FilterMoE uses joint node-channel routing of Chebyshev filter experts through a 3D gating tensor in pre-propagation GNNs and outperforms baselines on nine of eleven benchmarks while ranking first on all three large-scale ones with a 1.53-point average gain.
Sign: Scalable inception graph neural networks
11 Pith papers cite this work. Polarity classification is still indexing.
11
Pith papers citing it
citation-role summary
baseline 1
citation-polarity summary
roles
baseline 1polarities
baseline 1representative citing papers
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
WG-SRC is a white-box signal-subspace classifier that decomposes graph node classification into interpretable components to produce operational fingerprints distinguishing dataset behaviors like low-pass dominance or high-pass noise.
Temporal Graph Networks combine memory modules and graph operators to learn on dynamic graphs as timed event sequences, outperforming prior methods on transductive and inductive tasks while unifying earlier models as special cases.
FedLAB organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context via federated semantic barycenter pre-training, improving performance by up to 7.53% on benchmarks while enabling semantic traceability.
TGHE packs structurally identical ego-graph trees into shared CKKS ciphertexts for parallel encrypted GNN inference, delivering 66.9x speedup on a 3.7M-node financial graph with <0.002 AUC loss.
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
CAMPA resolves modal conflicts in decoupled multimodal GNNs via cross-modal aligned propagation and trajectory aligned aggregation, outperforming coupled and decoupled baselines on benchmarks while retaining efficiency.
HMH builds soft hierarchies with orthonormal Haar bases and heterophily-aware encoders to apply learnable spectral filters while using skip unpooling to avoid oversmoothing and hub bias on heterophilous graphs.
Robust diffusion operators and hidden-state re-propagation improve PPGNN accuracy to match message-passing GNNs on benchmarks.
AdvSynGNN uses multi-resolution structural synthesis, contrastive objectives, an adaptive transformer, and an adversarial propagation engine with residual label correction to improve node-level predictions on challenging graph topologies.
citing papers explorer
-
Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs
FilterMoE uses joint node-channel routing of Chebyshev filter experts through a 3D gating tensor in pre-propagation GNNs and outperforms baselines on nine of eleven benchmarks while ranking first on all three large-scale ones with a 1.53-point average gain.
-
Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
-
Operational Feature Fingerprints of Graph Datasets via a White-Box Signal-Subspace Probe
WG-SRC is a white-box signal-subspace classifier that decomposes graph node classification into interpretable components to produce operational fingerprints distinguishing dataset behaviors like low-pass dominance or high-pass noise.
-
Temporal Graph Networks for Deep Learning on Dynamic Graphs
Temporal Graph Networks combine memory modules and graph operators to learn on dynamic graphs as timed event sequences, outperforming prior methods on transductive and inductive tasks while unifying earlier models as special cases.
-
FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning
FedLAB organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context via federated semantic barycenter pre-training, improving performance by up to 7.53% on benchmarks while enabling semantic traceability.
-
TGHE: Template-based Graph Homomorphic Encryption for Privacy-Preserving GNN Inference in Edge-Cloud Systems
TGHE packs structurally identical ego-graph trees into shared CKKS ciphertexts for parallel encrypted GNN inference, delivering 66.9x speedup on a 3.7M-node financial graph with <0.002 AUC loss.
-
Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
-
CAMPA: Efficient and Aligned Multimodal Graph Learning via Decoupled Propagation and Aggregation
CAMPA resolves modal conflicts in decoupled multimodal GNNs via cross-modal aligned propagation and trajectory aligned aggregation, outperforming coupled and decoupled baselines on benchmarks while retaining efficiency.
-
Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation
HMH builds soft hierarchies with orthonormal Haar bases and heterophily-aware encoders to apply learnable spectral filters while using skip unpooling to avoid oversmoothing and hub bias on heterophilous graphs.
-
Revisiting Pre-Propagation GNNs: Robust Diffusion Operators and Hidden-State Re-Propagation
Robust diffusion operators and hidden-state re-propagation improve PPGNN accuracy to match message-passing GNNs on benchmarks.
-
AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation
AdvSynGNN uses multi-resolution structural synthesis, contrastive objectives, an adaptive transformer, and an adversarial propagation engine with residual label correction to improve node-level predictions on challenging graph topologies.