Notions and Notation Flashcards

1
Q

graph. :

A

a finite, nonempty set V (vertex set) and a set E (edge set) whose elements e in E are pairs (a,b) with a,b in V

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2
Q

digraph:

A

a directed graph, where edges go only 1 way - undirected graphs are well. undirected, they go both

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3
Q

multigraph:

A

a graph with parallel edges

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4
Q

2 vertices connected by an edge are:

A

adjacent, neighbours, the edge is incident on the 2 vertices

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5
Q

start and end vertex of a directed edge:

A

start = tail (vertex), predecessor
end = tip (vertex), successor

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6
Q

complete graphs:

A

Kn, all vertices are connected to each other
|E|=(n(n-1))/2

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7
Q

path graphs:

A

Pn, a line, string of vertices connected by 1 or 2 edges depending on if its an end one or not
|E|=|V|-1

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8
Q

cycle graphs:

A

Cn, aka circuit graphs, 1 cycle, a ring of however many vertices, what if you connected the end vertices of a path graph
|E|=|V|

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9
Q

complete bipartite graphs:

A

Km,n - vertex set is the union of 2 sets, and the edge set is each vertex of one set connecting to every vertex of the other
|E|=|V1|*|V2|

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10
Q

bipartite graph:

A

each vertex from the first set can be connected to only some of the vertices of the second, but the 2 vertex sets are still disjoint and each edge connects one vertex from one set to one in the other

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11
Q

cube graphs:

A

Id - the d-dimensional cube graph, each vertex is a string of d 0s and 1s, and all possible vertex labels occur. edges connect the vertices that differ by exactly one digit
|V|=2^d, |E|=d2^(d-1)

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12
Q

degree:

A

the degree of a vertex (deg(v)) is the number of edges connected to that vertex, or the number of times it appears in the edge set - digraphs have deg(in)(v) and deg(out)(v)

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13
Q

handshaking lemma:

A

if G(V,E) is an undirected graph, Σdeg(v)=2|E|

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14
Q

corollary of the handshaking lemma:

A

in an undirected graph, there must be an even number of vertices with odd degree

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15
Q

edge list:

A

take a vertex set {1,2,3}. the edge set is smth like E={(1,2),(2,3),(1,3)} - if every vertex is contained within the edge list, we can just use the edge list

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16
Q

adjacency matrix:

A

the adjacancy matrix A has elements Aij=1 if an edge between the vertices i and j exist, and 0 otherwise - row is the first/tail vertex, column is the second/tip in a digraph - if it’s a multigraph, take the number of edges

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17
Q

degree and adjecancy matrix:

A

you can calculate the degree of a vertex by summing the row/column, and for deg(out) you take the row and deg(in) take the column

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18
Q

adjacency list:

A

a list of all the vertices a given vertex v is connected to, Av, in a digraph you have the predecessor and successor lists for the vertices that precede or succeed v

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19
Q

isomorphism:

A

2 graphs are isomorphic if there exists a bijection f:V1->V2 such that the edge (F(a),F(b)) in E2 iff (a,b) in E1
|V1|=|V2| and |E1|=|E2|
deg(v)=deg(F(v))

20
Q

degree sequence:

A

a list of degrees of vertices of an undirected graph, arranged in ascending order
isomorphic graphs have the same degree sequence, but 2 graphs with the same degree sequence aren’t necessarily isomorphic

21
Q

subgraph:

A

a subgraph G’(V’,E’) is a part of a graph G(V,E) such that V’⊆V and E’⊆E
a subgraph induced by V’ is a subgraph that only contains vertices in V’ and edges that only involve vertices in V’

22
Q

k-colouring:

A

a function f:V->{1,…,k} that assigns distinct values to adjacent vertices - adjacent vertices must have different colours - if a graph has a k colouring we say it’s k-colourable

23
Q

examples of graphs and colourings:

A

the complete graphs Kn are n-colourable
Km,n (the complete bipartite graph) is 2-colourable (1 for each set)

24
Q

chromatic number:

A

the chromatic number χ(G) is the smallest number k such that G is k-colourable

25
lemmas from the chromatic number:
any graph has χ(G)=2 iff it's bipartite if H is a subgraph of G and G is k-colourable, so is H if H is a subgraph of G, χ(H)<=χ(G)
26
the greedy colouring algorithm:
order the vertices, order some colours, colour the first one the first colour, go through each vertex choosing the first possible colour that isn't the same as that of an adjacent vertex, stop when they're all coloured may not give χ(G), but will give an upper bound basic step is lookin at neighbour's colours, with 2|E| basic steps
27
floor and ceiling:
x is a real number the floor ⌊x⌋ is the greatest integer <=x the ceiling ⌈x⌉ is the least integer >=x for all real x, x<⌊x⌋+1
28
logs and lengths in decimal digits:
the decimal representation of a natural number n has exactly d=1+log10(n) digits NOTE!! this is the number of digits and Not a decimal number it's just named confusingly :(
29
square matrix multiplication:
basic step is arithmetic operations of 2 real numbers (addition and multiplication) ABij=(n)Σ(k=1)AikBkj that's n^2(n+(n-1))=2n^3+n^2 total operations cause n multiplications and n-1 additions for 1 entry, and n^2 entries
30
primality testing:
in these cases we use the worst case efficiency, cause you test for primes by testing all divisors from 2 to root(n) and if there's no divisors it's prime, so it could be 1 step or it could be root(n)-1 or inbetween, take the worst case root(n)-1 measure size of input with d=log10(n) (no. of digits) basic step is n mod b (does b divide n, if yes, n mod b = 0)
31
upper bound of asymptotic growth:
f(n)=O(g(n)) if ∃ c1>0 such that for all sufficiently large n, f(n)<=c1(g(n)) primality testing takes O(10^(d/2)) steps
32
lower bound of asymptotic growth:
f(n)=Ω(g(n)) if ∃ c2>0 such that for all sufficiently large n, f(n)>=c2(g(n))
33
both bounds of asymptotic growth:
f(n)=Θ(g(n)) if f(n)=O(g(n)) and f(n)=Ω(g(n)) greedy colouring takes Θ(|E|) basic steps, taking c1 as 1 and c2 as 3 matrix multiplication takes Θ(n^3) steps, by putting n^3 or 3n^3 on either side of 2n^3-n^2 and combining
34
walk:
a series of edges, if v0=vn, it's a closed walk
35
length of a walk:
no. of edges in the sequence
36
trail:
a walk where all the edges are distinct, closed trail has v0=vn
37
path:
a trail where all the vertices are distinct
38
cycle:
a closed trail with all distinct vertices except v0 and vn, which are the same
39
trails paths and walks connection:
all trails are walks and all paths are trails, venn diagram is 3 concentric circles with walks on the outside and paths in the centre
40
connected:
2 vertices are connected if there's a walk between them, a graph is connected if all vertices are connected
41
equivalence relation:
a relation ~ is equivalent if it's: reflexive - a~a symmetric - a~b -> b~a transitive - a~b and b~c -> a~c equivalance class is the disjoint subset of all the equivalent things, such as areas of rectangles on a 2d plane - each area has its own equivalence class of rectangles that don't overlap
42
equivalence relation in undirected graphs:
they are reflexive by definition they are symmetric as a walk can be reversed they are transitive cause you can attach 2 walks together to create a walk between the first vertex of the first walk and the last of the second
43
connected component:
of an undirected graph a subgraph induced by an equivalence class under the relation 'is-connected-to' on V p sure on a connected graph, there is only one connected component, just showing how many bits there are the equivalence class then contains the vertices that are part of that connected subgraph
44
accessibility:
a vertex b is accessible (or reachable) from another vertex a if a digraph G contains a walk from a to b - also all vertices are accessible from themselves
45
strongly/weakly connected:
in a digraph, 2 vertices a and b are strongly connected if there exists a walk from a to b and from b to a they're weakly connected if only one of those walks exists a digraph is strongly connected if all vertices are strongly connected it's weakly connected if all vertices are weakly connected, same as saying if all edges were converted to undirected ones, it would be connected
46
|G|:
the graph you get when you convert all directed edges of a directed multigraph to undirected ones - if you have a->b and b<-a, it becomes 2 parallel edges
47
connected vertices are always joined by a path proof:
going a to b if all vertices in the sequence of the walk are distinct, it's a path, you're done if not, remove everything before the last appearance of a and after the first appearance of b then, for each other repeated vertex, remove everything between their first appearance and the first vertex after the last appearance (so take out the last one) do that for all of them and voila a path, as there can only be so many repeated vertices