pith. machine review for the scientific record. sign in

arxiv: 1609.02907 · v4 · submitted 2016-09-09 · 💻 cs.LG · stat.ML

Recognition: 2 theorem links

Semi-Supervised Classification with Graph Convolutional Networks

Max Welling, Thomas N. Kipf

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:23 UTC · model grok-4.3

classification 💻 cs.LG stat.ML
keywords graph convolutional networkssemi-supervised learningnode classificationspectral graph convolutionscitation networksknowledge graphsgraph neural networks
0
0 comments X

The pith

Localized first-order graph convolutions enable scalable semi-supervised node classification by encoding structure and features.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a convolutional neural network variant that works directly on graphs for semi-supervised classification tasks. It derives an efficient layer from a first-order approximation to spectral graph convolutions, allowing information to propagate along edges while keeping computation linear in the number of edges. This produces node representations that combine local connectivity patterns with input features. Experiments show clear accuracy gains over prior methods on citation networks and knowledge graphs where labels are sparse. If the approach holds, it provides a practical way to apply deep learning to relational data without full supervision or expensive global computations.

Core claim

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.

What carries the argument

The graph convolutional layer using renormalized adjacency matrix multiplication to perform a localized first-order approximation of spectral convolution.

If this is right

  • The model trains and infers in time linear with the number of edges, enabling use on large sparse graphs.
  • Stacked layers propagate information across multiple hops of neighbors while remaining efficient.
  • Performance improves when both node features and graph edges are used jointly rather than separately.
  • The same architecture applies across citation networks and knowledge graphs with similar gains.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approximation may extend to other graph tasks such as link prediction or graph classification with minimal changes.
  • It suggests that full spectral methods are often unnecessary for practical node-level prediction on real networks.
  • Inductive variants could be derived to handle new nodes without retraining the full model.

Load-bearing premise

That a first-order localized approximation of spectral graph convolutions captures enough structure to support accurate semi-supervised classification on the tested citation and knowledge graphs.

What would settle it

A dataset where labels depend on higher-order or global graph patterns and the model shows no accuracy gain over non-graph or higher-order baselines.

read the original abstract

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript proposes Graph Convolutional Networks (GCNs) for semi-supervised node classification on graph-structured data. It motivates the architecture via a localized first-order approximation of spectral graph convolutions (leading to a simple propagation rule based on the renormalized adjacency matrix), shows that the model scales linearly with the number of edges, and learns hidden representations encoding both local structure and node features. Experiments on citation networks (Cora, Citeseer, Pubmed) and the NELL knowledge graph demonstrate that the approach outperforms baselines such as label propagation and manifold regularization by a significant margin.

Significance. If the central claims hold, this is a significant contribution that bridges spectral graph theory with practical neural network design, yielding a scalable and effective method for graph-based semi-supervised learning. The linear scaling, avoidance of expensive eigendecompositions, and strong empirical results on standard benchmarks are clear strengths; the model has become foundational in graph neural network research with extensive subsequent adoption and reproduction.

minor comments (3)
  1. [§2.2] §2.2, Eq. (8): The renormalization trick (adding self-loops and symmetric normalization) is introduced to address numerical issues, but a short sentence explaining its effect on the spectrum would improve accessibility for readers without deep spectral graph theory background.
  2. [Table 1] Table 1: Reporting standard deviations or results from multiple random seeds would strengthen the claim of consistent outperformance over baselines.
  3. [§3.1] §3.1: The hyperparameter selection procedure (e.g., for the number of hidden units or dropout) could be described in more detail to support full reproducibility.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of our manuscript, accurate summary of the proposed Graph Convolutional Networks approach, and recommendation to accept. We appreciate the recognition of the model's linear scaling, avoidance of eigendecompositions, and empirical results on standard benchmarks.

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained

full rationale

The paper's core derivation in Section 2 starts from the spectral graph convolution definition (citing Bruna et al. and Defferrard et al.), applies a first-order Chebyshev polynomial approximation to obtain a localized filter, and arrives at the renormalized propagation rule H^{(l+1)} = σ(Â H^{(l)} W^{(l)}) via algebraic simplification and the addition of self-loops for stability. This step is a direct mathematical reduction from prior spectral theory and does not invoke self-citations, fitted parameters renamed as predictions, or ansatzes smuggled from the authors' own prior work. Experiments in Section 3 are independent empirical evaluations on citation networks and NELL, with no load-bearing claim reducing to the model's own inputs by construction. The architecture choice is motivated externally and remains falsifiable against baselines.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of approximating spectral graph convolutions with a first-order localized form and the assumption that graph structure aids semi-supervised classification.

axioms (1)
  • domain assumption A localized first-order approximation of spectral graph convolutions is valid and sufficient for the semi-supervised classification task.
    Explicitly stated as the motivation for the convolutional architecture in the abstract.

pith-pipeline@v0.9.0 · 5370 in / 1281 out tokens · 90957 ms · 2026-05-10T15:23:36.615244+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 60 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?

    cs.CR 2026-05 accept novelty 8.0

    GraphIP-Bench shows stealing GNNs is easy at moderate query budgets, most defenses fail to block or reliably trace extraction, and watermarks lose verification power on surrogates while heterophilic graphs are harder ...

  2. Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables

    cs.LG 2026-05 unverdicted novelty 7.0

    Quantum-inspired observables reveal poor signal routing in standard spectral GNNs and motivate Schrödinger GNNs with superior propagation capacity.

  3. Supervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis

    cs.LG 2026-05 unverdicted novelty 7.0

    SD3MF learns deep hierarchical factorizations of multimodal brain graphs with a shared latent space and encoder-decoder training to jointly reconstruct graphs and predict supervised outcomes.

  4. \emph{DRIFT}: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts

    cs.LG 2026-05 accept novelty 7.0

    DRIFT is a benchmark for task-free continual graph learning under continuous distribution shifts, demonstrating that standard methods degrade without task boundary information.

  5. Weather-Robust Cross-View Geo-Localization via Prototype-Based Semantic Part Discovery

    cs.CV 2026-05 unverdicted novelty 7.0

    SkyPart uses learnable prototypes for patch grouping, altitude modulation only in training, graph-attention readout, and Kendall-weighted loss to set new state-of-the-art single-pass performance on SUES-200, Universit...

  6. GriNNder: Breaking the Memory Capacity Wall in Full-Graph GNN Training with Storage Offloading

    cs.DC 2026-05 unverdicted novelty 7.0

    GriNNder uses structured storage offloading with partition-wise caching and gradient regathering to train full-graph GNNs on limited memory, achieving up to 9.78x speedup over baselines.

  7. TopoU-Net: a U-Net architecture for topological domains

    cs.LG 2026-05 unverdicted novelty 7.0

    TopoU-Net is a rank-path U-Net for combinatorial complexes that encodes by lifting cochains upward along incidences, decodes by transporting downward, and merges via skip connections at matched ranks.

  8. CTQWformer: A CTQW-based Transformer for Graph Classification

    cs.LG 2026-05 unverdicted novelty 7.0

    CTQWformer fuses continuous-time quantum walks into a graph transformer and recurrent module to outperform standard GNNs and graph kernels on classification benchmarks.

  9. Unified Modeling of Lane and Lane Topology for Driving Scene Reasoning

    cs.CV 2026-05 unverdicted novelty 7.0

    UniTopo unifies lane detection and topology reasoning into a single perception model, outperforming prior methods on OpenLane-V2 benchmarks with TOP_ll scores of 30.1% and 31.8%.

  10. Value-Decomposed Reinforcement Learning Framework for Taxiway Routing with Hierarchical Conflict-Aware Observations

    cs.AI 2026-05 unverdicted novelty 7.0

    CaTR applies value-decomposed RL with hierarchical conflict-aware observations to achieve better safety-efficiency trade-offs than planning, optimization, and standard RL baselines in a realistic airport taxiway simulation.

  11. Structural Interpretations of Protein Language Model Representations via Differentiable Graph Partitioning

    cs.LG 2026-05 unverdicted novelty 7.0

    SoftBlobGIN combines ESM-2 representations with protein contact graphs via a lightweight GNN and differentiable substructure pooling to achieve 92.8% accuracy on enzyme classification, raise binding-site AUROC to 0.98...

  12. Robustness of Graph Self-Supervised Learning to Real-World Noise: A Case Study on Text-Driven Biomedical Graphs

    cs.LG 2026-05 unverdicted novelty 7.0

    Feature reconstruction in GSSL is robust to noise in text-driven biomedical graphs while relation reconstruction is sensitive, with bidirectional GNN architectures performing better on noisy data and yielding up to 7%...

  13. Local Truncation Error-Guided Neural ODEs for Large Scale Traffic Forecasting

    cs.LG 2026-05 unverdicted novelty 7.0

    LTE-ODE repurposes local truncation error as an unsupervised dynamic attention mask that preserves continuous Neural ODE evolution in stable regions while triggering discrete compensation only at anomaly points in lar...

  14. LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning

    cs.LG 2026-05 unverdicted novelty 7.0

    LUMINA-Bench is a standardized evaluation framework for ACOPF surrogate models that tests generalization across multiple grid topologies using accuracy and physics-constraint metrics.

  15. A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis

    cs.CL 2026-05 unverdicted novelty 7.0

    Presents MBFC-2025 dataset and multi-view embeddings with fusion methods for media bias and factuality, reporting SOTA results on ACL-2020 and new benchmarks on MBFC-2025.

  16. Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment

    cs.SI 2026-05 unverdicted novelty 7.0

    DRSA provides a plug-and-play alignment framework that decouples features and relations to prevent type collapse and relation confusion in heterogeneous graph foundation models.

  17. Aitchison Embeddings for Learning Compositional Graph Representations

    cs.LG 2026-05 unverdicted novelty 7.0

    Graph nodes are embedded as simplex compositions via ILR coordinates to yield intrinsically interpretable representations that preserve Aitchison geometry and enable subcompositional analysis.

  18. Advancing Edge Classification through High-Dimensional Causal Modeling of Node-Edge Interplay

    cs.LG 2026-05 unverdicted novelty 7.0

    CECF is a new causal framework for edge classification that balances high-dimensional edge features against node influences via GNN embeddings and cross-attention to achieve better performance than standard methods.

  19. Learning Neural Operator Surrogates for the Black Hole Accretion Code

    astro-ph.HE 2026-04 unverdicted novelty 7.0

    Physics-informed Fourier neural operators recover plasmoid formation in sparse SRRMHD vortex data where data-only models fail, and transformer operators approximate AMR jet evolution, marking first reported uses in th...

  20. GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA

    cs.CV 2026-04 conditional novelty 7.0

    GraphLeap decouples per-layer graph construction from feature updates in Vision GNNs by using previous-layer features for the current graph, enabling pipelined FPGA acceleration with up to 95.7× CPU speedup after fine-tuning.

  21. Break the Optimization Barrier of LLM-Enhanced Recommenders: A Theoretical Analysis and Practical Framework

    cs.IR 2026-04 unverdicted novelty 7.0

    TF-LLMER resolves optimization barriers in LLM-enhanced recommenders through embedding normalization and Rec-PCA that aligns semantic representations with collaborative co-occurrence graphs.

  22. Concept Graph Convolutions: Message Passing in the Concept Space

    cs.LG 2026-04 unverdicted novelty 7.0

    Concept Graph Convolutions perform message passing on node concepts to increase interpretability of graph neural networks without losing task performance.

  23. HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification

    cs.CL 2026-04 unverdicted novelty 7.0

    HopRank is a self-supervised LLM-tuning method that turns node classification into link prediction via hierarchical hop-based preference sampling, matching supervised GNN performance with zero labeled data on text-att...

  24. Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap

    cs.SE 2026-04 unverdicted novelty 7.0

    Atropos uses GCN on inference graphs for early failure prediction and hotswaps to larger LLMs, achieving 74% of large-model performance at 24% cost.

  25. Continual Learning for fMRI-Based Brain Disorder Diagnosis via Functional Connectivity Matrices Generative Replay

    q-bio.TO 2026-04 conditional novelty 7.0

    A structure-aware VAE generates realistic FC matrices for replay, combined with multi-level knowledge distillation and hierarchical contextual bandit sampling, to enable continual fMRI-based brain disorder diagnosis a...

  26. FlexVector: A SpMM Vector Processor with Flexible VRF for GCNs on Varying-Sparsity Graphs

    cs.DC 2026-04 unverdicted novelty 7.0

    FlexVector achieves 3.78x speedup and 40.5% lower energy for GCN inference on five real-world datasets by using flexible VRFs and graph preprocessing to match varying-sparsity graphs.

  27. Graph-PiT: Enhancing Structural Coherence in Part-Based Image Synthesis via Graph Priors

    cs.CV 2026-04 unverdicted novelty 7.0

    Graph-PiT adds graph priors and a hierarchical GNN to part-based image synthesis to enforce relational constraints and improve structural coherence over vanilla PiT.

  28. Graph Topology Information Enhanced Heterogeneous Graph Representation Learning

    cs.LG 2026-04 unverdicted novelty 7.0

    ToGRL learns high-quality graph structures from raw heterogeneous graphs via a two-stage topology extraction process and prompt tuning, outperforming prior methods on five datasets.

  29. Complex-Valued GNNs for Distributed Basis-Invariant Control of Planar Systems

    cs.LG 2026-04 unverdicted novelty 7.0

    Complex-valued GNNs using phase-equivariant activations achieve global basis invariance for distributed planar control, outperforming real-valued baselines in data efficiency, tracking, and generalization on flocking.

  30. Neural Operator: Graph Kernel Network for Partial Differential Equations

    cs.LG 2020-03 unverdicted novelty 7.0

    Graph Kernel Networks learn PDE solution operators that generalize across discretization methods and grid resolutions using graph-based kernel integration.

  31. FRAME: Forensic Routing and Adaptive Multi-path Evidence Fusion for Image Manipulation Detection

    cs.CV 2026-05 unverdicted novelty 6.0

    FRAME adaptively routes images through multiple forensic paths and fuses complementary evidence to improve manipulation detection and localization.

  32. Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation

    cs.LG 2026-05 unverdicted novelty 6.0

    Counterfactual metrics on semi-simulated benchmarks fail to identify the treatment effect estimators preferred by observable metrics on real datasets, with simple meta-learners outperforming specialized causal models.

  33. Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning

    cs.LG 2026-05 unverdicted novelty 6.0

    GTLM injects graph-aware attention biases into LLMs using only 0.015% extra parameters, enabling native graph processing that matches 7B models with a 1B model on text-attributed graph benchmarks.

  34. TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation

    cs.LG 2026-05 unverdicted novelty 6.0

    TrajDLM applies block diffusion language models to discrete road-segment sequences with topology constraints to generate realistic trajectories up to 2.8 times faster than prior methods while supporting zero-shot transfer.

  35. Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach

    cs.AI 2026-05 unverdicted novelty 6.0

    L3-PPI reformulates PPI pair classification as graph classification over a prompt graph with controlled virtual L3 paths to inject the biological interaction prior and boost performance on existing models.

  36. Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach

    cs.AI 2026-05 unverdicted novelty 6.0

    L3-PPI reformulates protein-protein interaction prediction as a graph classification task over a prompt graph containing virtual L3 paths to incorporate biological complementarity prior and improve performance.

  37. Quantum Injection Pathways for Implicit Graph Neural Networks

    quant-ph 2026-05 unverdicted novelty 6.0

    Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and...

  38. Hierarchical Perfusion Graphs for Tumor Heterogeneity Modeling in Glioma Molecular Subtyping

    cs.CV 2026-05 unverdicted novelty 6.0

    HiPerfGNN uses VQ-VAE on DSC perfusion curves to form hierarchical tumor habitat graphs that predict IDH mutation (AUC 0.96 internal, 0.89 external), 1p/19q codeletion, and WHO grade.

  39. Uncovering and Shaping the Latent Representation of 3D Scene Topology in Vision-Language Models

    cs.CV 2026-05 unverdicted novelty 6.0

    VLMs possess a latent 3D scene topology subspace corresponding to Laplacian eigenmaps that can be causally shaped via Dirichlet energy regularization to improve spatial task performance by up to 12.1%.

  40. GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model

    cs.AI 2026-05 unverdicted novelty 6.0

    GCCM prevents shortcut collapse in consistency models for graph prediction by using contrastive negative pairs and input feature perturbation, leading to better performance than deterministic baselines.

  41. A Unified Benchmark for Evaluating Knowledge Graph Construction Methods and Graph Neural Networks

    cs.LG 2026-05 unverdicted novelty 6.0

    A dual-purpose benchmark supplies two text-derived knowledge graphs and one expert reference graph on the same biomedical corpus to jointly measure construction method quality and GNN robustness via semi-supervised no...

  42. From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists

    cs.CR 2026-05 unverdicted novelty 6.0

    musicPIIrate uses Deep Sets and Graph Neural Networks to infer PII from playlists, outperforming baselines in 9 of 15 tasks, with JamShield defense reducing accuracy by ~10%.

  43. Actionable Real-Time Modeling of Surgical Team Dynamics via Time-Expanded Interaction Graphs

    cs.AI 2026-05 unverdicted novelty 6.0

    Time-expanded interaction graphs with graph neural networks enable real-time prediction of surgical procedure duration deviations from team communication patterns and support counterfactual identification of beneficia...

  44. H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction

    cs.SI 2026-05 unverdicted novelty 6.0

    H3 is a new three-hop index that predicts physician referrals using normalized indirect pathways and outperforms heuristics and neural nets on Medicare shared-patient data in both within-period and cross-period settings.

  45. Batch Normalization for Neural Networks on Complex Domains

    cs.LG 2026-05 unverdicted novelty 6.0

    Batch normalization layers are introduced for neural networks on complex domains with practical implementations for the Siegel disk and tested on radar, graph, and action recognition tasks.

  46. Exploring Sparse Matrix Multiplication Kernels on the Cerebras CS-3

    cs.DC 2026-04 unverdicted novelty 6.0

    Cerebras CS-3 achieves up to 100x speedup over CPU for SpMM and 20x for SDDMM at 90% sparsity, with performance improving for larger matrices, but becomes slower than CPU beyond 99% sparsity.

  47. Improving Graph Few-shot Learning with Hyperbolic Space and Denoising Diffusion

    cs.LG 2026-04 unverdicted novelty 6.0

    IMPRESS improves graph few-shot learning by learning representations in hyperbolic space and using denoising diffusion to better approximate target distributions from few support samples.

  48. Semi-Markov Reinforcement Learning for City-Scale EV Ride-Hailing with Feasibility-Guaranteed Actions

    cs.AI 2026-04 unverdicted novelty 6.0

    A robust semi-Markov RL agent with MILP feasibility projection and Wasserstein ambiguity set achieves $1.22M net profit on an NYC EV simulator with zero feeder violations, outperforming heuristic and other RL baselines.

  49. AnalogRetriever: Learning Cross-Modal Representations for Analog Circuit Retrieval

    cs.CV 2026-04 unverdicted novelty 6.0

    AnalogRetriever maps schematics, descriptions, and netlists of analog circuits into one embedding space and achieves 75.2% average Recall@1 across six retrieval directions while boosting an agentic design framework.

  50. A Hybrid Reinforcement and Self-Supervised Learning Aided Benders Decomposition Algorithm

    eess.SY 2026-04 unverdicted novelty 6.0

    A hybrid RL and self-supervised learning method accelerates generalized Benders decomposition by 57.5% on a MINLP case study while recovering optimal solutions.

  51. Physics-Conditioned Synthesis of Internal Ice-Layer Thickness for Incomplete Layer Traces

    cs.LG 2026-04 unverdicted novelty 6.0

    A physics-conditioned neural network with geometric and transformer components synthesizes complete ice-layer thickness from incomplete radar traces using a mask-aware loss, preserving observed values and improving do...

  52. TACENR: Task-Agnostic Contrastive Explanations for Node Representations

    cs.LG 2026-04 unverdicted novelty 6.0

    TACENR introduces a contrastive-learning method that identifies the most influential attribute, proximity, and structural features in node representations in a task-agnostic manner.

  53. LoReC: Rethinking Large Language Models for Graph Data Analysis

    cs.LG 2026-04 unverdicted novelty 6.0

    LoReC enhances LLMs for graph tasks via attention redistribution, graph re-injection into FFN, and logit rectification, yielding improvements over GraphLLM and GNN baselines on diverse datasets.

  54. DuConTE: Dual-Granularity Text Encoder with Topology-Constrained Attention for Text-attributed Graphs

    cs.CL 2026-04 unverdicted novelty 6.0

    DuConTE is a dual-granularity text encoder that incorporates graph topology into language model attention for improved node representations in text-attributed graphs.

  55. Unveiling Stochasticity: Universal Multi-modal Probabilistic Modeling for Traffic Forecasting

    cs.LG 2026-04 unverdicted novelty 6.0

    A drop-in Gaussian Mixture Model output layer converts deterministic traffic models to multi-modal probabilistic predictors trained solely with negative log-likelihood loss.

  56. Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval

    cs.AI 2026-04 unverdicted novelty 6.0

    A structured survey organizing graph-LLM integration methods by purpose, modality, and strategy across application domains.

  57. Modern Structure-Aware Simplicial Spatiotemporal Neural Network

    cs.LG 2026-04 unverdicted novelty 6.0

    ModernSASST is the first simplicial complex-based spatiotemporal model that combines random walks on high-dimensional complexes with parallelizable temporal convolutional networks for efficient high-order topology capture.

  58. NK-GAD: Neighbor Knowledge-Enhanced Unsupervised Graph Anomaly Detection

    cs.LG 2026-04 unverdicted novelty 6.0

    NK-GAD improves unsupervised graph anomaly detection on heterophilic graphs by combining a joint encoder for similar and dissimilar neighbors, neighbor reconstruction, center aggregation, and dual decoders, yielding a...

  59. TopFeaRe: Locating Critical State of Adversarial Resilience for Graphs Regarding Topology-Feature Entanglement

    cs.CR 2026-04 unverdicted novelty 6.0

    TopFeaRe models graph adversarial attacks as oscillations in a complex dynamic system and locates the critical resilience state via equilibrium-point theory applied to a two-dimensional topology-feature entangled function.

  60. UniDetect: LLM-Driven Universal Fraud Detection across Heterogeneous Blockchains

    cs.CR 2026-04 unverdicted novelty 6.0

    UniDetect is an LLM-based system that generates universal transaction summary texts and uses two-stage multimodal training on text plus graphs to detect fraudulent accounts across heterogeneous blockchains, outperform...

Reference graph

Works this paper leans on

32 extracted references · 32 canonical work pages · cited by 104 Pith papers

  1. [1]

    TensorFlow : Large-scale machine learning on heterogeneous systems, 2015

    Mart\' n Abadi et al. TensorFlow : Large-scale machine learning on heterogeneous systems, 2015

    work page 2015

  2. [2]

    Diffusion-convolutional neural networks

    James Atwood and Don Towsley. Diffusion-convolutional neural networks. In Advances in neural information processing systems (NIPS), 2016

    work page 2016

  3. [3]

    Manifold regularization: A geometric framework for learning from labeled and unlabeled examples

    Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of machine learning research (JMLR), 7 0 (Nov): 0 2399--2434, 2006

    work page 2006

  4. [4]

    On modularity clustering

    Ulrik Brandes, Daniel Delling, Marco Gaertler, Robert Gorke, Martin Hoefer, Zoran Nikoloski, and Dorothea Wagner. On modularity clustering. IEEE Transactions on Knowledge and Data Engineering, 20 0 (2): 0 172--188, 2008

    work page 2008

  5. [5]

    Spectral networks and locally connected networks on graphs

    Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. Spectral networks and locally connected networks on graphs. In International Conference on Learning Representations (ICLR), 2014

    work page 2014

  6. [6]

    Hruschka Jr, and Tom M

    Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R. Hruschka Jr, and Tom M. Mitchell. Toward an architecture for never-ending language learning. In AAAI, volume 5, pp.\ 3, 2010

    work page 2010

  7. [7]

    Convolutional neural networks on graphs with fast localized spectral filtering

    Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in neural information processing systems (NIPS), 2016

    work page 2016

  8. [8]

    Brendan L. Douglas. The W eisfeiler- L ehman method and graph isomorphism testing. arXiv preprint arXiv:1101.5211, 2011

    work page arXiv 2011

  9. [9]

    Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Al \'a n Aspuru-Guzik, and Ryan P

    David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Al \'a n Aspuru-Guzik, and Ryan P. Adams. Convolutional networks on graphs for learning molecular fingerprints. In Advances in neural information processing systems (NIPS), pp.\ 2224--2232, 2015

    work page 2015

  10. [10]

    Understanding the difficulty of training deep feedforward neural networks

    Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. In AISTATS, volume 9, pp.\ 249--256, 2010

    work page 2010

  11. [11]

    A new model for learning in graph domains

    Marco Gori, Gabriele Monfardini, and Franco Scarselli. A new model for learning in graph domains. In Proceedings. 2005 IEEE International Joint Conference on Neural Networks., volume 2, pp.\ 729--734. IEEE, 2005

    work page 2005

  12. [12]

    node2vec: Scalable feature learning for networks

    Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016

    work page 2016

  13. [13]

    Hammond, Pierre Vandergheynst, and R \'e mi Gribonval

    David K. Hammond, Pierre Vandergheynst, and R \'e mi Gribonval. Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis, 30 0 (2): 0 129--150, 2011

    work page 2011

  14. [14]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016

    work page 2016

  15. [15]

    Transductive inference for text classification using support vector machines

    Thorsten Joachims. Transductive inference for text classification using support vector machines. In International Conference on Machine Learning (ICML), volume 99, pp.\ 200--209, 1999

    work page 1999

  16. [16]

    Kingma and Jimmy Lei Ba

    Diederik P. Kingma and Jimmy Lei Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR), 2015

    work page 2015

  17. [17]

    Gated graph sequence neural networks

    Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. Gated graph sequence neural networks. In International Conference on Learning Representations (ICLR), 2016

    work page 2016

  18. [18]

    Link-based classification

    Qing Lu and Lise Getoor. Link-based classification. In International Conference on Machine Learning (ICML), volume 3, pp.\ 496--503, 2003

    work page 2003

  19. [19]

    Visualizing data using t-sne

    Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research (JMLR), 9 0 (Nov): 0 2579--2605, 2008

    work page 2008

  20. [20]

    Corrado, and Jeff Dean

    Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (NIPS), pp.\ 3111--3119, 2013

    work page 2013

  21. [21]

    Learning convolutional neural networks for graphs

    Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. Learning convolutional neural networks for graphs. In International Conference on Machine Learning (ICML), 2016

    work page 2016

  22. [22]

    Deepwalk: Online learning of social representations

    Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.\ 701--710. ACM, 2014

    work page 2014

  23. [23]

    The graph neural network model

    Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model. IEEE Transactions on Neural Networks, 20 0 (1): 0 61--80, 2009

    work page 2009

  24. [24]

    Collective classification in network data

    Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. Collective classification in network data. AI magazine, 29 0 (3): 0 93, 2008

    work page 2008

  25. [25]

    Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov

    Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research (JMLR), 15 0 (1): 0 1929--1958, 2014

    work page 1929

  26. [26]

    Line: Large-scale information network embedding

    Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. Line: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web, pp.\ 1067--1077. ACM, 2015

    work page 2015

  27. [27]

    Boris Weisfeiler and A. A. Lehmann. A reduction of a graph to a canonical form and an algebra arising during this reduction. Nauchno-Technicheskaya Informatsia, 2 0 (9): 0 12--16, 1968

    work page 1968

  28. [28]

    Deep learning via semi-supervised embedding

    Jason Weston, Fr \'e d \'e ric Ratle, Hossein Mobahi, and Ronan Collobert. Deep learning via semi-supervised embedding. In Neural Networks: Tricks of the Trade, pp.\ 639--655. Springer, 2012

    work page 2012

  29. [29]

    Revisiting semi-supervised learning with graph embeddings

    Zhilin Yang, William Cohen, and Ruslan Salakhutdinov. Revisiting semi-supervised learning with graph embeddings. In International Conference on Machine Learning (ICML), 2016

    work page 2016

  30. [30]

    Wayne W. Zachary. An information flow model for conflict and fission in small groups. Journal of anthropological research, pp.\ 452--473, 1977

    work page 1977

  31. [31]

    Learning with local and global consistency

    Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Sch \"o lkopf. Learning with local and global consistency. In Advances in neural information processing systems (NIPS), volume 16, pp.\ 321--328, 2004

    work page 2004

  32. [32]

    Semi-supervised learning using gaussian fields and harmonic functions

    Xiaojin Zhu, Zoubin Ghahramani, and John Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In International Conference on Machine Learning (ICML), volume 3, pp.\ 912--919, 2003

    work page 2003