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Interactive Graph Theory Learning

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Algorithm Selection

Maximal Clique

Finds largest complete subgraph (all vertices connected)

Time: O(3ⁿ/³)
Space: O(V)
Use Case: Social network analysis, bioinformatics, data mining

Algorithm Execution

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About Maximal Clique

A clique is a set of vertices that are all pairwise connected. A maximal clique cannot be extended by adding another vertex, and finding all maximal cliques, or the single largest one, is a fundamental NP-hard problem in network analysis.

How it works

The Bron-Kerbosch algorithm enumerates all maximal cliques by recursive backtracking over three sets: the current clique R, candidates P that connect to all of R, and excluded vertices X already covered. Choosing a good pivot dramatically prunes the recursion, and processing vertices in degeneracy order gives the best known enumeration bound of O(3 to the power n/3), matching the maximum possible number of maximal cliques.

Applications

Clique detection finds tightly knit communities in social networks, protein interaction complexes in biology, correlated assets in finance, and co-purchased product groups in recommender systems. Clique problems are also the standard vehicle for teaching NP-completeness reductions.

Read the full article: Applications of Graph Theory in the Real World

Related algorithms: Graph Coloring, Chordality Check, Bipartite Check

Interactive Controls
Basic Actions
Double Click → Add Node
Drag → Move Nodes
Shift + Click → Connect Nodes
Right Click → Context Menu
Advanced
Ctrl + Click → Multi-Select
Delete Key → Remove Selected
Double Click Edge → Edit Weight
Ctrl + Drag → Pan View

Zoom Controls

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Nodes: 4
Edges: 4