PDF Viewer

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (89)

[1]
HGphormer: Heterophilic Graph Transformer
2025Jianshe Wu, Yaolin Liu et al.
[2]
HopGAT: A multi-hop graph attention network with heterophily and degree awareness
2025Hang Zhang, Huan Wang et al.
[3]
GFformer: A Graph Transformer for Extracting All Frequency Information from Large-scale Graphs
2025Qi Zhang, Mengmeng Si et al.
[4]
Pruning Spurious Subgraphs for Graph Out-of-Distribtuion Generalization
2025Tianjun Yao, Haoxuan Li et al.
[5]
Self-supervised graph feature enhancement and scale attention for mechanical signal node-level representation and diagnosis
2025Xin Zhang, Jie Liu et al.
[6]
DruGNNosis-MoA: Elucidating Drug Mechanisms as Etiological or Palliative With Graph Neural Networks Employing a Large Language Model
2025Liad Brettler, Eden Berman et al.
[7]
Plain Transformers Can be Powerful Graph Learners
2025Liheng Ma, Soumyasundar Pal et al.
[8]
FDphormer: Beyond Homophily with Feature-Difference Position Encoding
2025Dong Li, Aijia Zhang et al.
[9]
Adversarial Contrastive Graph Augmentation with Counterfactual Regularization
2025Tao Long, Lei Zhang et al.
[10]
NLGT: Neighborhood-based and Label-enhanced Graph Transformer Framework for Node Classification
2025Xiaolong Xu, Yibo Zhou et al.
[11]
Multi-scale graph diffusion convolutional network for multi-view learning
2025Shiping Wang, Jiacheng Li et al.
[12]
Diffusion on Graph: Augmentation of Graph Structure for Node Classification
2025Yancheng Wang, Changyu Liu et al.
[13]
Denoising Structure against Adversarial Attacks on Graph Representation Learning
2025Na Chen, Ping Li et al.
[14]
Toward Effective Digraph Representation Learning: A Magnetic Adaptive Propagation based Approach
2025Xunkai Li, Daohan Su et al.
[15]
Nonlinear Correct and Smooth for Graph-Based Semi-Supervised Learning
2025Yuanhang Shao, Xiuwen Liu
[16]
Towards Scalable and Deep Graph Neural Networks via Noise Masking
2024Yuxuan Liang, Wentao Zhang et al.
[17]
Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs
2024Junjie Huang, Jiarui Qin et al.
[18]
Proformer: a scalable graph transformer with linear complexity
2024Zhu Liu, Peng Wang et al.
[19]
Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?
2024Zhongjian Zhang, Xiao Wang et al.
[20]
Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiency
2024Xuexin Chen, Ruichu Cai et al.

Showing 20 of 89 references

Founder's Pitch

"AdvSynGNN enhances graph neural networks' resilience and accuracy in noisy structures through adversarial synthesis and self-corrective propagation."

Graph Neural NetworksScore: 5View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

1/4 signals

2.5

Series A Potential

0/4 signals

0

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/19/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.