View PDF ↗
PDF Viewer
Open Full PDF

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 (18)

[1]
DVGMAE: Self-Supervised Dynamic Variational Graph Masked Autoencoder
2025Mengzhou Gao, Xinxun Zhang et al.
[2]
DyGMAE: A Novel Dynamic Graph Masked Autoencoder for Link Prediction
2025Weixiong Liu, Junwei Cheng et al.
[3]
DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs
2024Jiasheng Zhang, Jialin Chen et al.
[4]
CLDG: Contrastive Learning on Dynamic Graphs
2023Yiming Xu, Bin Shi et al.
[5]
Towards Better Dynamic Graph Learning: New Architecture and Unified Library
2023Le Yu, Leilei Sun et al.
[6]
Towards Better Evaluation for Dynamic Link Prediction
2022Farimah Poursafaei, Shenyang Huang et al.
[7]
TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning
2021Lu Wang, Xiaofu Chang et al.
[8]
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
2021Yanbang Wang, Yen-Yu Chang et al.
[9]
Temporal Graph Networks for Deep Learning on Dynamic Graphs
2020Emanuele Rossi, B. Chamberlain et al.
[10]
Inductive Representation Learning on Temporal Graphs
2020Da Xu, Chuanwei Ruan et al.
[11]
DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks
2020Aravind Sankar, Yanhong Wu et al.
[12]
TinyBERT: Distilling BERT for Natural Language Understanding
2019Xiaoqi Jiao, Yichun Yin et al.
[13]
Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
2019Srijan Kumar, Xikun Zhang et al.
[14]
DyRep: Learning Representations over Dynamic Graphs
2019Rakshit S. Trivedi, Mehrdad Farajtabar et al.
[15]
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
2019A. Pareja, Giacomo Domeniconi et al.
[16]
Representation Learning with Contrastive Predictive Coding
2018Aäron van den Oord, Yazhe Li et al.
[17]
Attention is All you Need
2017Ashish Vaswani, Noam Shazeer et al.
[18]
Gaussian Error Linear Units (GELUs)
2016Dan Hendrycks, Kevin Gimpel

Founder's Pitch

"DyGnROLE leverages role-aware modeling to enhance dynamic graph learning for asymmetrically behaving nodes."

Graph LearningScore: 5View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

3/4 signals

7.5

Series A Potential

1/4 signals

2.5

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: 2/26/2026

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.