directed-graph

We are given a graph with the following facts: edge(a,b) edge(a,c) edge(b,a) edge(c,d) edge(d,d) edge(d,e) edge(e,f) edge(f,g) edge(g,e) And we are asked to define a rule, cycle(X) , that determines if there is a cycle starting from the node X . I am really lost on how to do this, I tried attempting to traverse the nodes and checking if the next one would be the starting one again but I cannot seem to get it to work Karoly Horvath Archie's idea is a good starting point, but it will create an infinite loop if it finds another cycle while searching for the path. I also haven't used prolog for

In short, I need a fast algorithm to count how many acyclic paths are there in a simple directed graph. By simple graph I mean one without self loops or multiple edges. A path can start from any node and must end on a node that has no outgoing edges. A path is acyclic if no edge occurs twice in it. My graphs (empirical datasets) have only between 20-160 nodes, however, some of them have many cycles in them, therefore there will be a very large number of paths, and my naive approach is simply not fast enough for some of the graph I have. What I'm doing currently is "descending" along all

I have an application that creates many thousands of graphs in memory per second. I wish to find a way to persist these for subsequent querying. They aren't particularly large (perhaps max ~1k nodes). I need to be able to store the entire graph object including node attributes and edge attributes. I then need to be able to search for graphs within specific time windows based on a time attribute in a node. Is there a simple way to coerce this data into neo4j ? I've yet to find any examples of this. Though I have found several python libs including an embedded neo4j and a rest client. Is the

I have a list of items (blue nodes below) which are categorized by the users of my application. The categories themselves can be grouped and categorized themselves. The resulting structure can be represented as a Directed Acyclic Graph (DAG) where the items are sinks at the bottom of the graph's topology and the top categories are sources. Note that while some of the categories might be well defined, a lot is going to be user defined and might be very messy. Example: (source: theuprightape.net ) On that structure, I want to perform the following operations: find all items (sinks) below a

I have a classic dependency solving problem. I thought I was headed in the right direction, but now I've hit a roadblock and I am not sure how to proceed. Background In the known universe (the cache of all artifacts and their dependencies), each there is a 1->n relationship between artifacts and versions, and each version may contain a different set of dependencies. For example: A 1.0.0 B (>= 0.0.0) 1.0.1 B (~> 0.1) B 0.1.0 1.0.0 Given a set of "demand constraints", I'd like to find the best possible solution (where "best" is the highest possible version that still satisfies all constraints).

I am working on an assignment where one of the problems asks to derive an algorithm to check if a directed graph G=(V,E) is singly connected (there is at most one simple path from u to v for all distinct vertices u,v of V. Of course you can brute force check it, which is what I'm doing right now, but I want to know if there's a more efficient way. Could anyone point me in the right direction? Have you tried DFS . Run DFS for every vertex in the graph as source If a visited vertex is encountered again, the graph is not singly connected repeat for every unvisited vertex. The graph is singly