2019年10月15日 · Well, topological sorting is a specific order of the nodes of a directed acyclic graph, which can be calculated by depth-first search. Besides depth-first search, there are other methods to find a valid order, like the Kahn's algorighm. Note that a DAG might have many valid topological orders (not restricted to one).

2018年3月26日 · Topological Sort means you are given a list of jobs and list of prerequisites and you have to figure out the ordering of jobs. jobs = [1,2,3] prerequisites = [[1,2], [1, 3], [3,2]] result = [1,3,2] should be the order in which jobs should execute. here [1,2] signifies that job 2 cannot be started until job 1 is completed (job 1 is a prerequisite).

2011年11月17日 · Well, the trick with topological sorting is that it fails automatically if there's a cycle. Therefore the topological sort itself is a test for DAGness (and I don't think there's a simpler one). So instead of first testing whether the graph is a DAG, you just try to topologically sort it.

2013年6月17日 · Inspired by Arnauds answer and other interesting topological sorting algorithms have I created the cyclic-toposort project and published it on github. cyclic_toposort does exactly what you desire in that it quickly sorts a directed cyclic graph providing the minimal amount of violating edges. It optionally also provides the maximum groupings of ...

2016年7月6日 · To print all solutions of topological sort, we follow the following approach: Initialize all vertices as unmarked. Now choose vertex which is unmarked and has zero indegree and decrease indegree of all those vertices by 1 (corresponding to removing edges).

The typical way to handle topological sorting is by using a graph, and for each iteration remove all nodes which doesn't have an inbound connection, and simultaneously remove all connections outbound from those nodes. The removed nodes are the output of the iteration. Repeat until you cannot remove any more nodes.

2014年8月31日 · Algorithm for topological sorting if cycles exist. 1. Topological Sort pseudocode. 2.

2013年2月23日 · from collections import defaultdict # importing defaultdict def topological_sort(graph,b,a): # defining function T = [] visited = [] in_degree = [] for i in range(a+1): in_degree.append(0) # initialising the degree of each vertex =0 visited.append(0) # initialising all the vertics unvisited for i in range(1,a+1): for j in graph[i]: in_degree[j ...

Ok, so in topological sorting depending on the input data, there's usually multiple correct solutions for which order the graph can be "processed" so that all dependencies come before nodes that are "dependent" on them. However, I'm looking for a slightly different answer:

In the example, the visitation order (the topological sort is): a c f h e d i b g If num_heads ever arrives at a state when there are no nodes without incoming edges, then it means there is a cycle that cannot be topologically sorted and the algorithm exits.