Subgraph example sentences

"Subgraph" Example Sentences

1. The subgraph induced by the vertices {A, B, C} is a complete graph.
2. Finding the maximum clique in a subgraph is an NP-hard problem.
3. This algorithm efficiently identifies all connected subgraphs within the network.
4. A subgraph isomorphism test is computationally expensive for large graphs.
5. Each node in the main graph has a corresponding subgraph in the detailed representation.
6. The subgraph shows a clear clustering pattern.
7. We extracted a relevant subgraph from the massive dataset.
8. The largest connected subgraph represents the main component of the system.
9. Is this subgraph a tree?
10. Removing edges can create disconnected subgraphs.
11. The analysis focused on a specific subgraph of interest.
12. Several algorithms exist for finding minimum spanning subgraphs.
13. The induced subgraph preserves all edges between selected vertices.
14. Every tree is a subgraph of a complete graph.
15. This subgraph is planar.
16. The subgraph highlights the critical path.
17. Identifying all maximal subgraphs satisfying a given property is challenging.
18. The subgraph matching problem is computationally complex.
19. He proved that this subgraph is Hamiltonian.
20. We constructed a subgraph representing the communication network.
21. The research paper focuses on algorithms for finding specific types of subgraphs.
22. Each subgraph represents a distinct cluster in the data.
23. The algorithm iteratively refines the subgraph until convergence.
24. The subgraph's structure revealed important insights.
25. Finding the shortest path within a subgraph is computationally efficient.
26. Isomorphic subgraphs have identical structures.
27. The subgraph contains a cycle of length five.
28. The analysis of this subgraph is crucial for understanding the system's behavior.
29. The subgraph represents a component of the system.
30. This subgraph is acyclic.
31. The team developed a novel algorithm for finding specific subgraphs within large networks.
32. The subgraph is dense.
33. The subgraph is sparse.
34. The highlighted subgraph depicts a critical portion of the network.
35. This subgraph is bipartite.
36. The subgraph provides a simplified representation of the complex network.
37. Several subgraphs were analyzed to identify recurring patterns.
38. Each subgraph was evaluated for its structural properties.
39. The algorithm identifies the largest connected subgraph in polynomial time.
40. The subgraph’s diameter is relatively small.
41. The subgraph demonstrates a hierarchical structure.
42. Analyzing the subgraph revealed hidden relationships.
43. The minimum spanning subgraph has minimal total edge weight.
44. The induced subgraph is not connected.
45. We're focusing our efforts on this particular subgraph for now.
46. The subgraph’s properties significantly impact the overall network performance.
47. Are there any other interesting subgraphs within this larger network?
48. This subgraph is a strong contender for the optimal solution.
49. The size of the subgraph affects the computational complexity of the algorithm.
50. Further research will explore the properties of this specific subgraph in greater detail.