CODE: Building a Hypergraph & HyperGraph Transformers from Scratch скачать в хорошем качестве

CODE: Building a Hypergraph & HyperGraph Transformers from Scratch

Graphs are powerful—but many real-world systems don’t fit into pairwise relationships. That’s where hypergraphs come in. In this video, we build a hypergraph from scratch in code and then extend it into a HyperGraph Transformer, showing how attention mechanisms generalize beyond standard graphs. This is a code-first, intuition-driven walkthrough designed to help you understand why hypergraphs matter and how to implement them correctly. 🧠 What You’ll Learn 1️⃣ Why Graphs Are Not Enough Limitations of pairwise edges Real-world examples needing higher-order relations Why hyperedges change representation power 2️⃣ Hypergraph Fundamentals (Concept → Code) Nodes vs hyperedges Incidence matrices Bipartite graph representations Message passing in hypergraphs You’ll see how these ideas translate directly into code structures. 3️⃣ Building a Hypergraph Step-by-Step Defining nodes and hyperedges Encoding incidence relationships Computing hypergraph adjacency Preparing data for neural models This section focuses on implementation clarity, not just math. 4️⃣ From HyperGraphs to HyperGraph Transformers How attention generalizes to hyperedges Node–hyperedge–node attention flow Multi-head attention on higher-order structures Differences from standard Graph Transformers 5️⃣ Training & Use Cases When HyperGraph Transformers outperform graphs Applications in: Recommendation systems Bioinformatics Knowledge representation Multimodal reasoning Complex relational data 6️⃣ Common Pitfalls Hyperedge explosion Memory and compute tradeoffs When hypergraphs are unnecessary Debugging attention over hyperedges 🧠 Why HyperGraph Transformers Matter HyperGraph Transformers allow models to: Capture group interactions, not just pairs Learn richer structural representations Model complex systems more naturally They represent a shift from: Pairwise reasoning → Higher-order reasoning 🎯 Who This Video Is For ML engineers Graph & GNN researchers Advanced CS students AI practitioners working with structured data Anyone curious about what comes after graphs If you’re serious about structured learning and graph-based AI, this is essential knowledge. 👍 Like, share, and subscribe for deep dives into graph learning, transformers, AI research, and code-driven explanations. #Hypergraph #HyperGraphTransformer #GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #Transformers #AIResearch #MLEngineering #GraphLearning #StructuredData #NeuralNetworks #CodeTutorial #TechExplained #FutureOfAI