Graph Theory
 Graph Families / Classes
 Software
 Topological Graph Theory / Planar
 Minors
 Extremal Graph Theory
 Probablistic Graph Theory
 Algebraic Graph theory
 Decomposition
 Logic
 Problems
 Graph Neural Network
 Graph Rewriting / Graph Transformation
 Infinite Graphs
 Misc
https://en.wikipedia.org/wiki/Graph_theory
introduction to graph theory  grinberg Infinite graphs? How does this even make sense.
https://en.wikipedia.org/wiki/Line_graph
https://en.wikipedia.org/wiki/Strongly_connected_component#Definitions Condensation graph  strongly connected component collapsed to vertex
https://en.wikipedia.org/wiki/List_of_graphs particular graphs
https://en.wikipedia.org/wiki/Multigraph multiple edges between vertices https://en.wikipedia.org/wiki/Directed_graph directed edges https://en.wikipedia.org/wiki/Rooted_graph graph with distinguished root. “flow graphs”. Aczel’s anti foundation axiom https://en.wikipedia.org/wiki/Directed_acyclic_graph https://en.wikipedia.org/wiki/Graph_labeling
https://en.wikipedia.org/wiki/Hypergraph edges connect more than one vertex https://github.com/pnnl/HyperNetX https://github.com/yamafaktory/hypergraph
Drags: A Simple Algebraic Framework For Graph Rewriting
Egraphs :)
Reinhard Diestel  Graph Theory
Graph Families / Classes
https://en.wikipedia.org/wiki/Category:Graph_families
https://en.wikipedia.org/wiki/Complete_graph totally connected
https://en.wikipedia.org/wiki/Regular_graph Every vertex has same number of neighbors
https://en.wikipedia.org/wiki/Perfect_graph
https://en.wikipedia.org/wiki/Permutation_graph
https://en.wikipedia.org/wiki/Cograph indcutively generated by disojint union and complement
https://en.wikipedia.org/wiki/Distancehereditary_graph
https://en.wikipedia.org/wiki/Chordal_graph all cycles of 4 or more vertices have a chord
series parallel graph
https://en.wikipedia.org/wiki/Bipartite_graph separated into two sets of vertices that are not connected. Two colorable. Tree’s are bartite. Even cycle graphs Konig’s theorem  minimum vertex cover = maximum edge cover
https://en.wikipedia.org/wiki/Planar_graph https://en.wikipedia.org/wiki/Circle_packing_theorem
https://en.wikipedia.org/wiki/Intersection_graph all graphs are intersection graphs. Intersections of sets
https://en.wikipedia.org/wiki/Interval_graph intersection graph of a set of intervals
https://en.wikipedia.org/wiki/Comparability_graph things that are comparable in a partial order have an edge. Total order has complete comparaibility graph. Comparability graphs are perfect
cayley graph https://en.wikipedia.org/wiki/Cayley_graph turn group into graph. edges are generators. Can easily get infinite graph in this way
Software
networkx
graphtools igraph
hypernetx
boost graph Lemon
https://docs.rs/petgraph/latest/petgraph/ petgraph rust https://depthfirst.com/articles/2020/02/03/graphsinrustanintroductiontopetgraph/
// cargodeps: petgraph
use petgraph::prelude::Graph;
use petgraph::dot::Dot;
fn main() {
let mut graph = Graph::<&str, u32>::new();
let origin = graph.add_node("Denver");
let destination_1 = graph.add_node("San Diego");
let destination_2 = graph.add_node("New York");
graph.extend_with_edges(&[
(origin, destination_1, 250),
(origin, destination_2, 1099)
]);
println!("{}", Dot::new(&graph));
}
Representation
Set of pairs Adjacency list. multigraph
raw pointery. Ironically, graphs are easy to define in C. Raw access to pointers makes traversing around in the grap possible. Global questions/search about the graph become annying.
E,V,s,t. Functional rep. You see this in categorical presentations. This is a finite category and then a functor takes it to data. Multigraph really.
 https://proofassistants.stackexchange.com/questions/1698/makingafinitegraphtypeinleanintroductionrule
structure Graph where  a choice. Make E V parameters or not.
E : Type
V : Type
s : E > V
t : E > V
 It's not good data though. We're not going to be able to compute
def ex := Graph.mk Unit Unit (fun _ => ()) (fun _ => ())
def ex2 := Graph.mk Bool Unit (fun _ => ()) (fun _ => ())  two edges to one vertex
def Graph2 (V : Type) : Type := V > V > Prop
inductive ex1 : Bool > Bool > Prop where
true_edge : ex1 true true
def ex2 x y := match x, y with
 true, true => True
 _,_ => False
def Set (X : Type) : Type := X > Prop
 a set of edges
def Graph3 (V : Type) : Type := Set (Prod V V)
#print (<>)
 #check Iso
 theorem iso (V : Type) : Iso (Graph2 V) (Graph3 V) := (fun p => fun (x,y) => p x y, fun p => fun x y => p (x, y))
structure SymGraph (V) where
g : Graph2 V
sym : forall x y, g x y > g y x
def main : IO Unit := pure ()
half edge
Topological Graph Theory / Planar
https://en.wikipedia.org/wiki/Planar_graph https://en.wikipedia.org/wiki/Book_embedding https://en.wikipedia.org/wiki/Topological_graph_theory
https://en.wikipedia.org/wiki/User:David_Eppstein/Graph_Drawing graph drawing.
Kuratowski’s theorem
Minors
https://en.wikipedia.org/wiki/Graph_minor  minors are formed from graphs via edge contraction, vertex and edge deletion
https://en.wikipedia.org/wiki/Forbidden_graph_characterization classes of graphs are often characterized by forbidden minors
https://en.wikipedia.org/wiki/Robertson%E2%80%93Seymour_theorem  Robertson Seymour. Graphs, ordered by the minor relationship, form a well quasiorder
Extremal Graph Theory
https://en.wikipedia.org/wiki/Extremal_graph_theory graphons Szemerédi regularity lemma
Probablistic Graph Theory
https://en.wikipedia.org/wiki/Random_graph
https://en.wikipedia.org/wiki/Percolation_theory
Algebraic Graph theory
Graph expanders
https://en.wikipedia.org/wiki/Adjacency_matrix https://en.wikipedia.org/wiki/Spectral_graph_theory cheeger constant. cheeger inequality
Positive and negative value on eigenvectors are a way of labelling or cutting graph.
https://en.wikipedia.org/wiki/Expander_graph sparse graph with strong connectivity properties. hard to cut apart.
Miracles of algerbaic graph theory
kepner and gilbert  graphs in language of linear algebra
Cut
https://en.wikipedia.org/wiki/Cut_(graph_theory) a
https://en.wikipedia.org/wiki/Bridge_(graph_theory) an edge who’s removal increases number of connected components Tarjan bridge finding algo Chain decomposition
https://en.wikipedia.org/wiki/Vertex_separator#Minimal_separators A set of veriies to separate graph in two
SDP Max cut approximation
Sherali Adams
Flow
https://en.wikipedia.org/wiki/Flow_network
https://en.wikipedia.org/wiki/Maximum_flow_problem
Max cut
Decomposition
https://en.wikipedia.org/wiki/Modular_decomposition https://en.wikipedia.org/wiki/Split_(graph_theory) split decomposition https://en.wikipedia.org/wiki/Branchdecomposition
Tree Decompositions
https://en.wikipedia.org/wiki/Tree_decomposition There was also the kleene algebra variation of Treewidth is the width of a minimal tree decomposition Cops and Robbers pursuit evasion Elimination ordering https://en.wikipedia.org/wiki/Junction_tree_algorithm graphical models have their own terminology for this https://pacechallenge.org/past/ PACE challenge. Herusitics and exact Correspondence between Multilevel Graph Partitions and Tree Decompositions Parametrized algorithms Treewidth, pathwidth and cospan decompositions with applications to graphaccepting tree automata
Use the sql plan?
 https://en.wikipedia.org/wiki/Pathwidth Insisting the tree decomposition is a path graph
 https://en.wikipedia.org/wiki/Cutwidth Order the vertices, number of edges cut to take apart the grpah
 https://en.wikipedia.org/wiki/Cliquewidth build graph via union, label rename, label join. Width is minimum number of labels
 https://en.wikipedia.org/wiki/Twinwidth
Encoding Treewidth into SAT SAT approach to cique width
Graph Partition
https://en.wikipedia.org/wiki/Graph_partition
Kernighan–Lin algorithm Huh. The opposite ordering of names to the TSP heurisitc. Take vertices with maximum improvement, swap them. Lock them?
https://en.wikipedia.org/wiki/Fiduccia%E2%80%93Mattheyses_algorithm Fiduccia–Mattheyses algorithm FM algorithm. terative partition
METIS hMetis for hypergraphs
Fiedler. split nodes into positive and negative of first eigenvector. +1 Indicator vector. The cost is Sum (v_i  v_j)^2. Relax from boolean indicator. Graph Laplacian sLs. Balanced cut constraints sum s = 0
.
https://web.eecs.umich.edu/~imarkov/pubs/book/part_survey.pdf huh using to reorder bdd hypergraph partitioning HighQuality Multilevel Hypergraph Partitioning KaHyPar https://github.com/lnisuofu/LSOracle lsoracle. uses kayhyper Graph partitioning is useful in logic synthesis?
parttion of sparse matrices search in a graph can be represented using multiplying by adjacency matrix
It’s almost “dual” to graph coloring, negating the sign of color color surafce. We want to clump colors as much as possible rather than
% functional
{color(N, C) : colors(C)} = 1 : nodes(N).
#minimize { edge(X,Y), color(X,C1), color(Y,C2), C1 != C2 } +
% maximize the colors that are touching
#maximize { edge(X,Y), color(X,C), color(Y,C)}
Optmization metrics: number of cut edges normalized in some way. “Volume” is sum of edges. ormalize by volume? Qutient, normalized cut
Karger’s algorithm  random for min cut. Randomly contract edges until you have only two vertices left. The edges between those are your guess for a cut and the set of contracted together vertcies are the two sides of the cut
Logic
https://en.wikipedia.org/wiki/Logic_of_graphs
model checking First order logic can ask about subgraph isomorphism. This is
Monadic second order logic. Existential second of logic let’s you talk about selection sets of certaain kinds. Model checking these formulas requires solving dificult NP problems. Many of the problems below fall into this class.
Courcelle’s theorem
MSO1 sets of vertices. cliquewidth graph
MSO2 sets of edges and vertices. treewidth
Problems
PACE https://pacechallenge.org/past/ https://www.optil.io/optilion/problems some kind of kaggle for optimuizatin problems https://sullivan.cs.utah.edu/#!/software
 Twinwidth  minimum contraction sequence to single vertex SAT encoding
 directed feedback vertex set  remove vertices to make graph acyclic
 cluster editting  set of edge modifications (addition or deletion) of minimum size that transformsG into a cluster graph.
 treedepth
 treewidth
 hypertree width
 steiner tree
https://en.wikipedia.org/wiki/User:David_Eppstein/Graph_Algorithms
Easy
Topological sort reachability shortest path minimum spanning tree max flow min cut . A corolary on linear duality.
Many (all?) of the easy problems are reducible simply to a linear program.
https://en.wikipedia.org/wiki/Lexicographic_breadthfirst_search Orders the vertices
Enumeration
https://en.wikipedia.org/wiki/BEST_theorem
Hamiltonian cycles
https://en.wikipedia.org/wiki/Hamiltonian_path Visit each vertex once. Travellign salesman problem is weighted relative
https://en.wikipedia.org/wiki/Eulerian_path visit every edge exactly once
Clique
https://en.wikipedia.org/wiki/Clique_problem Find maximal completely connected cluster (clique)
Ramsey theory guarantees clique exists
What Maximum Clique Algorithms Can Teach Us, And ViceVersa
Coloring
https://en.wikipedia.org/wiki/Graph_coloring Register allocation
https://en.wikipedia.org/wiki/Edge_coloring chormatic index = minimum number of edge colors https://en.wikipedia.org/wiki/Vizing%27s_theorem every graph can be edge colored with close to the maximum degree (edge count on single vertex)
Covering
https://en.wikipedia.org/wiki/Covering_problems
Isomorphism
nauty bliss saucy
graph canonicalization. Find a way to sort vertices? If they all had unique attributes, sweet. https://en.wikipedia.org/wiki/Graph_canonization
Then number of neighbors, other little facts. properties of neigbors Could use properties of nehigbors Could use labelling of neighbors too. That becomes a self consistent search. However, if you already know certain orderings (based on properties). Or you could branch I’m not sure I’m understanding this the same way nauty does.
Graph hashing
https://github.com/calebh/dihash merkle tree version into a tree width version. Can a canonical (minimal) identifier be found via dynamc programming?
disjoint set partition data structure?
isomorphism vs bisimulation. Different but related ideas.
automorphism  permutation of graph such that is an isomorphism vertex orbit
Practical graph isomorphism II
Colorings are partitions. A mapping from vertices to numbers. Refinement
Logic Programming with Graph Automorphism: Integrating naut with Prolog (Tool Description)
import pynauty
g = pynauty.Graph(10)
print(g)
Interning alpha equiv terms graphlog. graphs as datalog objects. Graphs as the fundamental store. automorphisms + group union find
AC terms are unstructure graphs Typically terms are structured graphs.
(1,(2,3))
def graph_from_term(t):
tid = fresh()
graph.add_vertex(tid)
if isinstance(t,set):
# for set or multiset we don't want the indirection
for n,c in enumerate(t):
graph.add_vertex(n) # add intermiedate node lbaelling which child. Or if we can edge label do that
graph.add_edge(tid,n)
cid = graph_from_vertex(c)
graph.add_vertex(n,cid)
return tid
import networkx as nx
import matplotlib.pyplot as plt
def fun(name):
def res(*args):
G = nx.Graph()
G.add_node(0)
for arg in args:
G = nx.disjoint_union(G,arg)
G.add_edge(0,len(G)  len(arg))
return G
return res
def const(a):
G = nx.Graph()
G.add_node((0,a))
return G
a = const("a")
plus = fun("+")
paa = plus(a,a)
G = plus(paa,paa)
nx.draw(G, with_labels=True)
#plt.show()
table = {}
def intern(G):
h = nx.weisfeiler_lehman_graph_hash(G) # we should obviously cache this, but whatever
matches = table.get(h)
if matches == None:
table[h] = [G]
return G
else:
if G in matches:
print("found")
return G
for m in matches:
#if m is G: # fast shortcutting
# return m
#else:
if nx.is_isomorphic(m,G):
print("iso")
return m
matches.append(G)
return G
a0 = intern(a)
print(table)
a = const("a")
a1 = intern(a)
print(table)
print(a1 is a0)
table = {} # enumerating in toplogica order would be nice. OrderedDict. We seem to be fine though. default dict guarantees this?
class Node():
def __init__(self,id, term):
self.id = id # use id(Node) ?
self.term = term
def __hash__(self):
return hash(self.id)
def __eq__(self,b):
return self is b #self.id == b.id ?
def __repr__(self):
return repr(self.term)
def hashcons(x):
# x is hashable and equality.
s = table.get(x)
if s != None:
return s
else:
n = Node(len(table), x)
table[x] = n
return n
def hashset(iter):
s = frozenset(iter)
return hashcons(s)
def reify(): # reify returns the set of all things we've seen so far. Wellfounded. We could have tracked them.
return hashset(table.values())
# reify is transitively closed
empty = hashset([])
one = reify()
two = reify()
three = reify()
print(three)
three2 = hashset([two, one, empty])
print(three2 is three)
print(three2.id)
print(three.id)
print(three2)
print(three)
def pair(a,b):
return hashset([hashset([a]), hashset([a,b])])
print(pair("a","b"))
print(pair("a","b") is pair("a","b"))
def union(a,b):
return hashset(a.term  b.term)
def trans(s):
if isinstance(s.term, set):
set(trans(x) for x in s.term)
return
else:
return s
Hmm. this is also basically a method for tree isomorphism. More or less follows the lins of the ahu algorithm.
Hash consing generates ids that witness the well foundedness. hashunconsing
A unique id, but then we also kind of want to go back from id to terms. We could maintaini an id table, but from whence do we receive id?
Our memoizing table choices

Int > Term
Hash consing does maximal compression to unique CSE form. To hash cons already built stuff is to perform congruence closure? disjoint sets of tid.
add(1,2,3).
add(1,2,4).
add(3,4,5).
root(Z,Z1) : add(X,Y,Z), add(X1,Y1,Z1), root(X1,Xr), root(X,Xr), root(Y,Y2), root(Y1, Yr).
Hash consing is not appropriate for context dependency. The sharing is not dignified. Bisimulation as equivalence algebraic equivalence graph equivalence
AC shuffling. Terms equal modulo AC. Terms equal modulo AC and Alpha. That’s kind of cool.
query automorphisms  permutation symmettries of a conjunctive query.
query containment / query equivalence
Queries as ported hypergraphs. Chandra and Merlin. Bonchi says https://www.irif.fr/~greta/event/jul2nd2021bonchi/
from z3 import *
S = Sort("S")
x, y, z = Consts("x y z", S)
R = Function("R", S,S,BoolSort())
Q1 = And(R(x,y), R() )
Implies()
import networkx as nx
class HyperGraph(nx.Graph):
counter = 0
def add_node(i, attrs):
attrs[""] = "node"
super.add_node(i, )
def add_hyperedge(*nodes):
self.counter += 1
attr["type"] = "hyperedge"
super.add_node(counter, attr)
for n in nodes
super.add_edge(self.counter , n)
class Var():
def __init__(self, x):
self.x = x
class Query(HyperGraph):
#def var(x):
# actually since it adds not seen nodes anyhow, don't bother
# self.add_node(x)
# return Var(x)
def relation(name):
def res(*args):
self.add_node(name) # hyperedge modelled as
for i in args:
if isinstance(i,Var):
self.add_edge( , , )
h(X,Y)
Uniqueness is kind of easier to enforce than maximal sharing (?) because subgraph identity is tough. One incoming edge. Forbid subgraph automorphisms? Can that be done? A minimal (labeled/colored) graph that you have a homomorphism to. Does this minimal graph have automorphisms?
Approximate matching. Convex relaxation
Quadratic assignement problem
On convex relaxation of graph isomorphism
MILP
A permutation matrix = 01 matrix with all
$PBP^T = A$
doubly stochastic matrix
https://en.wikipedia.org/wiki/Graph_homomorphism generalization of coloring. colorings are homomoprhisms to complete graph
Weisfeiler and Leman Test. https://davidbieber.com/post/20190510weisfeilerlehmanisomorphismtest/ An iterative thing. Get a bunch of fingerprints. Actually looks a lot like partition refinement for autmata minimization. I suppose taking a graph, reducing to a canonical automata is a reasonable canonical object.
A SHORT TUTORIAL ON THE WEISFEILERLEHMAN TEST AND ITS VARIANTS
for n in G.neighbors():
hash((selfid,frozenset( G.neighbors ))) # multiset is better.
Eigenvalues of graph laplacian are a fingerprint. Automorphisms ought to show up in representation theory stuff somehow?
subgraph isomorphgism
https://en.wikipedia.org/wiki/Subgraph_isomorphism_problem
subgraph matching and query containment. You can use SQL to build a pattern that matches against a database. This is part of a general framework of homomorphisms.
CREATE TABLE k3(a,b);
INSERT INTO k3 VALUES (1,2), (2,3), (3,1);
INSERT INTO k3 SELECT k3.b, k3.a FROM k3;
SELECT * FROM k3;
 automorphisms
SELECT e1.a, e1.b, e2.a, e2.b, e3 FROM k3 as e1, k3 as e2, k3 as e3 WHERE e1.b = e2.a and e2.b = e3.a and e3.b = e1.a;
Analysis of Graph Transformation Systems: Native vs Translationbased Techniques https://nicolasbehr.gitlab.io/ReSMT/#f1
https://en.wikipedia.org/wiki/Induced_subgraph induced subgraph. Subset of vertices + all internally connected edges.
neural subgraph matching subgraph mining
Graph Neural Network
Stanford CS224W: Machine Learning with Graphs
the power of graph learning  simons https://www.youtube.com/watch?v=iAxrFW1ku4I
Graph Rewriting / Graph Transformation
“Graph grammars”
See term rewriting drags term graphs
graph transformation Greta Graph TRansformation Theory and Applications (GReTA) internationa http://graphtransformationforsoftwareengineers.org/ https://www.irif.fr/~greta/ Graph Transformation for Software Engineer He’s been givng tutorials
ICGT international conference on graph transformations double pushout. A pattern L. the “kept” bits K the right hand side R, glued in
A Software Package for Chemically Inspired Graph Transformation https://jakobandersen.github.io/mod/ Why did they name it this. Hmm. reaction enumeration. Chemical reaction modelling. That’s intriguing.
AGG GP 2 https://github.com/UoYCSplasma/GP2
GGL https://www.tbi.univie.ac.at/software/GGL/ graph grammar obrary. Built on bosot graphs GDGEN.Net PORGY https://www.youtube.com/watch?v=D_YHlXawg9o&ab_channel=GReTASeminar
GRAPE verigraph https://link.springer.com/chapter/10.1007/9783319753966_9
galoisenne Lots of cool links
https://github.com/AlgebraicJulia/AlgebraicRewriting.jl catlab. CSet rewriting. https://arxiv.org/abs/2111.03784 Computational categorytheoretic rewriting. A generalization of graph rewriting? Typed graphs.
Table 1 AGG[34] Y N S N 2017 Y N Both Groove[27] Y N S N 2021 Y N App Kappa[14] N N N 2021 Y Y App VeriGraph[1] Y N D Y 2017 N Y Lib ReGraph[13]
Chemical rewriting “Chemical Graph Transformation and Applications” https://www.youtube.com/watch?v=mzIXfspeJE&ab_channel=GReTASeminar SMILES is a graph. chemical databases. See note on chemistry
My temptation would be to: find a pattern L (using datalog/sql probably), store result in an env dict, blow it all away, paste in R filling in from the env.
The “env” can be represented as a graph homomorphism though from the pattern (a dictionary mapping vertices to vertices and edges to edges).
CHR
CHYP replacement
wacksmackadoo wofram hypergraph rewriting https://www.wolframphysics.org/
import sqlite3
import networkx as nx
def query_of_graph(G):
counter = 0
froms = []
wheres = []
for (i,j) in G.edges:
e = f"e_{i}_{j}"
froms.append(f"edge AS {e}")
wheres.append(f"{e}.i = {i}")
wheres.append(f"{e}.j = {j}")
froms = ",".join(froms)
wheres = "AND".join(wheres)
return f"FROM {froms} WHERE {wheres}"
def db_of_graph(G):
db.execute("INSERT INTO verts VALUE (?)", G)
db.execute("INSERT INTO edge VALUES (?,?)", G.edges)
db.execute("INSERT INTO attrs VALUES (?,?)", [])
import networkx as nx
G= nx.Graph()
G.add_node(1, foo=7)
print(type(G.nodes[1]))
print([type(n) for n in G.nodes])
import sqlite3
import networkx as nx
def db_of_graph(conn, G):
con.executemany("INSERT INTO nodes VALUES (?)", map(lambda v : (v,), G.nodes))
con.executemany("INSERT INTO edges VALUES (?, ?)", G.edges)
def graph_of_db(con):
G = nx.DiGraph()
res = con.execute("SELECT * FROM nodes")
G.add_nodes_from(map(lambda x: x[0], res.fetchall()))
res = con.execute("SELECT * FROM edges")
G.add_edges_from(res.fetchall())
return G
def query_of_graph(G):
selects = []
froms = []
wheres = []
for node in G:
froms += [f"nodes AS v{node}"]
selects += [f"v{node}.v AS v{node}"]
for i, (a,b) in enumerate(G.edges):
froms += [f"edges as e{i}"]
wheres += [f"e{i}.src = v{a}.v" , f"e{i}.dst = v{b}.v"]
sql = "SELECT " + ", ".join(selects) + \
"\nFROM " + ", ".join(froms) + \
"\nWHERE " + " AND ".join(wheres)
return sql
G = nx.path_graph(7, create_using=nx.DiGraph)
lhs = nx.path_graph(3, create_using=nx.DiGraph)
con = sqlite3.connect(":memory:")
con.execute("CREATE TABLE nodes(v)")
con.execute("CREATE TABLE edges(src,dst)")
db_of_graph(con, G)
res = con.execute(query_of_graph(lhs))
print(res.fetchall())
# Result: [(0, 1, 2), (1, 2, 3), (2, 3, 4), (3, 4, 5), (4, 5, 6)]
print(graph_of_db(con))
"DELETE FROM nodes WHERE nodes.v = ?"
"DELETE FROM edges where edges.src = ? OR edges.dst = ?"
con.execute("DELETE FROM nodes")
con.execute("DELETE FROM edges")
colors = nx.complete_graph(2) # a two coloring
db_of_graph(con,colors)
# symmetrize. Maybe db_of_graph should do this. if not isinstanc(G,nx.DiGraph)
con.execute("INSERT INTO edges SELECT edges.dst, edges.src FROM edges")
res = con.execute("SELECT * FROM edges")
res = print(res.fetchall())
res = con.execute(query_of_graph(G))
print(res.fetchall())
# [(1, 0, 1, 0, 1, 0, 1), (0, 1, 0, 1, 0, 1, 0)]
examples:
 term rewriting
 https://en.wikipedia.org/wiki/Graph_reduction Lazy, STG
 OPtimal reduction
 Category
 egraphs
 https://github.com/UoYCSplasma/GP2/tree/master/programs 2 coloring, topological sort, transitive closure. Recognizers of series paralle, acyclic graphs. That’s cool.
import networkx as nx
G = nx.Graph()
G.add_edges_from([
(1,2),
(2,3)
])
class Rule():
def __init__(lhs,rhs,iterface):
self.lhs = nx.Graph()
self.lhs.add_edges_from(lhs)
self.rhs = nx.Graph()
self.rhs.add_edges_from(rhs)
class TreeRule(Rule):
def __init__(self):
def run_rule(G, rule):
match = nx.subgraph_match(rule.lhs, G)
delete(G, match)
return apply(G, rule.rhs)
morph(id,X,Y), morph(F,Y,Z) ==> morph(F,X,Z).
morph(swap, ...) %hmm.
graphize(id, X,Y) : morph(id,X,Y).
graphize(comp(F,G), X, Z) : %gensym(Y),
graphize(F,X,Y), graphize(G,Y,Z).
graphize(otimes(F,G), [X,Y], [Z,W]) : graphize()
graphize(id,X,X).
graphize(comp(F,G), X,Z) : graphize(F,X,Y), graphize(G,Y,Z).
graphize(otimes(F,G), XY, ZW) : graphize(F,XA, ZB), graphize(G, AY,BW). % difference lists. or append(X1,X2,X),
graphize(swap, [A,BZ]Z, [B,AZ]Z).
% inverse collection out of chr
morph(F,A,B) \ collect(F,A,B) ==> true.
collect(F,A,B), collect(G,B,C) ==> collect(comp(F,G),A,C).
auto : autochr, deauto.
autochr
prove : auto, rw(foo), simp, .
main() : theorem(forall(X,foo(X)),
(auto, simp, rw, )
)
vs
theorem(forall(X,foo(X))),
auto, simp, qed,
theorem(myname, forall(X,foo(X))).
auto. simp. qed.
% axiom
asserta(theorem(forall(X,foo(X)))).
axiom(F) : asserta(theorem(forall)X,foo(X)).
Pfaffian orientation
https://en.wikipedia.org/wiki/Pfaffian_orientation A directon to each edge such that each cycle https://tex.stackexchange.com/questions/692892/kasteleynorientationforsquarelattice
Kastelyn orietnation
FKT algorithm for counting perfect matchings. Katelyn Temperley Fisher. Dimers. pfaffian of skew symmettric matrix can be reduced to determinant counting is useful in stat phys. entropy. https://en.wikipedia.org/wiki/Geometrical_frustration https://en.wikipedia.org/wiki/Holographic_algorithm
Matchings
https://en.wikipedia.org/wiki/Matching_(graph_theory) a set of edges such that none share vertices
maximum matching
https://en.wikipedia.org/wiki/Perfect_matching pick edges such that exactly one edge touches every vertex
Related to https://en.wikipedia.org/wiki/Edge_cover where each vertex must touch at least one edge
Infinite Graphs
https://en.wikipedia.org/wiki/End_(graph_theory)
Compactness
Misc
https://en.wikipedia.org/wiki/Graph_polynomial Tutte polynomial
bipartite graphs