
L. Allison, Monash University, 2008.
Encoding Rooted (strict) Binary Trees
 A ‘strict binary tree’, where every node either has
two subtrees (is a Fork), or has zero subtrees (is a Leaf),
can be encoded in 1bit per edge:
 Perform a prefix traversal of the tree,
 output 'F' for a Fork, 'L' for a Leaf,
 i.e.,
 data Tree = Leaf  Fork Tree Tree
 data type defn
 code Leaf = "L"
 code (Fork Left Right) = "F"
++ code Left
++ code Right
 e.g.,

 F L F F L L L

 Note that the method gives a prefixcode for trees;
the end of the string for a tree can be detected when #L=#F+1.
It is reasonable to give L and F 1bit codes because the
numbers of Ls and Fs are approximately equal.
 The tree can be reconstructed from the traversal.

 Note that encoding then decoding preserves the ordering of subtrees from
left to right; if this is unnecessary a shorter encoding must be possible
(it should be possible to save 2 bits for the example above).

 The method can be extended to nonbinary trees
provided the degree of each node is known, for example,
if this is a known constant, or if the degrees are also encoded.
For a tree of constant degree k, #L=(k1)×#F+1
(so the codeword lengths depend on k).

 This method of encoding trees has been used
in Minimum Message Length (MML) inference for
decision (classification) trees, and as
the basis of a universal code for
positive integers.
Encoding Rooted nary Trees.
 Arbitrary nary trees can be encoded in about 2bits per edge:
 Perform a prefix traversal of the tree,
 output 'd', for down, when descending an edge, and
 output 'u', for up, when returning up an edge.
 i.e.
 data NTree = Leaf  Fork [NTree]
 code Leaf = ""
 code (Fork []) = error "not allowed"
 code (Fork ts) = concat(map (\t > 'd' ++ code t ++ 'u') ts)
 e.g.,
 tree as above,
 d u d d d u d u u d u u

 Each edge corresponds to one 'd' and one 'u'.
 The encoding requires about 2bits per edge.
 The method applies to trees of any degree.

 The method gives a prefixcode for trees provided that the end of
the string can be detected; the end can be indicated by appending an
extra 'u' because the root has no ancestor,
 d u d d d u d u u d u u u
 this is unnecessary if the degree of the root is otherwise known.
 The tree can be reconstructed from the traversal.

 Note that encoding then decoding preserves the ordering of subtrees from
left to right; if this is unnecessary a shorter encoding must be possible.

 Also see
generating
(enumerating) sequences of n pairs of matched brackets (parentheses);
think 'd' = '(',
and 'u' = ')'.
Unrooted nary Trees
 An unrooted nary tree can be encoded by choosing an
arbitrary vertex (node) as the root and then encoding the tree
as a rooted tree.
This transmits log_{2}Vbits of extra information
which can be subtracted.
OuterPlanar Graphs
 An outerplanar graph can be drawn in the plane
so that its vertices lie on a circle;
some edges may run along the circle, and
other edges may run across the interior of the circle without intersecting.
 A tree is an outerplanar graph, and an outerplanar graph is a planar graph.
Encoding Planar Graphs (Plane Graphs)
 A plane graph (a planar graph embedded in the plane)
can be encoded in about 4bits per edge [Tur84],
in fact in log_{2}(14) bits per edge [Via08].

 graph (after [Tur84])


 with spanning tree & traversal (bold arrows),
 encoding of spanning tree: d d u d u u d d u d d u u u

 Sweeping around each vertex anticlockwise,
 encode each "crosstree" edge by ( . . . ), e.g.,

vertex 
1   2   3   2   4   2   1   5   6   5   7   8   7   5   1 
s.tree 
 d   d   u   d   u   u   d   d   u   d   d   u   u   u  
other 
    ((     )((   ((     )   ))(     )   )     )   
 all: d d ( ( u d ) ( ( u ( ( u d ) d ) ) ( u d ) d ) u u ) u
 (appending an extra 'u', say, would mark
the end of the encoding).

 The numbers of ‘d’s and ‘u’s are
approximately equal, the numbers of (s and )s are equal, and
the blandest assumption is that the numbers
of ‘u’s and (s will be about the same.
The alphabet {d, u, (, )} has
size 4 (2bit codes), and
each edge, of either kind, corresponds to two letters (4bits).
Any known skew in the alphabet could be used to give better compression.

 Loops and multiedges can be encoded.

 The graph is planar so edges do not cross.
The graph can be reconstructed by
reconstructing the spanning tree, as before, and
matching up corresponding (s and )s
in a lastin, firstout manner.

 [Via08] tightened up the
encoding (log_{2}14 < 4)
by recognising that certain combinations cannot be produced in the encoding.

 Note that the construction encodes more than the planar graph 
it encodes a particular embedding of the graph in the plane, a plane graph.
It also distinguishes the start ("root") vertex;
log_{2}Vbits can be saved if this is arbitrary.

 If it is possible that the graph is not connected,
the number of connected components must be encoded followed by
each connected component as above.
Chemical Compounds
Chemical compounds can be treated as graphs,
each vertex labelled with a chemical element, and
each edge labelled with a chemical bond type
(single  double  triple  aromatic  hydrogen).
These chemical compound graphs can be compressed and
the information content of one compound given another can be
calculated [All14]
to give a notion of their similarity.
General Graphs
 G = ⟨V, E⟩

 If selfloops (⟨v_{i},v_{i}⟩) and multiedges
are not allowed, an
 Undirected Graph has V×(V1)/2 possible edges,
 the adjacency matrix can be coded in that many bits if
pr(⟨v_{i},v_{j}⟩ exists)=0.5
∀i,j>i,
 and a
 Directed Graph has V×(V1) possible edges,
 the adjacency matrix can be coded in that many bits if
pr(⟨v_{i},v_{j}⟩ exists)=0.5
∀i,j≠i.

 The
binomial distribution
can be used to encode (rows of) an adjacency matrix
if the probability of edge existence varies from graph to graph.
There could be one density parameter per graph, or
one parameter per row (or column) of the adjacency matrix.

 If G is sparse,
one may be tempted to encode the adjacencylists representation.
In fact the adjacency list of a vertex contains exactly
the same information, and has the same message length [All18],
as the corresponding row of the adjacency matrix  see
method 4 (sparse list) under the
binomial distribution.
 (Similar considerations apply to a dense graph.)
 Regularenough subgraphs, once identified, imply certain edges
do or do not exist, e.g.,
 K_{m} 
K_{m',m''} 
. . . 
K_{m}  1s 
?  ? 
K_{m',m''} 
? 
0s  1s 
? 
1s  0s 
. . .  ? 
?  ? 
 (if K_{m',m''} disallows withinpart edges)
 To be worthwhile, the saving on edges must outweigh the cost of
encoding the membership of a subgraph.
 Finding such subgraphs is hard.
 If the vertex ordering is arbitrary, any special subgraphs
might as well come first and it is only necessary
to encode their kinds and sizes.
If the vertex ordering is not arbitrary,
it takes about log ^{V}C_{m} bits
to specify the members of K_{m}, for example.
Motifs
If a graph is "sufficiently regular" [Tur84], or
if it contains repeated motifs, it must be possible
to obtain a more efficient encoding of the graph [All18] using this fact.
E.g., [FM95] suggest that if a graph contains
a bipartite clique, K_{m,n}, the m×n edges of the b.clique
can be replaced by a new "special vertex" and m+n edges involving it.
This transformation, G↔G', results in a graph, G', that has
more vertices and fewer edges (is sparser) but it is not necessarily
compressible unless K_{m,n} subgraphs are frequent in G
compared to "random" graphs, and any compression might be had more directly.
Finding maximal bipartite cliques is hard, but
finding large ones quickly may suffice.
Other "regular" subgraphs, if frequent in G,
could be used for the same purpose.
Discovering general motifs that occur "frequently" in a given graph
is challenging and there are many issues to consider such as
whether or not instances of motifs may overlap or not.
These can be resolved and used to to compress a graph [All18].
Notes
 [All14] L. Allison,
On the complexity of graphs (networks) by information content, and
conditional (mutual) information given other graphs,
TR #2014/277, FIT, Monash University,
May 2014
[www (click)].

 [All18] L. Allison, 'Graphs', Ch.11 in
Coding Ockham's Razor, Springer, 2018
[doi:10.1007/9783319764337_11]].

 [FM95] T. Feder & R. Motwani,
Clique partitions, graph compression and speeding up algorithms,
Jrnl. Comp. and Sys. Sci., 51(2), pp.261272, 1995.

 [Tur84] G. Turan,
On the succinct representation of graphs,
Discr. Applied Maths., 8, pp.289294, 1984.

 [Via08] R. Viana,
Quick encoding of plane graphs in log_{2}(14) bits per edge,
Inf. Proc. Lett., 108, pp.150154, 2008.
(L.A., 2008, updated 2018, 2020.)

