#### 16.1` `Introducing Graphs

In From Acyclicity to Cycles we introduced a special kind of sharing: when the data become cyclic, i.e., there exist values such that traversing other reachable values from them eventually gets you back to the value at which you began. Data that have this characteristic are called graphs.Technically, a cycle is not necessary to be a graph; a tree or a DAG is also regarded as a (degenerate) graph. In this section, however, we are interested in graphs that have the potential for cycles.

Lots of very important data are graphs. For instance, the people and connections in social media form a graph: the people are nodes or vertices and the connections (such as friendships) are links or edges. They form a graph because for many people, if you follow their friends and then the friends of their friends, you will eventually get back to the person you started with. (Most simply, this happens when two people are each others’ friends.) The Web, similarly is a graph: the nodes are pages and the edges are links between pages. The Internet is a graph: the nodes are machines and the edges are links between machines. A transportation network is a graph: e.g., cities are nodes and the edges are transportation links between them. And so on. Therefore, it is essential to understand graphs to represent and process a great deal of interesting real-world data.

Graphs are important and interesting for not only practical but also principled reasons. The property that a traversal can end up where it began means that traditional methods of processing will no longer work: if it blindly processes every node it visits, it could end up in an infinite loop. Therefore, we need better structural recipes for our programs. In addition, graphs have a very rich structure, which lends itself to several interesting computations over them. We will study both these aspects of graphs below.

##### 16.1.1` `Understanding Graphs

`identical`

). As
we saw earlier [From Acyclicity to Cycles], it is not completely
straightforward to create such a structure, but what we saw earlier
[Streams From Functions] can help us here, by letting us
suspend the evaluation of the cyclic link. That is, we have to
not only use `rec`

, we must also use a function to delay
evaluation. In turn, we have to update the annotations on the
fields. Since these are not going to be “trees” any more, we’ll use
a name that is suggestive but not outright incorrect:
```
data BinT:
| leaf
| node(v, l :: ( -> BinT), r :: ( -> BinT))
end
```

```
rec tr = node("rec", lam(): tr end, lam(): tr end)
t0 = node(0, lam(): leaf end, lam(): leaf end)
t1 = node(1, lam(): t0 end, lam(): t0 end)
t2 = node(2, lam(): t1 end, lam(): t1 end)
```

`BinT`

. Here’s the obvious
program:
```
fun sizeinf(t :: BinT) -> Number:
cases (BinT) t:
| leaf => 0
| node(v, l, r) =>
ls = sizeinf(l())
rs = sizeinf(r())
1 + ls + rs
end
end
```

`sizeinf`

in a moment.)Do Now!

What happens when we call

`sizeinf(tr)`

?

It goes into an infinite loop: hence the `inf`

in its name.

`tr`

, we can in fact traverse edges an infinite
number of times. But the total number of constructed nodes is only
one! Let’s write this as test cases in terms of a `size`

function, to be defined:
```
check:
size(tr) is 1
size(t0) is 1
size(t1) is 2
size(t2) is 3
end
```

It’s clear that we need to somehow remember what nodes we have visited previously: that is, we need a computation with “memory”. In principle this is easy: we just create an extra data structure that checks whether a node has already been counted. As long as we update this data structure correctly, we should be all set. Here’s an implementation.

```
fun sizect(t :: BinT) -> Number:
fun szacc(shadow t :: BinT, seen :: List<BinT>) -> Number:
if has-id(seen, t):
0
else:
cases (BinT) t:
| leaf => 0
| node(v, l, r) =>
ns = link(t, seen)
ls = szacc(l(), ns)
rs = szacc(r(), ns)
1 + ls + rs
end
end
end
szacc(t, empty)
end
```

`seen`

, is called an accumulator,
because it “accumulates” the list of seen nodes.Note
that this could just as well be a set; it doesn’t have to be a list.
The support function it needs checks whether a given node has already
been seen:
```
fun has-id<A>(seen :: List<A>, t :: A):
cases (List) seen:
| empty => false
| link(f, r) =>
if f <=> t: true
else: has-id(r, t)
end
end
end
```

How does this do? Well, `sizect(tr)`

is indeed `1`

, but
`sizect(t1)`

is `3`

and `sizect(t2)`

is `7`

!

Do Now!

Explain why these answers came out as they did.

```
ls = szacc(l(), ns)
rs = szacc(r(), ns)
```

`ns`

: namely, the current node and
those visited “higher up”. As a result, any nodes that “cross
sides” are counted twice.The remedy for this, therefore, is to remember every node we
visit. Then, when we have no more nodes to process, instead of
returning only the size, we should return all the nodes visited
until now. This ensures that nodes that have multiple paths to them
are visited on only one path, not more than once. The logic for this
is to return two values from each traversal—

```
fun size(t :: BinT) -> Number:
fun szacc(shadow t :: BinT, seen :: List<BinT>)
-> {n :: Number, s :: List<BinT>}:
if has-id(seen, t):
{n: 0, s: seen}
else:
cases (BinT) t:
| leaf => {n: 0, s: seen}
| node(v, l, r) =>
ns = link(t, seen)
ls = szacc(l(), ns)
rs = szacc(r(), ls.s)
{n: 1 + ls.n + rs.n, s: rs.s}
end
end
end
szacc(t, empty).n
end
```

Sure enough, this function satisfies the above tests.

##### 16.1.2` `Representations

The representation we’ve seen above for graphs is certainly a start
towards creating cyclic data, but it’s not very elegant. It’s both
error-prone and inelegant to have to write `lam`

everywhere, and
remember to apply functions to `()`

to obtain the actual
values. Therefore, here we explore other representations of graphs
that are more conventional and also much simpler to manipulate.

The structure of the graph, and in particular, its density. We will discuss this further later [Measuring Complexity for Graphs].

The representation in which the data are provided by external sources. Sometimes it may be easier to simply adapt to their representation; in particular, in some cases there may not even be a choice.

The features provided by the programming language, which make some representations much harder to use than others.

A way to construct graphs.

A way to identify (i.e., tell apart) nodes or vertices in a graph.

Given a way to identify nodes, a way to get that node’s neighbors in the graph.

Our running example will be a graph whose nodes are cities in the United States and edges are direct flight connections between them:

##### 16.1.2.1` `Links by Name

```
type Key = String
data KeyedNode:
| keyed-node(key :: Key, content, adj :: List<String>)
end
type KNGraph = List<KeyedNode>
type Node = KeyedNode
type Graph = KNGraph
```

`kn-`

stands for “keyed node”.
```
kn-cities :: Graph = block:
knWAS = keyed-node("was", "Washington", [list: "chi", "den", "saf", "hou", "pvd"])
knORD = keyed-node("chi", "Chicago", [list: "was", "saf", "pvd"])
knBLM = keyed-node("bmg", "Bloomington", [list: ])
knHOU = keyed-node("hou", "Houston", [list: "was", "saf"])
knDEN = keyed-node("den", "Denver", [list: "was", "saf"])
knSFO = keyed-node("saf", "San Francisco", [list: "was", "den", "chi", "hou"])
knPVD = keyed-node("pvd", "Providence", [list: "was", "chi"])
[list: knWAS, knORD, knBLM, knHOU, knDEN, knSFO, knPVD]
end
```

```
fun find-kn(key :: Key, graph :: Graph) -> Node:
matches = for filter(n from graph):
n.key == key
end
matches.first # there had better be exactly one!
end
```

Exercise

Convert the comment in the function into an invariant about the datum. Express this invariant as a refinement and add it to the declaration of graphs.

```
fun kn-neighbors(city :: Key, graph :: Graph) -> List<Key>:
city-node = find-kn(city, graph)
city-node.adj
end
```

```
check:
ns = kn-neighbors("hou", kn-cities)
ns is [list: "was", "saf"]
map(_.content, map(find-kn(_, kn-cities), ns)) is
[list: "Washington", "San Francisco"]
end
```

##### 16.1.2.2` `Links by Indices

In some languages, it is common to use numbers as names. This is
especially useful when numbers can be used to get access to an element
in a constant amount of time (in return for having a bound on the
number of elements that can be accessed). Here, we use a list—

`ix-`

stands for
“indexed”.
```
data IndexedNode:
| idxed-node(content, adj :: List<Number>)
end
type IXGraph = List<IndexedNode>
type Node = IndexedNode
type Graph = IXGraph
```

```
ix-cities :: Graph = block:
inWAS = idxed-node("Washington", [list: 1, 4, 5, 3, 6])
inORD = idxed-node("Chicago", [list: 0, 5, 6])
inBLM = idxed-node("Bloomington", [list: ])
inHOU = idxed-node("Houston", [list: 0, 5])
inDEN = idxed-node("Denver", [list: 0, 5])
inSFO = idxed-node("San Francisco", [list: 0, 4, 3])
inPVD = idxed-node("Providence", [list: 0, 1])
[list: inWAS, inORD, inBLM, inHOU, inDEN, inSFO, inPVD]
end
```

`0`

. To find a node:
```
fun find-ix(idx :: Key, graph :: Graph) -> Node:
lists.get(graph, idx)
end
```

```
fun ix-neighbors(city :: Key, graph :: Graph) -> List<Key>:
city-node = find-ix(city, graph)
city-node.adj
end
```

```
check:
ns = ix-neighbors(3, ix-cities)
ns is [list: 0, 5]
map(_.content, map(find-ix(_, ix-cities), ns)) is
[list: "Washington", "San Francisco"]
end
```

Something deeper is going on here. The keyed nodes have
intrinsic keys: the key is part of the datum itself. Thus, given
just a node, we can determine its key. In contrast, the indexed nodes
represent extrinsic keys: the keys are determined outside the
datum, and in particular by the position in some other data
structure. Given a node and not the entire graph, we cannot know for
what its key is. Even given the entire graph, we can only determine
its key by using `identical`

, which is a rather unsatisfactory
approach to recovering fundamental information. This highlights a
weakness of using extrinsically keyed representations of
information. (In return, extrinsically keyed representations are
easier to reassemble into new collections of data, because there is no
danger of keys clashing: there are no intrinsic keys to clash.)

##### 16.1.2.3` `A List of Edges

`le-`

stands for “list of edges”.
```
data Edge:
| edge(src :: String, dst :: String)
end
type LEGraph = List<Edge>
type Graph = LEGraph
```

```
le-cities :: Graph =
[list:
edge("Washington", "Chicago"),
edge("Washington", "Denver"),
edge("Washington", "San Francisco"),
edge("Washington", "Houston"),
edge("Washington", "Providence"),
edge("Chicago", "Washington"),
edge("Chicago", "San Francisco"),
edge("Chicago", "Providence"),
edge("Houston", "Washington"),
edge("Houston", "San Francisco"),
edge("Denver", "Washington"),
edge("Denver", "San Francisco"),
edge("San Francisco", "Washington"),
edge("San Francisco", "Denver"),
edge("San Francisco", "Houston"),
edge("Providence", "Washington"),
edge("Providence", "Chicago") ]
```

```
fun le-neighbors(city :: Key, graph :: Graph) -> List<Key>:
neighboring-edges = for filter(e from graph):
city == e.src
end
names = for map(e from neighboring-edges): e.dst end
names
end
```

```
check:
le-neighbors("Houston", le-cities) is
[list: "Washington", "San Francisco"]
end
```

##### 16.1.2.4` `Abstracting Representations

We would like a general representation that lets us abstract over the
specific implementations. We will assume that broadly we have
available a notion of `Node`

that has `content`

, a notion of
`Key`

s (whether or not intrinsic), and a way to obtain the
neighbors—

##### 16.1.3` `Measuring Complexity for Graphs

Before we begin to define algorithms over graphs, we should consider how to measure the size of a graph. A graph has two components: its nodes and its edges. Some algorithms are going to focus on nodes (e.g., visiting each of them), while others will focus on edges, and some will care about both. So which do we use as the basis for counting operations: nodes or edges?

No two nodes are connected. Then there are no edges at all.

Every two nodes is connected. Then there are essentially as many edges as the number of pairs of nodes.

Therefore, when we want to speak of the complexity of algorithms over graphs, we have to consider the sizes of both the number of nodes and edges. In a connected graphA graph is connected if, from every node, we can traverse edges to get to every other node., however, there must be at least as many edges as nodes, which means the number of edges dominates the number of nodes. Since we are usually processing connected graphs, or connected parts of graphs one at a time, we can bound the number of nodes by the number of edges.