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Adaptive Subgraph Neural Network with Reinforced Critical Structure Mining

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-540-45167-9_11openalex-snapshot
On Graph Kernels: Hardness Results and Efficient Alternatives
inferredcites →https://doi.org/10.1007/s10618-014-0365-yopenalex-snapshot
Graph based anomaly detection and description: a survey
inferredcites →https://doi.org/10.1007/s41109-019-0195-3openalex-snapshot
A survey on graph kernels
inferredcites →https://doi.org/10.1016/j.dss.2020.113303openalex-snapshot
Fraud detection: A systematic literature review of graph-based anomaly detection approaches
inferredcites →https://doi.org/10.1016/s0925-2312(00)00324-6openalex-snapshot
Reinforcement Learning: An Introduction
inferredcites →https://doi.org/10.1021/jm00106a046openalex-snapshot
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity
inferredcites →https://doi.org/10.1038/nature14236openalex-snapshot
Human-level control through deep reinforcement learning
inferredcites →https://doi.org/10.1038/s42005-022-00863-wopenalex-snapshot
Investigating and modeling the dynamics of long ties
inferredcites →https://doi.org/10.1093/bioinformatics/btg130openalex-snapshot
Statistical evaluation of the Predictive Toxicology Challenge 2000–2001
inferredcites →https://doi.org/10.1093/bioinformatics/bti1007openalex-snapshot
Protein function prediction via graph kernels
inferredcites →https://doi.org/10.1103/physreve.69.066138openalex-snapshot
Estimating mutual information
inferredcites →https://doi.org/10.1103/revmodphys.42.272openalex-snapshot
Some Basic Definitions in Graph Theory
inferredcites →https://doi.org/10.1109/asonam.2018.8508729openalex-snapshot
Node, Motif and Subgraph: Leveraging Network Functional Blocks Through Structural Convolution
inferredcites →https://doi.org/10.1109/cvpr.2017.11openalex-snapshot
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
inferredcites →https://doi.org/10.1109/dsw.2018.8439897openalex-snapshot
MOTIFNET: A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS
inferredcites →https://doi.org/10.1109/icdm.2005.132openalex-snapshot
Shortest-Path Kernels on Graphs
inferredcites →https://doi.org/10.1109/icdm.2019.00177openalex-snapshot
Learning Robust Representations with Graph Denoising Policy Network
inferredcites →https://doi.org/10.1109/ijcnn.2005.1555942openalex-snapshot
A new model for learning in graph domains
inferredcites →https://doi.org/10.1109/tbdata.2018.2850013openalex-snapshot
Network Representation Learning: A Survey
inferredcites →https://doi.org/10.1109/tkde.2019.2957755openalex-snapshot
Subgraph Networks With Application to Structural Feature Space Expansion
inferredcites →https://doi.org/10.1109/tkde.2021.3118815openalex-snapshot
A Comprehensive Survey on Graph Anomaly Detection With Deep Learning
inferredcites →https://doi.org/10.1109/tnn.2008.2005605openalex-snapshot
The Graph Neural Network Model
inferredcites →https://doi.org/10.1109/tnnls.2020.2978386openalex-snapshot
A Comprehensive Survey on Graph Neural Networks
inferredcites →https://doi.org/10.1109/tpami.2007.70765openalex-snapshot
Graph-Based Semisupervised Learning
inferredcites →https://doi.org/10.1109/tpami.2020.2999032openalex-snapshot
Second-Order Pooling for Graph Neural Networks
inferredcites →https://doi.org/10.1109/tpami.2021.3054830openalex-snapshot
Graph Neural Networks with Convolutional ARMA Filters
inferredcites →https://doi.org/10.1109/tpami.2021.3062794openalex-snapshot
Topology-Aware Graph Pooling Networks
inferredcites →https://doi.org/10.1109/tpami.2021.3080635openalex-snapshot
Line Graph Neural Networks for Link Prediction
inferredcites →https://doi.org/10.1109/tpami.2021.3112205openalex-snapshot
Recognizing Predictive Substructures With Subgraph Information Bottleneck
inferredcites →https://doi.org/10.1109/tpami.2022.3144993openalex-snapshot
Reinforced, Incremental and Cross-Lingual Event Detection From Social Messages
inferredcites →https://doi.org/10.1109/tpami.2022.3154319openalex-snapshot
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
inferredcites →https://doi.org/10.1145/2783258.2783417openalex-snapshot
Deep Graph Kernels
inferredcites →https://doi.org/10.1145/321921.321925openalex-snapshot
An Algorithm for Subgraph Isomorphism
inferredcites →https://doi.org/10.1145/3292500.3330982openalex-snapshot
Graph Convolutional Networks with EigenPooling
inferredcites →https://doi.org/10.1145/3308558.3313461openalex-snapshot
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective
inferredcites →https://doi.org/10.1145/3340531.3411903openalex-snapshot
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
inferredcites →https://doi.org/10.1145/3357384.3357880openalex-snapshot
Graph Convolutional Networks with Motif-based Attention
inferredcites →https://doi.org/10.1145/3366423.3380112openalex-snapshot
Graph Representation Learning via Graphical Mutual Information Maximization
inferredcites →https://doi.org/10.1145/3394486.3403088openalex-snapshot
Policy-GNN: Aggregation Optimization for Graph Neural Networks
inferredcites →https://doi.org/10.1145/3442381.3449822openalex-snapshot
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism
inferredcites →https://doi.org/10.1145/3442381.3449953openalex-snapshot
Interpreting and Unifying Graph Neural Networks with An Optimization Framework
inferredcites →https://doi.org/10.1145/3490181openalex-snapshot
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks
inferredcites →https://doi.org/10.1145/3534678.3539366openalex-snapshot
Causal Attention for Interpretable and Generalizable Graph Classification
inferredcites →https://doi.org/10.1609/aaai.v29i1.9277openalex-snapshot
The Network Data Repository with Interactive Graph Analytics and Visualization
inferredcites →https://doi.org/10.1609/aaai.v32i1.11691openalex-snapshot
Adaptive Graph Convolutional Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v32i1.11782openalex-snapshot
An End-to-End Deep Learning Architecture for Graph Classification
inferredcites →https://doi.org/10.1609/aaai.v34i04.5987openalex-snapshot
Motif-Matching Based Subgraph-Level Attentional Convolutional Network for Graph Classification
inferredcites →https://doi.org/10.1609/aaai.v34i04.5997openalex-snapshot
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
inferredcites →https://doi.org/10.17863/cam.48429openalex-snapshot
Graph Attention Networks
inferredcites →https://doi.org/10.24963/ijcai.2018/483openalex-snapshot
Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
inferredcites →https://doi.org/10.24963/ijcai.2020/456openalex-snapshot
Neighbor Combinatorial Attention for Critical Structure Mining
inferredcites →https://doi.org/10.24963/ijcai.2020/472openalex-snapshot
Exploiting Mutual Information for Substructure-aware Graph Representation Learning
inferredcites →https://doi.org/10.4230/lipics.ecoop.2025.6openalex-snapshot
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
inferredcites →https://doi.org/10.4230/lipics.sea.2024.20openalex-snapshot
Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1312.6203openalex-snapshot
Spectral Networks and Locally Connected Networks on Graphs
inferredcites →https://doi.org/10.48550/arxiv.1606.00709openalex-snapshot
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
inferredcites →https://doi.org/10.48550/arxiv.1704.01212openalex-snapshot
Neural Message Passing for Quantum Chemistry
inferredcites →https://doi.org/10.48550/arxiv.1704.01665openalex-snapshot
Learning Combinatorial Optimization Algorithms over Graphs
inferredcites →https://doi.org/10.48550/arxiv.1805.08090openalex-snapshot
Graph Capsule Convolutional Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1805.11921openalex-snapshot
Anonymous Walk Embeddings
inferredcites →https://doi.org/10.48550/arxiv.1806.08804openalex-snapshot
Hierarchical Graph Representation Learning with Differentiable Pooling
inferredcites →https://doi.org/10.48550/arxiv.1808.06670openalex-snapshot
Learning deep representations by mutual information estimation and maximization
inferredcites →https://doi.org/10.48550/arxiv.1904.08082openalex-snapshot
Self-Attention Graph Pooling
inferredcites →https://doi.org/10.48550/arxiv.1904.09981openalex-snapshot
GraphNAS: Graph Neural Architecture Search with Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.1906.04477openalex-snapshot
Causal Discovery with Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.2006.10538openalex-snapshot
Subgraph Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2007.02133openalex-snapshot
Simple and Deep Graph Convolutional Networks
inferredcites →https://doi.org/10.48550/arxiv.2102.05152openalex-snapshot
On Explainability of Graph Neural Networks via Subgraph Explorations
inferredcites →https://doi.org/10.48550/arxiv.2201.08821openalex-snapshot
Representing Long-Range Context for Graph Neural Networks with Global Attention
inferredcites →https://doi.org/10.48550/arxiv.2202.03036openalex-snapshot
Structure-Aware Transformer for Graph Representation Learning
inferredcites →https://doi.org/10.5281/zenodo.1318390openalex-snapshot
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
inferredcites →https://doi.org/10.5281/zenodo.15206559openalex-snapshot
Formal Semantics for Kolmogorov-Arnold Network Representations of Operational Games
inferredcites →https://doi.org/10.5281/zenodo.20695702openalex-snapshot
RhizoDB: A Bounded Activation-Flow Architecture for Graph-Based Memory Systems

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Subgraph representation learning with self-attention and free adversarial training
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Crucial rather than random: Attacking crucial substructure for backdoor attacks on graph neural networks
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Subgraph autoencoder with bridge nodes
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AutoGSP: Automated graph-level representation learning via subgraph detection and propagation deceleration
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RSEA-MVGNN: Multi-view graph neural network with reliable structural enhancement and aggregation
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GL-BKGNN: Graphlet-based Bi-Kernel Interpretable Graph Neural Networks
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Reinforced Heterogeneous Graphlet Design for Knowledge Graph Representation Learning
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AutoMTNAS: Automated meta-reinforcement learning on graph tokenization for graph neural architecture search
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Improving Augmentation Consistency for Graph Contrastive Learning
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Intralayer structure reconstruction of general weighted output-coupling multilayer complex networks
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Subgraph-Aware Joint Modeling for Graph Classification via Efficient Frequent Pattern Mining and Fusion
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GSEK: Graph Structured Neural Networks with Energy Graph Kernel
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BK-MSSNN: BI-Kernel Based Multi-Scale Subgraph Neural Networks
inferred← citeshttps://doi.org/10.1109/icwapr63074.2024.10870507openalex-snapshot
IAGNN:Graph Neural Networks with Information Augmentation
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Interactive Semantics-Enhanced Vision-Language Model-Driven Hypergraph Reasoning for Robotic Decision-Making in Proactive Human–Robot Collaboration
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Extract and Refine Brain Subgraph for Disorder Analysis via Cross-Domain Learning
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ReiPool: Reinforced Pooling Graph Neural Networks for Graph-Level Representation Learning
inferred← citeshttps://doi.org/10.1109/tnnls.2023.3341802openalex-snapshot
Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration
inferred← citeshttps://doi.org/10.1109/tnse.2025.3526340openalex-snapshot
Local Optimization Policy for Link Prediction via Reinforcement Learning
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IBCS: Learning Information Bottleneck-Constrained Denoised Causal Subgraph for Graph Classification
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ComS2T: A Complementary Spatiotemporal Learning System for Data-Adaptive Model Evolution
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Robust Multidimensional Graph Neural Networks for Signal Processing in Wireless Communications With Edge-Graph Information Bottleneck
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Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification
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Critical Structure-aware Graph Neural Networks
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Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator
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Progressive Stacking for Scalable Graph Condensation
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Counterfactual Analysis on Large Graphs
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A dynamic graph-based PU-learning approach for sequential recommendation in rare disease detection
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