This comprehensive guide details Graph Learning Techniques, starting with foundational graph theory and advancing to modern methodologies like Graph Fourier Transform analysis. The resource emphasizes state-of-the-art approaches for analyzing diverse graph signals, particularly focusing on band-limited data. Crucially, it explores robust privacy preservation methods for safeguarding latent graph structures and stimuli. Practical applications demonstrate effectiveness in complex network analysis, including human brain dynamics and modeling global COVID-19 spread patterns.