Dijkstra algorithm [76] was used to calculate the shortest paths from a source keypoint to all the vertices, by iteratively growing the set of vertices q until it returns the shortest path.
Ge islands preferentially grow at the vertices of arrowhead-like steps, aligning along the center of a mesa.
On 1.5-μm-wide mesas, on the other hand, the resulting steps have a zigzag-shaped pattern and Ge islands also preferentially grow at the vertices of zigzag steps.
Unfortunately as the number of vertices increase, the running time of these algorithms grow exponentially and thus they become impractical.
BFS can be performed with run time linear in terms of the number of edges and vertices, O ( N + M ), and space complexity linear in terms of N. Given that most social networks will be small-world and scale-free, the number of edges will not grow too fast with the number of vertices.
Once constructed, the quadratic serendipity functions span the {1,x,y,x2,xy,y2} space of functions and grow by 2n on a mesh element where n is the number of the polygon's vertices.
BfsEnumP1-3 take a parameter d, grow a family tree up to depth d as BfsSimEnum does, and assign numbers to the vertices (molecular trees) in depth d by BFS order.
Although finding a shortest path on a DAG has a polynomial complexity in the number of vertices and edges, this number grows exponentially as the size of the network and initial data traffic load increase.
The algorithm iterates through all the vertices of the mesh and grows a new region N i each time it finds a vertex that does not belong to any of the regions M or N i already created (new seed point).
In general, the problem of finding all minimal k-cores continues to be difficult to solve due to the fact the number of minimal k-cores in a graph grows with the number of vertices and edges.
