 Copyright 2011 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute www.deri.ie Enabling Networked Knowledge.

Slides:



Advertisements
Similar presentations
Class 12: Communities Network Science: Communities Dr. Baruch Barzel.
Advertisements

Mobile Communication Networks Vahid Mirjalili Department of Mechanical Engineering Department of Biochemistry & Molecular Biology.
AI Pathfinding Representing the Search Space
Network Overview Discovery and Exploration for Excel (NodeXl) Hands On Exercise Presented by: Samer Al-khateeb Class: Social Media Mining and Analytics.
Introduction to NodeXL Like MSPaint™ for graphs. — the Community.
CSE 5243 (AU 14) Graph Basics and a Gentle Introduction to PageRank 1.
Analysis and Modeling of Social Networks Foudalis Ilias.
Relationship Mining Network Analysis Week 5 Video 5.
Analysis of Social Media MLD , LTI William Cohen
Networks. Graphs (undirected, unweighted) has a set of vertices V has a set of undirected, unweighted edges E graph G = (V, E), where.
DATA MINING LECTURE 12 Link Analysis Ranking Random walks.
Lecture 9 Measures and Metrics. Structural Metrics Degree distribution Average path length Centrality Degree, Eigenvector, Katz, Pagerank, Closeness,
Using Structure Indices for Efficient Approximation of Network Properties Matthew J. Rattigan, Marc Maier, and David Jensen University of Massachusetts.
CS 728 Lecture 4 It’s a Small World on the Web. Small World Networks It is a ‘small world’ after all –Billions of people on Earth, yet every pair separated.
Sampling from Large Graphs. Motivation Our purpose is to analyze and model social networks –An online social network graph is composed of millions of.
CSE 321 Discrete Structures Winter 2008 Lecture 25 Graph Theory.
Network Science and the Web: A Case Study Networked Life CIS 112 Spring 2009 Prof. Michael Kearns.
HCC class lecture 22 comments John Canny 4/13/05.
CS8803-NS Network Science Fall 2013
Network Measures Social Media Mining. 2 Measures and Metrics 2 Social Media Mining Network Measures Klout.
(Social) Networks Analysis III Prof. Dr. Daning Hu Department of Informatics University of Zurich Oct 16th, 2012.
1. cluster the data. 2. for the data of a cluster, set up the network. 3. begin at a random vertex as source/sink s, choose its farthest vertex as the.
1 iSee Player Tutorial Using the Forest Biomass Accumulation Model as an Example ( Tutorial Developed by: (
Biological Networks Lectures 6-7 : February 02, 2010 Graph Algorithms Review Global Network Properties Local Network Properties 1.
LANGUAGE NETWORKS THE SMALL WORLD OF HUMAN LANGUAGE Akilan Velmurugan Computer Networks – CS 790G.
Small World Social Networks With slides from Jon Kleinberg, David Liben-Nowell, and Daniel Bilar.
Science: Graph theory and networks Dr Andy Evans.
Using Graph Theory to Study Neural Networks (Watrous, Tandon, Conner, Pieters & Ekstrom, 2012)
An Introduction to Social Network Analysis Yi Li
Vertices and Edges Introduction to Graphs and Networks Mills College Spring 2012.
 Copyright 2011 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute Enabling Networked Knowledge.
3. SMALL WORLDS The Watts-Strogatz model. Watts-Strogatz, Nature 1998 Small world: the average shortest path length in a real network is small Six degrees.
1. 2 CIShell Features A framework for easy integration of new and existing algorithms written in any programming language. CIShell Sci2 Tool NWB Tool.
Most of contents are provided by the website Graph Essentials TJTSD66: Advanced Topics in Social Media.
Slides are modified from Lada Adamic
The Structure of the Web. Getting to knowing the Web How big is the web and how do you measure it? How many people use the web? How many use search engines?
Community Discovery in Social Network Yunming Ye Department of Computer Science Shenzhen Graduate School Harbin Institute of Technology.
CS 590 Term Project Epidemic model on Facebook
Small World Social Networks With slides from Jon Kleinberg, David Liben-Nowell, and Daniel Bilar.
LexPageRank: Prestige in Multi-Document Text Summarization Gunes Erkan, Dragomir R. Radev (EMNLP 2004)
Topical Scientific Community —A combined perspective of topic and topology Jin Mao Postdoc, School of Information, University of Arizona Sept 4, 2015.
Copyright © Curt Hill Graphs Definitions and Implementations.
Informatics tools in network science
Graphs Definition: a graph is an abstract representation of a set of objects where some pairs of the objects are connected by links. The interconnected.
Selected Topics in Data Networking Explore Social Networks:
Importance Measures on Nodes Lecture 2 Srinivasan Parthasarathy 1.
Models of Web-Like Graphs: Integrated Approach
Topical Analysis and Visualization of (Network) Data Using Sci2 Ted Polley Research & Editorial Assistant Cyberinfrastructure for Network Science Center.
S OCIAL N ETWORK A NALYSIS F OR D UMMIES Y ANNE B ROUX DH S UMMER S CHOOL L EUVEN, S EPTEMBER
GRAPH AND LINK MINING 1. Graphs - Basics 2 Undirected Graphs Undirected Graph: The edges are undirected pairs – they can be traversed in any direction.
Topics In Social Computing (67810) Module 1 (Structure) Centrality Measures, Graph Clustering Random Walks on Graphs.
Randolph’s Community Health Network RANDOLPH HEALTH SERVICE AREA JULY 2015.
Centralities (Gephi and Python)
Central nodes (Python and Gephi).
Social Networks Analysis
Empirical analysis of Chinese airport network as a complex weighted network Methodology Section Presented by Di Li.
Gephi Gephi is a tool for exploring and understanding graphs. Like Photoshop (but for graphs), the user interacts with the representation, manipulate the.
Network analysis.
How to Create a KPI Dashboard Report
Network Science: A Short Introduction i3 Workshop
Section 8.6 of Newman’s book: Clustering Coefficients
Department of Computer Science University of York
Central Nodes (Python and Gephi).
Clustering Coefficients
Gephi.
(Social) Networks Analysis II
Graphs G = (V, E) V are the vertices; E are the edges.
Practical Applications Using igraph in R Roger Stanton
Centralities Using Gephi and Python Prof. Ralucca Gera,
Digital humanities Filtering.
Presentation transcript:

 Copyright 2011 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute Enabling Networked Knowledge Gephi Workshop 2 David Crowley Maciej Dabrowski

Digital Enterprise Research Institute Enabling Networked Knowledge Install netvizz app

Digital Enterprise Research Institute Enabling Networked Knowledge Install netvizz app

Digital Enterprise Research Institute Enabling Networked Knowledge Numbers will be different

Digital Enterprise Research Institute Enabling Networked Knowledge Open in Gephi Open Gephi – clicking should open it if not you can open it through Gephi Save a “clean” copy before playing  So you can always run through the workshop again or if you mess with it too much you can start again with the original

Digital Enterprise Research Institute Enabling Networked Knowledge

Digital Enterprise Research Institute Enabling Networked Knowledge So we have a hairball…. We can run a layout Force Atlas 2 Gephi designed layout – good for small to medium graphs 10,000 nodes +

Digital Enterprise Research Institute Enabling Networked Knowledge Force Atlas 2 Run the layout for a few seconds and press stop You should end up with a graph like this (not exactly the same or it could even be quite different)

Digital Enterprise Research Institute Enabling Networked Knowledge Gravity If your graph has loads of “islands” or “small clusters” dispersed then you can up the Gravity to bring them together – try 2 and press run and stop a few seconds later

Digital Enterprise Research Institute Enabling Networked Knowledge Gravity (2) Gravity = 1 Gravity = 4

Digital Enterprise Research Institute Enabling Networked Knowledge Options Play with the other layout options  Hovering over an option will give you an explanation  Selecting them will show you what it means Important thing to remember is that this is not changing your graph this is just for visualisation i.e. same graphs but different ways to view it You could easily view this data in tabular view (Excel) but hard to get any insight from it

Digital Enterprise Research Institute Enabling Networked Knowledge Ranking (by degree) Degree – number of connections (not directed)

Digital Enterprise Research Institute Enabling Networked Knowledge Centrality

Digital Enterprise Research Institute Enabling Networked Knowledge Added colours (red = high degree)

Digital Enterprise Research Institute Enabling Networked Knowledge Graph/Network Statistics On the right hand side

Digital Enterprise Research Institute Enabling Networked Knowledge Network Measures Average Degree Avg. Weighted Degree Network Diameter Graph Density HITS Modularity PageRank Connected Components Avg. Clustering Components Eigenvector Centrality Avg. Path Length

Digital Enterprise Research Institute Enabling Networked Knowledge Network Measures Average Degree – handy to check how connected the graph is Average Weighted Degree – if the vertices have edge weights Network Diameter – average graph distance between all pairs of nodes – connected noides have graph distance = 1. Diameter is the longest graph distance between any two nodes in the network Graph Density – Measures how close the network is to complete. A complete graph has all possible edges and density = 1

Digital Enterprise Research Institute Enabling Networked Knowledge Network Measures HITS – measures “authority” and “quality” at each node Modularity – used for community detection – for a random network modularity = 0. Randomness is not “natural” PageRank – think of nodes as pages – simulates user clicks on links on pages Connected Components – weakly (undirected) or strongly connected (directed)

Digital Enterprise Research Institute Enabling Networked Knowledge Network Measures Avg. Clustering Components – The clustering coefficient (Watts-Strogatz), when applied to a single node, is a measure of how complete the neighborhood of a node is. When applied to an entire network, it is the average clustering coefficient over all of the nodes in the network. Used to find “small world” networks Eigenvector Centrality – A measure of node importance in a network based on a node’s connections (similar to PageRank) Avg. Path Length

Digital Enterprise Research Institute Enabling Networked Knowledge Ranking (by size) If you click on the reddish diamond shape (size/weight) You can change how you visualise your graph Changes the node sizes according to degree

Digital Enterprise Research Institute Enabling Networked Knowledge Ranking (by size) Min -5 Max – 50

Digital Enterprise Research Institute Enabling Networked Knowledge With Overlap Turned Off