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Central Nodes (Python and Central Nodes (Python and

Central Nodes (Python and - PowerPoint Presentation

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Central Nodes (Python and - PPT Presentation

Gephi Python code Use Metricspy from the workshops website 2 NetworkX Reference PDF reference for NetworkX Github interactive version 3 Finding centralities The part of the code that finds ID: 743669

nodes centrality gephi graph centrality nodes graph gephi network python closeness eigenvector networkx part top version save choose centralities

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Presentation Transcript

Slide1

Central Nodes

(Python and

Gephi)Slide2

Python code

Use Metrics.py from the workshop’s website

2Slide3

NetworkX Reference

PDF reference for NetworkXGithub interactive version

3Slide4

Finding centralities

The part of thecode that finds and printscentralities:Output not sorted,

rather a list as:

Node

:

centrality_value

4Slide5

Follow along or try it

Follow along:http://faculty.nps.edu/rgera/MA4404/PythonCode/MetricsV2.htmlOr

try the code on your computer

, the same zip file from the main page, and use

MetricsV2-ForPython3.py (for version 3.5) or MetricsV2-ForPython2.py

(for version 2.7)

5Slide6

Top centrality nodes Cod: MetricsV2-ForPython2 (or 3)

This part of the code arranges and prints the top 10 central nodes (degree centrality)Python 2.7:

Python 3.5:

The ten was arbitrary, change it here:

6Slide7

Closeness/Eigenvector

The closeness centrality is normalized by dividing by (n-1), where n is the number of nodes in the connected part of graph containing the node. If the graph is not completely connected, this algorithm computes the closeness centrality for each connected part separately

.

The eigenvector calculation is done by the power iteration method and has no guarantee of convergence. The iteration will stop after

max_iter

iterations or an error tolerance of

number_of_nodes(G)*

tol has been reached. For directed graphs this is “left”

eigevector

centrality which corresponds to the in-edges in the graph. For out-edges eigenvector centrality first reverse the graph with

G.reverse

().

7

https

://

networkx.github.io/documentation/networkx-1.9/reference/generated/networkx.algorithms.centrality.eigenvector_centrality.html#networkx.algorithms.centrality.eigenvector_centralitySlide8

Visualizing them on the network

Code that identifies the top 30%:http://glowingpython.blogspot.com/2013/02/betweenness-centrality.html

I have not

implemented

it, you can try

it if you would

like.

8Slide9

Gephi

Excellence Through KnowledgeSlide10

Resources for Gephi:

Overview and explanations of GephiGephi’s

overview

tutorial

An

introductory video to create data for Gephi

and to use degree, closeness and betweeness (also posted on the website

unde today’s lecture).Gephi’s

overview of layouts

Basic navigationSlide11

Layouts

Choose the appropriate layout so that visualization is meaningful. Common force directed (repulsion) ones:Force Atalas

2 (It

is focused on

being

useful to explore and get meaning for real

data, and a good readability, slow)

Yifan Hu (similar to FA2, fast, good for large graphs)Fruchterman-Reingold (The

nodes are the mass particles and the edges are springs between the particles. The algorithms try to minimize the energy of this physical system. It has become a standard but remains very slow

.)

OpenOrd

layout (good for communities)

Not force directed:

Expansion

Geographic

map with

GeoLayout

11Slide12

SAVE

Once you have a visualization that you like, save the network, so that the next time you open it looks the sameCannot use undo in Gephi

When you run an analysis, save the network again with a different name for future references

When you open part of a network on a new tab in

Gephi

, save that as well.

12Slide13

Preview Tab

Click Preview next to the Data

Laboratory

, you

might like that

view of the

network better:If you export, then this is what

you export:

13Slide14

Centrality graph example

14

Source: Discovering Sets of Key Players in Social Networks – Daniel Ortiz-Arroyo – Springer 2010/

Open both your network and

this graph

(you can also find it on the

website

).Slide15

Centralities

Click diameter under Statisticsmodule on the rightCentralities that are available:

Closeness

PageRank

HITS

BetweenessFor directed

graphs, check:

15Slide16

Ranking nodes by centrality

Once you ran a metric, you can size/color the nodes based on your choices you ran.Under on the topleft, choose

Nodes

and

either

size or

colorDepending on the version you run, you will see:

16Slide17

Filtering nodes by centralty

Find Filters on the top right, next to Statistics

Under topology, you can find

the centralities

Choose one, drag and drop

it to the Queries

Choose the bounds needed.

17Slide18

Export

You can export the visualized graph as SVG or PDF:Go to preview (fix if needed)Resize for large networksClick SVG

(SVG is

vectorial

graphics like PDF so they scale to different sizes nicely)

18Slide19

Other metrics

19

Average path length: under

the statics module, right

Computes the average of

shortest paths between all

pairs of nodes Result:Slide20

Most of these are not in Gephi/Python/R

20

An interactive periodic table of centralities:

http://

schochastics.net/sna/periodic.html