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Multi-view Graph Representation Learning Beyond Homophily
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-030-58574-7_13openalex-snapshot
Learning Canonical Representations for Scene Graph to Image Generation
inferredcites →https://doi.org/10.1007/978-3-642-04898-2_643openalex-snapshot
Information Theory and Statistics
inferredcites →https://doi.org/10.1007/s00521-021-05966-zopenalex-snapshot
Dual attentive graph neural network for metro passenger flow prediction
inferredcites →https://doi.org/10.1016/s0925-2312(97)90018-7openalex-snapshot
Advances in neural information processing systems 7
inferredcites →https://doi.org/10.1073/pnas.1713019115openalex-snapshot
Multiscale mixing patterns in networks
inferredcites →https://doi.org/10.1093/comnet/cnab014openalex-snapshot
Multi-Scale attributed node embedding
inferredcites →https://doi.org/10.1103/physreve.67.026126openalex-snapshot
Mixing patterns in networks
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2019.00981openalex-snapshot
Label Efficient Semi-Supervised Learning via Graph Filtering
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01549openalex-snapshot
Exploring Simple Siamese Representation Learning
inferredcites →https://doi.org/10.1109/jcai15434.2009openalex-snapshot
2009 International Joint Conference on Artificial Intelligence
inferredcites →https://doi.org/10.1109/msp.2017.2693418openalex-snapshot
Geometric Deep Learning: Going beyond Euclidean data
inferredcites →https://doi.org/10.1109/tkde.69openalex-snapshot
IEEE Transactions on Knowledge and Data Engineering
inferredcites →https://doi.org/10.1145/3397191openalex-snapshot
On Proximity and Structural Role-based Embeddings in Networks
inferredcites →https://doi.org/10.1145/3442381.3449953openalex-snapshot
Interpreting and Unifying Graph Neural Networks with An Optimization Framework
inferredcites →https://doi.org/10.24963/ijcai.2019/594openalex-snapshot
ProNE: Fast and Scalable Network Representation Learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s40747-025-02136-9openalex-snapshot
Homophily-aware multi-view graph clustering via multi-order filtering
inferred← citeshttps://doi.org/10.1007/s44443-026-00693-8openalex-snapshot
From nodes to communities: a novel dual graph learning framework for community detection in attributed networks
inferred← citeshttps://doi.org/10.1016/j.asoc.2024.112656openalex-snapshot
LSPI: Heterogeneous graph neural network classification aggregation algorithm based on size neighbor path identification
inferred← citeshttps://doi.org/10.1016/j.cie.2025.111122openalex-snapshot
Temporal transaction network anomaly detection for Industrial Internet of Things with federated graph neural networks
inferred← citeshttps://doi.org/10.1016/j.ibmed.2026.100374openalex-snapshot
KAMDA: A multi-view Kolmogorov-Arnold Network integrating pre-trained BERT embeddings and similarity-based imputation for microbe-drug association prediction
inferred← citeshttps://doi.org/10.1016/j.ins.2024.120916openalex-snapshot
CAT: A causal graph attention network for trimming heterophilic graphs
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.114037openalex-snapshot
Self-supervised contrastive learning for heterophilic graph with latent recurring pattern embedding
inferred← citeshttps://doi.org/10.1109/eccst68196.2025.11441007openalex-snapshot
Self-Supervised Representation Learning and Structured Knowledge Mining for Heterogeneous Multi-Source Data
inferred← citeshttps://doi.org/10.1109/icde65448.2025.00288openalex-snapshot
Efficient Integration of Multi-View Attributed Graphs for Clustering and Embedding
inferred← citeshttps://doi.org/10.1145/3819818openalex-snapshot
Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning
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