2023
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Leveraging free labels to power up heterophilic graph learning in weakly-supervised settings: An empirical study
Wu X, Wu H, Wang R, Li D, Zhou X, Lu K · 2023
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Learning strong graph neural networks with weak information
Liu Y, Ding K, Wang J, Lee V, Liu H, Pan S · 2023
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Li M, Guo X, Wang Y, Wang Y, Lin Z · 2022
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Simplifying approach to node classification in graph neural networks
Maurya S K, Liu X, Murata T · 2022
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Hp-gmn: Graph memory networks for heterophilous graphs
Xu J, Dai E, Zhang X, Wang S · 2022
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From local to global: Spectral-inspired graph neural networks
Huang N, Villar S, Priebe C E, Zheng D, Huang C, Yang L, Braverman V · 2022
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Beyond homophily: Structure-aware path aggregation graph neural network
Sun Y, Deng H, Yang Y, Wang C, Xu J, Huang R, Cao L, Wang Y, Chen L · 2022
Cited alongside, same era.
Graph pointer neural networks
Yang T, Wang Y, Yue Z, Yang Y, Tong Y, Bai J · 2022
Cited alongside, same era.
Park J, Yun S, Park H, Kang J, Jeong J, Kim K M, Ha J W, Kim H J · 2022
Cited alongside, same era.
Later among the works it cites.
Auto-heg: Automated graph neural network on heterophilic graphs
Zheng X, Zhang M, Chen C, Zhang Q, Zhou C, Pan S · 2023
Later among the works it cites.
Foundation models for decision making: Problems, methods, and opportunities
Yang S, Nachum O, Du Y, Wei J, Abbeel P, Schuurmans D · 2023
Later among the works it cites.
Graph neural network generated metal-organic frameworks for carbon capture
Bayraktar Z, Molla S, Mahavadi S · 2023
Later among the works it cites.
Topological graph representation of stratigraphic properties of spatial-geological characteristics and compression modulus prediction by mechanism-driven learning
Wang M, Wang E, Liu X, Wang C · 2023
Later among the works it cites.
A novel deep learning method for automatic recognition of coseismic landslides
Yang Q, Wang X, Zhang X, Zheng J, Ke Y, Wang L, Guo H · 2023
Later among the works it cites.
Mamba: Linear-time sequence modeling with selective state spaces
Gu A, Dao T · 2023
Later among the works it cites.
Empower text-attributed graphs learning with large language models (llms)
Yu J, Ren Y, Gong C, Tan J, Li X, Zhang X · 2023
Later among the works it cites.
He X, Bresson X, Laurent T, Perold A, LeCun Y, Hooi B · 2023
Later among the works it cites.
Towards graph foundation models: A survey and beyond
Liu J, Yang C, Lu Z, Chen J, Li Y, Zhang M, Bai T, Fang Y, Sun L, Yu P S, others · 2023
Later among the works it cites.
Towards foundation models for knowledge graph reasoning
Galkin M, Yuan X, Mostafa H, Tang J, Zhu Z · 2023
Later among the works it cites.
When do graph neural networks help with node classification? investigating the homophily principle on node distinguishability
Luan S, Hua C, Xu M, Lu Q, Zhu J, Chang X W, Fu J, Leskovec J, Precup D · 2024
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Shehzad A, Xia F, Abid S, Peng C, Yu S, Zhang D, Verspoor K · 2024
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Benchmarking spectral graph neural networks: A comprehensive study on effectiveness and efficiency
Liao N, Liu H, Zhu Z, Luo S, Lakshmanan L V · 2024
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Towards graph prompt learning: A survey and beyond
Long Q, Yan Y, Zhang P, Fang C, Cui W, Ning Z, Xiao M, Cao N, Luo X, Xu L, others · 2024
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Luan S, Lu Q, Hua C, Wang X, Zhu J, Chang X W, Wolf G, Tang J · 2024
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Simple and asymmetric graph contrastive learning without augmentations
Xiao T, Zhu H, Chen Z, Wang S · 2024
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Characterizing graph datasets for node classification: Homophily-heterophily dichotomy and beyond
Platonov O, Kuznedelev D, Babenko A, Prokhorenkova L · 2024
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Lee S Y, Kim S, Bu F, Yoo J, Tang J, Shin K · 2024
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Affinity-based homophily: Can we measure homophily of a graph without using node labels?
Ojha I, Bose K, Das S · 2024
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What is missing in homophily? disentangling graph homophily for graph neural networks
Zheng Y, Luan S, Chen L · 2024
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Opengsl: A comprehensive benchmark for graph structure learning
Zhiyao Z, Zhou S, Mao B, Zhou X, Chen J, Tan Q, Zha D, Feng Y, Chen C, Wang C · 2024
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Classic gnns are strong baselines: Reassessing gnns for node classification
Luo Y, Shi L, Wu X M · 2024
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Luan S, Hua C, Lu Q, Ma L, Wu L, Wang X, Xu M, Chang X W, Precup D, Ying R, others · 2024
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Graph regulation network for point cloud segmentation
Du Z, Liang J, Liang J, Yao K, Cao F · 2024
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Figure: Simple and efficient unsupervised node representations with filter augmentations
Ekbote C, Deshpande A, Iyer A, Sellamanickam S, Bairi R · 2024
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Flexible diffusion scopes with parameterized laplacian for heterophilic graph learning
Lu Q, Zhu J, Luan S, Chang X W · 2024
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Pc-conv: Unifying homophily and heterophily with two-fold filtering
Li B, Pan E, Kang Z · 2024
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Optimizing polynomial graph filters: A novel adaptive krylov subspace approach
Huang K, Cao W, Ta H, Xiao X, Liò P · 2024
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Huang K, Wang Y G, Li M, others · 2024
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Node-wise filtering in graph neural networks: A mixture of experts approach
Han H, Li J, Huang W, Tang X, Lu H, Luo C, Liu H, Tang J · 2024
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Neighbors selective graph convolutional network for homophily and heterophily
Ai G, Gao Y, Wang H, Li X, Wang J, Yan H · 2024
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Knn-gnn: A powerful graph neural network enhanced by aggregating k-nearest neighbors in common subspace
Li L, Yang W, Bai S, Ma Z · 2024
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Differentiable cluster graph neural network
Dong Y, Dupty M H, Deng L, Liu Z, Goh Y L, Lee W S · 2024
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Revisiting the message passing in heterophilous graph neural networks
Zheng Z, Bei Y, Zhou S, Ma Y, Gu M, Xu H, Lai C, Chen J, Bu J · 2024
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Lg-gnn: Local-global adaptive graph neural network for modeling both homophily and heterophily
Yu Z, Feng B, He D, Wang Z, Huang Y, Feng Z · 2024
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Sign is not a remedy: Multiset-to-multiset message passing for learning on heterophilic graphs
Liang L, Kim S, Shin K, Xu Z, Pan S, Qi Y · 2024
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Breaking the entanglement of homophily and heterophily in semi-supervised node classification
Sun H, Li X, Wu Z, Su D, Li R H, Wang G · 2024
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Gnndld: Graph neural network with directional label distribution
Chaudhary C, Boran N K, Sangeeth N, Singh V · 2024
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Edge directionality improves learning on heterophilic graphs
Rossi E, Charpentier B, Di Giovanni F, Frasca F, Günnemann S, Bronstein M M · 2024
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Zhuo W, Tan G · 2024
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The heterophilic snowflake hypothesis: Training and empowering gnns for heterophilic graphs
Wang K, Zhang G, Zhang X, Fang J, Wu X, Li G, Pan S, Huang W, Liang Y · 2024
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Learning personalized scoping for graph neural networks under heterophily
Deng G, Zhou H, Kannan R, Prasanna V · 2024
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Heterophily-aware graph attention network
Wang J, Guo Y, Yang L, Wang Y · 2024
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Polyformer: Scalable node-wise filters via polynomial graph transformer
Ma J, He M, Wei Z · 2024
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Polynormer: Polynomial-expressive graph transformer in linear time
Deng C, Yue Z, Zhang Z · 2024
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Sigformer: Sign-aware graph transformer for recommendation
Chen S, Chen J, Zhou S, Wang B, Han S, Su C, Yuan Y, Wang C · 2024
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When transformer meets large graphs: An expressive and efficient two-view architecture
Kuang W, Wang Z, Wei Z, Li Y, Ding B · 2024
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Learning a mini-batch graph transformer via two-stage interaction augmentation
Li W, Chen K, Liu S, Zheng T, Huang W, Song M · 2024
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Vcr-graphormer: A mini-batch graph transformer via virtual connections
Fu D, Hua Z, Xie Y, Fang J, Zhang S, Sancak K, Wu H, Malevich A, He J, Long B · 2024
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Less is more: on the over-globalizing problem in graph transformers
Xing Y, Wang X, Li Y, Huang H, Shi C · 2024
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Ntformer: A composite node tokenized graph transformer for node classification
Chen J, Jiang S, He K · 2024
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Mpformer: Advancing graph modeling through heterophily relationship-based position encoding
Li D, Qi B, Gao J, Xiong H, Gu B, Chen X · 2024
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Graph triple attention network: A decoupled perspective
Wang X, Zhu Y, Shi H, Liu Y, Hong C · 2024
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A fractional graph laplacian approach to oversmoothing
Maskey S, Paolino R, Bacho A, Kutyniok G · 2024
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Unleashing the power of high-pass filtering in continuous graph neural networks
Zhang A, Li P · 2024
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Flexible graph neural diffusion with latent class representation learning
Wan L, Han H, Sun L, Zhang Z, Ning Z, Yan X, Xia F · 2024
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A generalized neural diffusion framework on graphs
Li Y, Wang X, Liu H, Shi C · 2024
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A survey on mixture of experts
Cai W, Jiang J, Wang F, Tang J, Kim S, Huang J · 2024
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Slog: An inductive spectral graph neural network beyond polynomial filter
Xu H, Yan Y, Wang D, Xu Z, Zeng Z, Abdelzaher T F, Han J, Tong H · 2024
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Predicting global label relationship matrix for graph neural networks under heterophily
Liang L, Hu X, Xu Z, Song Z, King I · 2024
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Mitigating oversmoothing through reverse process of gnns for heterophilic graphs
Park M, Heo J, Kim D · 2024
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A survey on self-supervised learning: Algorithms, applications, and future trends
Gui J, Chen T, Zhang J, Cao Q, Sun Z, Luo H, Tao D · 2024
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Graph contrastive learning under heterophily via graph filters
Yang W, Mirzasoleiman B · 2024
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Polygcl: Graph contrastive learning via learnable spectral polynomial filters
Chen J, Lei R, Wei Z · 2024
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Hetergcl: Graph contrastive learning framework on heterophilic graph
Wang C, Liu Y, Yang Y, Li W · 2024
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Gauss: Graph-customized universal self-supervised learning
Yang L, Hu W, Xu J, Shi R, He D, Wang C, Cao X, Wang Z, Niu B, Guo Y · 2024
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Efficient contrastive learning for fast and accurate inference on graphs
Xiao T, Zhu H, Zhang Z, Guo Z, Aggarwal C C, Wang S, Honavar V G · 2024
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S3gcl: Spectral, swift, spatial graph contrastive learning
Wan G, Tian Y, Huang W, Chawla N V, Ye M · 2024
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Improving graph contrastive learning via adaptive positive sampling
Zhuo J, Qin F, Cui C, Fu K, Niu B, Wang M, Guo Y, Wang C, Wang Z, Cao X, others · 2024
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Graph contrastive learning reimagined: Exploring universality
Zhuo J, Cui C, Fu K, Niu B, He D, Wang C, Guo Y, Wang Z, Cao X, Yang L · 2024
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Disambiguated node classification with graph neural networks
Zhao T, Zhang X, Wang S · 2024
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Redundancy is not what you need: An embedding fusion graph auto-encoder for self-supervised graph representation learning
Li M, Zhang Y, Wang S, Hu Y, Yin B · 2024
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Ugmae: A unified framework for graph masked autoencoders
Tian Y, Zhang C, Kou Z, Liu Z, Zhang X, Chawla N V · 2024
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Masked graph modeling with multi-view contrast
Luo Y, Li S, Sui Y, Wu J, Wu J, Wang X · 2024
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Masked graph autoencoders with contrastive augmentation for spatially resolved transcriptomics data
Fang D, Zhu F, Xie D, Min W · 2024
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Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs
Yu X, Liu Z, Fang Y, Liu Z, Chen S, Zhang X · 2024
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Prodigy: Enabling in-context learning over graphs
Huang Q, Ren H, Chen P, Kržmanc G, Zeng D, Liang P S, Leskovec J · 2024
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Prog: A graph prompt learning benchmark
Zi C, Zhao H, Sun X, Lin Y, Cheng H, Li J · 2024
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Universal prompt tuning for graph neural networks
Fang T, Zhang Y, Yang Y, Wang C, Chen L · 2024
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Subgraph-level universal prompt tuning
Lee J, Yang W, Kang J · 2024
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Multigprompt for multi-task pre-training and prompting on graphs
Yu X, Zhou C, Fang Y, Zhang X · 2024
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A unified graph selective prompt learning for graph neural networks
Jiang B, Wu H, Zhang Z, Wang B, Tang J · 2024
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Inductive graph alignment prompt: Bridging the gap between graph pre-training and inductive fine-tuning from spectral perspective
Yan Y, Zhang P, Fang Z, Long Q · 2024
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A novel prompt tuning for graph transformers: Tailoring prompts to graph topologies
Wang J, Deng Z, Lin T, Li W, Ling S · 2024
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Hetgpt: Harnessing the power of prompt tuning in pre-trained heterogeneous graph neural networks
Ma Y, Yan N, Li J, Mortazavi M, Chawla N V · 2024
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Hgprompt: Bridging homogeneous and heterogeneous graphs for few-shot prompt learning
Yu X, Fang Y, Liu Z, Zhang X · 2024
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Dygprompt: Learning feature and time prompts on dynamic graphs
Yu X, Liu Z, Fang Y, Zhang X · 2024
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Krait: A backdoor attack against graph prompt tuning
Song Y, Singh R, Palanisamy B · 2024
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Ddiprompt: Drug-drug interaction event prediction based on graph prompt learning
Wang Y, Xiong Y, Wu X, Sun X, Zhang J · 2024
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G-prompt: Graphon-based prompt tuning for graph classification
Duan Y, Liu J, Chen S, Chen L, Wu J · 2024
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Gaugllm: Improving graph contrastive learning for text-attributed graphs with large language models
Fang Y, Fan D, Zha D, Tan Q · 2024
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Killing two birds with one stone: Cross-modal reinforced prompting for graph and language tasks
Jiang W, Wu W, Zhang L, Yuan Z, Xiang J, Zhou J, Xiong H · 2024
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Urban region pre-training and prompting: A graph-based approach
Jin J, Song Y, Kan D, Zhu H, Sun X, Li Z, Sun X, Zhang J · 2024
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Gpt4rec: Graph prompt tuning for streaming recommendation
Zhang P, Yan Y, Zhang X, Kang L, Li C, Huang F, Wang S, Kim S · 2024
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Self-pro: A self-prompt and tuning framework for graph neural networks
Gong C, Li X, Yu J, Cheng Y, Tan J, Yu C · 2024
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Non-homophilic graph pre-training and prompt learning
Yu X, Zhang J, Fang Y, Jiang R · 2024
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Homophily-related: Adaptive hybrid graph filter for multi-view graph clustering
Wen Z, Ling Y, Ren Y, Wu T, Chen J, Pu X, Hao Z, He L · 2024
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Provable filter for real-world graph clustering
Xie X, Pan E, Kang Z, Chen W, Li B · 2024
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Boosting pseudo-labeling with curriculum self-reflection for attributed graph clustering
Zhu P, Li J, Wang Y, Xiao B, Zhang J, Lin W, Hu Q · 2024
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Link prediction under heterophily: A physics-inspired graph neural network approach
Di Francesco A G, Caso F, Bucarelli M S, Silvestri F · 2024
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Ld2: Scalable heterophilous graph neural network with decoupled embeddings
Liao N, Luo S, Li X, Shi J · 2024
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Ags-gnn: Attribute-guided sampling for graph neural networks
Das S S, Ferdous S, Halappanavar M M, Serra E, Pothen A · 2024
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Simplifying and empowering transformers for large-graph representations
Wu Q, Zhao W, Yang C, Zhang H, Nie F, Jiang H, Bian Y, Yan J · 2024
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Spikegraphormer: A high-performance graph transformer with spiking graph attention
Sun Y, Zhu D, Wang Y, Tian Z, Cao N, O’Hared G · 2024
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Universally robust graph neural networks by preserving neighbor similarity
Zhu Y, Lai Y, Ai X, Zhou K · 2024
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Refining latent homophilic structures over heterophilic graphs for robust graph convolution networks
Qiu C, Nan G, Xiong T, Deng W, Wang D, Teng Z, Sun L, Cui Q, Tao X · 2024
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Resurrecting label propagation for graphs with heterophily and label noise
Cheng Y, Shan C, Shen Y, Li X, Luo S, Li D · 2024
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Unveiling privacy vulnerabilities: Investigating the role of structure in graph data
Yuan H, Xu J, Wang C, Yang Z, Wang C, Yin K, Yang Y · 2024
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On provable privacy vulnerabilities of graph representations
Wu R, Fang G, Pan Q, Zhang M, Liu T, Wang W, Zhao W · 2024
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A data-centric graph neural network for node classification of heterophilic networks
Xue Y, Jin Z, Gao W · 2024
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Graph neural networks with soft association between topology and attribute
Yang Y, Sun Y, Wang S, Guo J, Gao J, Ju F, Yin B · 2024
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Make heterophilic graphs better fit gnn: A graph rewiring approach
Bi W, Du L, Fu Q, Wang Y, Han S, Zhang D · 2024
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Learning to model graph structural information on mlps via graph structure self-contrasting
Wu L, Lin H, Zhao G, Tan C, Li S Z · 2024
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Gslb: the graph structure learning benchmark
Li Z, Sun X, Luo Y, Zhu Y, Chen D, Luo Y, Zhou X, Liu Q, Wu S, Wang L, others · 2024
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Ta-detector: A gnn-based anomaly detector via trust relationship
Wen J, Jiang N, Li L, Zhou J, Li Y, Zhan H, Kou G, Gu W, Zhao J · 2024
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Truncated affinity maximization: One-class homophily modeling for graph anomaly detection
Qiao H, Pang G · 2024
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Zhang R, Cheng D, Liu X, Yang J, Ouyang Y, Wu X, Zheng Y · 2024
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Multi-view discriminative edge heterophily contrastive learning network for attributed graph anomaly detection
Jin W, Ma H, Zhang Y, Li Z, Chang L · 2024
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Gad-nr: Graph anomaly detection via neighborhood reconstruction
Roy A, Shu J, Li J, Yang C, Elshocht O, Smeets J, Li P · 2024
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Dga-gnn: Dynamic grouping aggregation gnn for fraud detection
Duan M, Zheng T, Gao Y, Wang G, Feng Z, Wang X · 2024
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Revisiting graph-based fraud detection in sight of heterophily and spectrum
Xu F, Wang N, Wu H, Wen X, Zhao X, Wan H · 2024
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Portable graph-based rumour detection against multi-modal heterophily
Nguyen T T, Ren Z, Nguyen T T, Jo J, Nguyen Q V H, Yin H · 2024
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Challenging low homophily in social recommendation
Jiang W, Gao X, Xu G, Chen T, Yin H · 2024
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Heterophily-aware fair recommendation using graph convolutional networks
Gholinejad N, Chehreghani M H · 2024
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Kumaraswamy wavelet for heterophilic scene graph generation
Chen L, Song Y, Lin S, Wang C, He G · 2024
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Joint homophily and heterophily relational knowledge distillation for efficient and compact 3d object detection
Chen S, Wei L, Liang L, Lang C · 2024
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Cgmega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection
Li H, Han Z, Sun Y, Wang F, Hu P, Gao Y, Bai X, Peng S, Ren C, Xu X, others · 2024
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Slgcn: Structure-enhanced line graph convolutional network for predicting drug–disease associations
Liu B M, Gao Y L, Li F, Zheng C H, Liu J X · 2024
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Molecule generation by heterophilious triple flows
Wang H, Solin A, Garg V · 2024
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Data-driven network neuroscience: On data collection and benchmark
Xu J, Yang Y, Huang D, Gururajapathy S S, Ke Y, Qiao M, Wang A, Kumar H, McGeown J, Kwon E · 2024
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Neurodegenerative brain network classification via adaptive diffusion with temporal regularization
Cho H, Sim J, Wu G, Kim W H · 2024
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On the heterophily of program graphs: A case study of graph-based type inference
Xu S, Shen J, Li Y, Yao Y, Yu P, Xu F, Ma X · 2024
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A just-in-time software defect localization method based on code graph representation
Zhang H, Min W, Wei Z, Kuang L, Gao H, Miao H · 2024
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Shen Z, Kang Z · 2024
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When heterophily meets heterogeneity: New graph benchmarks and effective methods
Lin J, Guo X, Zhang S, Zhou D, Zhu Y, Shun J · 2024
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Sheaf hypernetworks for personalized federated learning
Nguyen B, Sani L, Qiu X, Liò P, Lane N D · 2024
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Unig-encoder: A universal feature encoder for graph and hypergraph node classification
Zou M, Gan Z, Wang Y, Zhang J, Sui D, Guan C, Leng S · 2024
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Understanding heterophily for graph neural networks
Wang J, Guo Y, Yang L, Wang Y · 2024
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Demystifying structural disparity in graph neural networks: Can one size fit all?
Mao H, Chen Z, Jin W, Han H, Ma Y, Zhao T, Shah N, Tang J · 2024
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Leveraging invariant principle for heterophilic graph structure distribution shifts
Yang J, Chen Z, Xiao T, Zhang W, Lin Y, Kuang K · 2024
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On performance discrepancies across local homophily levels in graph neural networks
Loveland D, Zhu J, Heimann M, Fish B, Schaub M T, Koutra D · 2024
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Multi-track message passing: Tackling oversmoothing and oversquashing in graph learning via preventing heterophily mixing
Pei H, Li Y, Deng H, Hai J, Wang P, Ma J, Tao J, Xiong Y, Guan X · 2024
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Homophily modulates double descent generalization in graph convolution networks
Shi C, Pan L, Hu H, Dokmanić I · 2024
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Gao X, Yu J, Jiang W, Chen T, Zhang W, Yin H · 2024
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Influence maximization via graph neural bandits
Feng Y, Tan V Y, Cautis B · 2024
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Graphcbal: Class-balanced active learning for graph neural networks via reinforcement learning
Yu C, Zhu J, Li X · 2024
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Graphrare: Reinforcement learning enhanced graph neural network with relative entropy
Peng T, Wu W, Yuan H, Bao Z, Pengru Z, Yu X, Lin X, Liang Y, Pu Y · 2024
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Hc-gst: Heterophily-aware distribution consistency based graph self-training
Wang F, Zhao T, Xu J, Wang S · 2024
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Searching heterophily-agnostic graph neural networks
Wei L, He Z, Zhao H, Yao Q · 2024
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Unraveling the impact of heterophilic structures on graph positive-unlabeled learning
Wu Y, Yao J, Han B, Yao L, Liu T · 2024
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Can graph learning improve task planning?
Wu X, Shen Y, Shan C, Song K, Wang S, Zhang B, Feng J, Cheng H, Chen W, Xiong Y, others · 2024
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A review of graph neural networks in epidemic modeling
Liu Z, Wan G, Prakash B A, Lau M S, Jin W · 2024
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Graph mamba: Towards learning on graphs with state space models
Behrouz A, Hashemi F · 2024
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Kan: Kolmogorov-arnold networks
Liu Z, Wang Y, Vaidya S, Ruehle F, Halverson J, Soljačić M, Hou T Y, Tegmark M · 2024
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Continuous spiking graph neural networks
Yin N, Wan M, Shen L, Patel H L, Li B, Gu B, Xiong H · 2024
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Bridging local details and global context in text-attributed graphs
Wang Y, Zhu Y, Zhang W, Zhuang Y, Li Y, Tang S · 2024
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Gnns as adapters for llms on text-attributed graphs
Huang X, Han K, Yang Y, Bao D, Tao Q, Chai Z, Zhu Q · 2024
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Exploring the potential of large language models (llms) in learning on graphs
Chen Z, Mao H, Li H, Jin W, Wen H, Wei X, Wang S, Yin D, Fan W, Liu H, others · 2024
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Mao Q, Liu Z, Liu C, Li Z, Sun J · 2024
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Exploring the potential of large language models for heterophilic graphs
Wu Y, Li S, Fang Y, Shi C · 2024
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A survey of large language models for graphs
Ren X, Tang J, Yin D, Chawla N, Huang C · 2024
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Graph machine learning in the era of large language models (llms)
Fan W, Wang S, Huang J, Chen Z, Song Y, Tang W, Mao H, Liu H, Liu X, Yin D, others · 2024
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Position: Graph foundation models are already here
Mao H, Chen Z, Tang W, Zhao J, Ma Y, Zhao T, Shah N, Galkin M, Tang J · 2024
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Graphgpt: Graph instruction tuning for large language models
Tang J, Yang Y, Wei W, Shi L, Su L, Cheng S, Yin D, Huang C · 2024
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Opengraph: Towards open graph foundation models
Xia L, Kao B, Huang C · 2024
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Anygraph: Graph foundation model in the wild
Xia L, Huang C · 2024
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Beyond homophily: Neighborhood distribution-guided graph convolutional networks
Liu S, He D, Yu Z, Jin D, Feng Z · 2025
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Heterogeneous graph attention network
Wang X, Ji H, Shi C, Wang B, Ye Y, Cui P, Yu P S · 2032
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A framework for recommending accurate and diverse items using bayesian graph convolutional neural networks
Sun J, Guo W, Zhang D, Zhang Y, Regol F, Hu Y, Guo H, Tang R, Yuan H, He X, others · 2039
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Tree decomposed graph neural network
Wang Y, Derr T · 2049
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Simgcl: graph contrastive learning by finding homophily in heterophily
Liu C, Yu C, Gui N, Yu Z, Deng S · 2089
Closest in time.
Cross-context backdoor attacks against graph prompt learning
Lyu X, Han Y, Wang W, Qian H, Tsang I, Zhang X · 2094
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