1 Introduction  
2 Notation and Framework  
2.1 Graph-theoretic Notation  
2.2 Probabilistic Tools  
2.3 Concentration Bounds for Decomposable Functions  
2.4 Concentration Bounds for General Lipschitz Functions  
3 Generalization for Learning from Graph-dependent Data  
3.1 Generalization Bounds via Fractional Rademacher Complexity  
3.2 Generalization Bounds via Algorithmic Stability  
4 Applications  
4.1 Bipartite Ranking  
4.2 Multi-class Classification  
4.3 Learning from m-dependent Data  
5 Concluding Remarks