I've been playing with this the last few days.
I first tried making each node hold a set of refs to edges, and each edge hold a set of refs to the nodes. I set them equal to each other in a (dosync... (ref-set...))
type of operation. I didn't like this because changing one node requires a large amount of updates, and printing out the graph was a bit tricky. I had to override the print-method
multimethod so the repl wouldn't stack overflow. Also any time I wanted to add an edge to an existing node, I had to extract the actual node from the graph first, then do all sorts of edge updates and that sort of thing to make sure everyone was holding on to the most recent version of the other thing. Also, because things were in a ref, determining whether something was connected to something else was a linear-time operation, which seemed inelegant. I didn't get very far before determining that actually performing any useful algorithms with this method would be difficult.
Then I tried another approach which is a variation of the matrix referred to elsewhere. The graph is a clojure map, where the keys are the nodes (not refs to nodes), and the values are another map in which the keys are the neighboring nodes and single value of each key is the edge to that node, represented either as a numerical value indicating the strength of the edge, or an edge structure which I defined elsewhere.
It looks like this, sort of, for 1->2, 1->3, 2->5, 5->2
(def graph {node-1 {node-2 edge12, node-3 edge13},
node-2 {node-5 edge25},
node-3 nil ;;no edge leaves from node 3
node-5 {node-2 edge52}) ;; nodes 2 and 5 have an undirected edge
To access the neighbors of node-1 you go (keys (graph node-1))
or call the function defined elsewhere (neighbors graph node-1)
, or you can say ((graph node-1) node-2)
to get the edge from 1->2
.
Several advantages:
- Constant time lookup of a node in the graph and of a neighboring node, or return nil if it doesn't exist.
- Simple and flexible edge definition. A directed edge exists implicitly when you add a neighbor to a node entry in the map, and its value (or a structure for more information) is provided explicitly, or nil.
- You don't have to look up the existing node to do anything to it. It's immutable, so you can define it once before adding it to the graph and then you don't have to chase it around getting the latest version when things change. If a connection in the graph changes, you change the graph structure, not the nodes/edges themselves.
- This combines the best features of a matrix representation (the graph topology is in the graph map itself not encoded in the nodes and edges, constant time lookup, and non-mutating nodes and edges), and the adjacency-list (each node "has" a list of its neighboring nodes, space efficient since you don't have any "blanks" like a canonical sparse matrix).
- You can have multiples edges between nodes, and if you accidentally define an edge which already exists exactly, the map structure takes care of making sure you are not duplicating it.
- Node and edge identity is kept by clojure. I don't have to come up with any sort of indexing scheme or common reference point. The keys and values of the maps are the things they represent, not a lookup elsewhere or ref. Your node structure can be all nils, and as long as it's unique, it can be represented in the graph.
The only big-ish disadvantage I see is that for any given operation (add, remove, any algorithm), you can't just pass it a starting node. You have to pass the whole graph map and a starting node, which is probably a fair price to pay for the simplicity of the whole thing. Another minor disadvantage (or maybe not) is that for an undirected edge you have to define the edge in each direction. This is actually okay because sometimes an edge has a different value for each direction and this scheme allows you to do that.
The only other thing I see here is that because an edge is implicit in the existence of a key-value pair in the map, you cannot define a hyperedge (ie one which connects more than 2 nodes). I don't think this is a big deal necessarily since most graph algorithms I've come across (all?) only deal with an edge that connects 2 nodes.
transient
, however. Do you have any example code that illustrates this idea, even for something as simple as a vector with itself as an element? – Junie