Recently I had to quickly come up with Python code that found the longest path through a weighted DAG (directed acylic graph). This particular DAG had a single start node and a single end node, but there were multiple weighted paths from the start to the end. "Longest path" in this context meant the path with the highest sum of weights.
To begin with, I coded up an exhuastive algorithm for trying all possible paths. This wouldn't be fast enough in the general case, but it didn't take long to write and is useful for comparison.
Internet rumour (contradicted by Wikipedia I now see) had it that the well-known Dijkstra's algorithm for the shortest path could be adapted for longest paths (but only in a DAG) by using the negative of the weights. However, a test of a couple of libraries for doing so indicated that negative weights were not supported (segfault with R for example), and I didn't want to code Dijkstra myself, so I decided to hunt around the internet for a direct solution to the longest path problem.
Geeksforgeeks (appropriately enough I guess) had an lucid description of a 3-step algorithm which I implemented. The only snag was that Step 1 involved a topological sort. Again, I didn't want to spend any time figuring this out so I looked for an implementation in Python and found one at ActiveState's Python recipes. I note that another popular Python implementation is available at PyPI.
Here's the code (if you are using an RSS reader to read this, you may have to click through to the original blog post):