Hierarchical Graph Masked AutoEncoders (Hi-GMAE): A Novel Multi-Scale GMAE Framework Designed to Handle the Hierarchical Structures within Graph

Graph Self-supervised Pre-training (GSP) Techniques In graph analysis, labeled data is a challenge for traditional supervised learning. Graph Self-supervised Pre-training (GSP) techniques overcome this by extracting meaningful representations from graph data without labeled examples. Contrastive and Generative GSP Methods GSP methods fall into two categories: contrastive and generative. Contrastive methods create multiple graph views and learn by contrasting positive and negative samples. Generative methods focus on learning node representations through reconstruction. Hierarchical Graph Masked AutoEncoders (Hi-GMAE) Hi-GMAE captures hierarchical information in graphs with multi-scale coarsening, CoFi masking with recovery, and Fi-Co encoder and decoder. Validation and Performance Hi-GMAE outperforms existing models in contrastive and generative pre-training, showing superior capability in capturing and leveraging hierarchical graph information. Practical AI Solutions Identify Automation Opportunities, Define KPIs, Select an AI Solution, and Implement Gradually. Contact us at hello@itinai.com for AI KPI management advice and stay updated on our Telegram t.me/itinainews or Twitter @itinaicom for continuous insights into leveraging AI. AI Sales Bot Explore the AI Sales Bot from itinai.com/aisalesbot for 24/7 customer engagement and managing interactions across all customer journey stages. List of Useful Links: AI Lab in Telegram @itinai – free consultation Twitter – @itinaicom