Knowledge Inference for Graphs

Related lectures (30)

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Explores entity disambiguation, graph embeddings, scoring functions, and learning methods.

Explores Prim's algorithm for minimum spanning trees and introduces the Traveling Salesman Problem.

Covers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

Covers the problem statement of assembly, precision requirements, common couplings, stability, and spatial vectors.

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Covers polynomial identity testing using oracles and random point evaluation, with applications in graph theory and algorithmic aspects.

Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

Explores the transition from sparse to dense graphs and their real-world applications.

Explores coordination in networked control systems, graph theory, and consensus algorithms.