building a reading map based on a reduced graph of co-edited pages

The drawing of the graph of pages linked when their share editors is very messy. It also show a non-pertinent structure to achieve our goal. On an another hand, the hyperlink graph is also not very pertinent because it is more about semantical and logical relationships between content that the after effect of intelligence shaping a knowledge landscape.

When trying to read a knowledge map for learning, it is important to keep the number of paths/edges in a low order scope. In that order, we propose here a strategy to reduce the number of edges while keeping a socio-semantical logic and reducing the influence of strong attractors like "Pi" or "mathematics".

importing original graphs

In [1]:
%run "libraries.ipynb"
In [2]:
import networkx as nx
from IPython.display import display, HTML

In order to achieve our goal, we are going to start with the initial bi-partite graph composed by pages and editors.

In [3]:
# list of pages
pages = map(lambda x: x.strip(),"data/pagenames.txt","r", "utf-8-sig").readlines())

# pages-editors bi-partite graph
pages_editors_graph = nx.read_gexf("data/pages-editors.gexf")

# pages graph projection from pages-editors bi-partite graph
pages_graph = nx.read_gexf("data/pages-linked-by-coeditors.gexf")

reducing the graph

The main strategy we are going to use is not to go with computing out-degrees but only keep in-degrees. Big pages like "Pi" or "mathematics" tend to attract more co-editors and will overpower the other pages and break the logicality of the paths we propose by being to central.

The main hypothesis we propose here is that in a setting of recommanding the 3 next pages a user need to read a particular page $p_i$ are the 3 pages that rank $p_i$ at their top.

The implemented below is pretty straighforward:

  • for a page $p_i$
    • take all its neighbors $p_{ij}$
      • compute the number of co-editors between $p_i$ and $p_{ij}$
      • rank $p_i$ in the neighborhood of $p_{ij}$
    • keep the 3 pages which rank $p_i$ as highest
In [4]:
g = nx.DiGraph()

for p in pages:
    #print p
    #print "==========="
    # calculate rank in neighbor top co-edited ranking
    nb = sorted(pages_graph["p:%s" % (p)].items(),
        key=lambda (k,x): -int(x["coeditors"]))
    for name, info in nb:
        nb_mirror = sorted(pages_graph[name].items(),
                key=lambda (k,x): -int(x["coeditors"]))
        nb_mirror = [ x[0] for x in nb_mirror ]
        editors = pages_editors_graph[name]        
        info["editors"] = len(editors)
        info["exclusive editors"] = len([n for n in editors if len(pages_editors_graph[n]) == 1 ])
        info["ranking"] = nb_mirror.index("p:%s" % (p)) + 1
    # get the 3 pages to which the current page are in top ranking
    nb2 = sorted(nb, key=lambda (x): x[1]["ranking"])
    for name, info in nb2[0:3]:
        #print name
        g.add_edge(p, name.split(":")[1])

    #print ""
In [5]:
print "reduced graph"
print "============="
print "nodes: %s" % (len(g.nodes()))
print "edges: %s" % (len(g.edges()))
print "reduction: %s/%s (%s)" % (len(g.edges()),
reduced graph
nodes: 303
edges: 909
reduction: 909/39927 (0.0227665489518)

With only 303 nodes, edges are the main source of noise. Our method demonstrate that while keeping all node connected we also reduce drastically the number of edges by keeping only 2% of them.


In the following section, we are going to use the Louvain partitioning method in order to check if the reduced graph structure also keep important information and therefore provide use more readibility about our data.

All the muscle are done by Thomas Aynaud the implentation of into a python library.

In [6]:
import community
partitions = community.best_partition(g.to_undirected())
In [7]:
def print_groups(communities):
    html = "<table>"

    for c, ps in communities.iteritems():
        html += "<tr><td style=\"width: 100px; text-align: center; \"><h3>group %s</h3></td><td>" % (c)
        html += ", ".join(map(lambda x: u"<a href=\"{0}\" target=\"_blank\">{0}</a>".format(x), ps))
        html += "</td></tr>"
    html += "</table>"

communities = {}
for k, v in partitions.iteritems():
    communities.setdefault(v, []).append(k)


group 0

Discrete geometry, Polygon triangulation, Heronian tetrahedron, Point in polygon, Convex hull, Mathematical morphology, Digital geometry, Geometry of numbers, Minkowski's theorem, Parallelepiped, Coordinate-free, Image analysis, Zonohedron, Polytope, Delaunay triangulation, Lattice (group), Symmetry, Voronoi diagram, Polyhedron, Dissection problem, Taxicab geometry, Computational geometry, Heronian triangle, Angular defect, Hilbert's third problem, Minkowski addition, Pythagorean triple, Graham scan, Convex geometry, Point location

group 1

Triangle inequality, Integral geometry, Invariant (mathematics), Point (geometry), Klein geometry, Polar sine, Root system, Noncommutative geometry, Pappus's centroid theorem, Parametric surface, Contact geometry, Group action, Parabolic geometry (differential geometry), Riemannian geometry, Analytic geometry, Dividing a circle into areas, Systolic geometry, Line (geometry), Central angle, Oval (projective plane), N-sphere, Strähle construction, Affine space, Euclidean distance, Lie sphere geometry, Concurrent lines, Roman surface, Tropical geometry, Parabola, Normal (geometry), Arc (projective geometry), Parallelogram law, Projective plane, Topology, Symplectic geometry, Minkowski space, Differential geometry, Conformal geometry, 3-sphere, Projective space

group 2

Prismatoid, Kepler–Poinsot polyhedron, Point groups in three dimensions, Tetrahedron, Polytope compound, Star polygon, Wallpaper group, Space group, Polygon, Honeycomb (geometry), Pattern, Frieze group, Deltahedron, Pyramid (geometry), Prism (geometry), Uniform tessellation, Platonic solid, Point groups in two dimensions, Uniform polyhedron, Schläfli symbol, Binary space partitioning, Johnson solid, Archimedean solid, Crystal system, Point group

group 3

Symmedian, 2D geometric model, Congruence (geometry), Isosceles trapezoid, Equilateral triangle, List of circle topics, List of triangle topics, Acute and obtuse triangles, Incircle and excircles of a triangle, Focus (geometry), Tangential quadrilateral, Rectangle, Heron's formula, Circumscribed circle, Brahmagupta's formula, Cuisenaire rods, List of triangle inequalities, Nine-point circle, Inscribed angle, Altitude (triangle), Quadrilateral, Curve of constant width, Kite (geometry), Coordinate rotations and reflections, Internal and external angle, Isoperimetric inequality, Matrix representation of conic sections, Orthodiagonal quadrilateral, Bicentric quadrilateral, Equidiagonal quadrilateral, Trapezoid, Wallace–Bolyai–Gerwien theorem, Euler line, Right triangle, Isosceles triangle, Sangaku, Integer triangle, Reuleaux triangle, Bretschneider's formula, Pedal triangle, Geometric shape, List of trigonometry topics, Pedoe's inequality, Angle, Mirror image, Concyclic points, Locus (mathematics), Pick's theorem, Cyclic quadrilateral, Rhombus, Cross section (geometry), Orthocentric system, Ellipse

group 4

Elliptic geometry, Spherical trigonometry, Steiner chain, Erlangen program, Riemann sphere, Soddy's hexlet, Spherical geometry, Penrose tiling, Prototile, Aperiodic tiling, Pascal's theorem, Homogeneous coordinates, Quasicrystal, Fractal, Wang tile, Desargues' theorem, Quantum geometry, The Method of Mechanical Theorems, Monge's theorem, Toric variety, Pappus's hexagon theorem, Hyperbolic geometry, Stereographic projection, Hilbert's axioms

group 5

Incidence (geometry), Absolute geometry, Squaring the circle, Dandelin spheres, Descriptive geometry, Poncelet–Steiner theorem, Duality (projective geometry), Cone, Line at infinity, Similarity (geometry), Angle trisection, Hyperplane at infinity, Regular Polytopes (book), Plane at infinity, Euclidean geometry, Point at infinity, Pythagorean theorem, Tessellation, Compass-and-straightedge construction, Symmetry group, Four-dimensional space, Regular polytope

group 6

Complex geometry, Geometrization conjecture, Ordered geometry, Kissing number problem, Napkin ring problem, Enumerative geometry, Leech lattice, Golden angle, Algebraic geometry, Birational geometry, Kepler conjecture, Power center (geometry), Projective geometry, Euclidean shortest path, Borromean rings, Non-Euclidean geometry, Ruppeiner geometry, Affine geometry, Mathematics and fiber arts, Coxeter group, Sphere packing, Chirality (mathematics), Ehrhart polynomial

group 7

Annulus (mathematics), Pi, 3D computer graphics, 2D computer graphics, Cavalieri's principle, Parallel (geometry), Geometry, Ptolemy's theorem, Van Hiele model, Convex uniform honeycomb, Relative direction, List of interactive geometry software, Trigonometry, Crystal, Astronomy, Solid geometry, Parabolic microphone, Shape, Dihedral angle, Straightedge, Parametric equation, Hidden line removal, Computer graphics, Handedness, Mathematics, Circle, Triangle, Constructive solid geometry

group 8

Shear mapping, Girard Desargues, Parallel postulate, Holditch's theorem, Inversive geometry, Distance geometry, Information geometry, Infinitesimal transformation, Finite geometry, Homothetic transformation, Pseudosphere, Isometry, Pons asinorum, Incidence geometry, Burmester's theory, Affine transformation, Glide reflection, Rotation (mathematics), Translation (geometry), Synthetic geometry, Mrs. Miniver's problem, Transformation geometry, Reflection (mathematics), Hyperplane

group 9

Epipolar geometry, Bézier curve, Eccentricity (mathematics), Semi-major axis, Paraboloid, Parabolic reflector, Spline (mathematics), Square, Non-uniform rational B-spline, Complex projective plane, Quadric, Conic section, Cross-ratio, Projective line over a ring, Sphere, Spheroid, Projective line, Hyperbola, Robot control, B-spline, Sphericon, Hermite spline, Ray tracing (graphics), Homothetic center, Ellipsoid, 3D projection, Ball (mathematics), Thales' theorem, Translational symmetry, Möbius transformation, Hadwiger's theorem, Homography, Torus, Hyperboloid

The partition given by the louvain partitioning system is not that helpfull but it also isolate the big generalist/abstract nodes into the group 7.

In [8]:
nx.write_gexf(g, "data/reading_maps/pages-coedited-reduced-3.gexf")

adjacency matrix

We are going to plot an adjacency matrix combined with partitions in order to visualy check the pertinence of the underlying structure. It gives a preview of the possibilty to draw a map with a find colouring to differientiate the different group of nodes but also apply a spacialization that will separate them geometricaly.

In [9]:
from matplotlib import patches

ordered_nodes = [ n for c, ps in communities.iteritems() for n in ps ]
adjacency_matrix = nx.to_numpy_matrix(g, nodelist=ordered_nodes)

fig = plt.figure(figsize=(5, 5)) # in inches

plt.imshow(adjacency_matrix, cmap="Greys", interpolation="none")

ax = plt.gca()

#print communities
idx = 0
for i,c in communities.iteritems():
    m = len(c)
    ax.add_patch(patches.Rectangle((idx, idx),
                                   m, # Width
                                   m, # Height
    idx += m

rending with force atlas 2 and sigma.js

This is not working yet but you can check the pdf rendering made with gephi.

In [10]:
<div id="graph_container"></div>
In [11]:

require.config({paths: { 'sigma': ""}});
//require.config({paths: {sigmagexf: ""}});

require(["sigma"], function(sigma){