You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
It is a probabilistic technique used for detecting small subgraph patterns (e.g., paths, cycles, trees) in large graphs efficiently. It provides an alternative to brute-force methods.
Detecting small subgraph patterns, such as a path of length k (i.e., a k-path) or a k-cycle, in an arbitrary graph G is NP-hard. A naive approach that checks all possible subsets of k vertices results in an exponential O(n^k ) time complexity, which is impractical for large graphs.
Color-Coding significantly reduces the time complexity to O(2^k ⋅poly(n)) by randomly assigning colors to vertices and then using dynamic programming to efficiently find the desired subgraph.
If available I would like to implement the algorithm in PyDataStructs.
The text was updated successfully, but these errors were encountered:
Color-Coding Algorithm
It is a probabilistic technique used for detecting small subgraph patterns (e.g., paths, cycles, trees) in large graphs efficiently. It provides an alternative to brute-force methods.
Detecting small subgraph patterns, such as a path of length k (i.e., a k-path) or a k-cycle, in an arbitrary graph G is NP-hard. A naive approach that checks all possible subsets of k vertices results in an exponential O(n^k ) time complexity, which is impractical for large graphs.
Color-Coding significantly reduces the time complexity to O(2^k ⋅poly(n)) by randomly assigning colors to vertices and then using dynamic programming to efficiently find the desired subgraph.
If available I would like to implement the algorithm in PyDataStructs.
The text was updated successfully, but these errors were encountered: