Like this presentation? Why not share!

- 2013 NodeXL Social Media Network An... by Marc Smith 39659 views
- 最短経路問題 by chsommer 4532 views
- Exploring Social Media with NodeXL by Shalin Hai-Jew 593 views
- Social network Analysis - whys and ... by Muthu Kumaar Than... 3575 views
- Analyzing social media networks wit... by Marc Smith 17528 views
- Gephi Quick Start by Gephi Consortium 457945 views

9,453

-1

-1

Published on

Made as part of project work for ITB course at VGSOM IIT Kharagpur.

By : Mayank Mohan

Anuradha Chakraborty

( Batch of 2012)

No Downloads

Total Views

9,453

On Slideshare

0

From Embeds

0

Number of Embeds

79

Shares

0

Downloads

0

Comments

0

Likes

6

No embeds

No notes for slide

- 1. ANURADHA CHAKRABORTY (10BM60014) MAYANK MOHAN (10BM60048)
- 2. WHAT IS NODEXL? “NodeXL is a free, open-source template for Microsoft® Excel® 2007 and 2010 that makes it easy to explore network graphs. With NodeXL, a network edge list can be entered in a worksheet, a button can be clicked and graph can be seen, all in the familiar environment of the Excel window” WHO WILL REQUIRE IT? Students who are learning social media network analysis Professionals who are interested in applying network analysis to business problems
- 3. WHY IS IT EASY-TO-USE AND APPROPRIATE?Builds on the familiar spreadsheet paradigmProvide an easy-to-use tool for nonprogrammers A variety of visual properties Supports powerful filtering Calculates frequently used network metrics Offers rich support for diverse visual network layoutsIncludes powerful automated features, while allowing for manual control graphical designIntegrates metrics, statistical methods, and visualization: gains the benefit of all three approaches.Supports work with modest-sized networks of several thousand vertices
- 4. STEP 1: IMPORT DATATo know how to importdata, watch our video:http://youtu.be/39yXz72qdow
- 5. STEP 2: TAG NAMES TO VERTICES AND FILTER DATA
- 6. STEP 3: CLUSTER THE DATA
- 7. FACEBOOK TWITTER YOUTUBE MAIL FLICKR and a lot of other social networking sites..
- 8. Search word: “Kahaani” Limit : 300 We effectively found out the two people who were most effective in the conversation
- 9. Search word: “Blood Cancer” Limit : 300 It is evident that Social messages are more re-tweeted than advertisements or other breaking news We effectively found out the two people who were most effective in the conversation
- 10. And finally… We searched respectively 1. “Nike Discount” 2. “Adidas Discount” Search Limit: 300 What have we found?
- 11. “ADIDAS DISCOUNT” vs “NIKE DISCOUNT”
- 12. Search word: “Kahaani” Limit : 300 We effectively found out the two people who were most effective in the conversation
- 13. Search word: “Kahaani” Limit : 300 We effectively found out the two people who were most effective in the conversation
- 14. Search word: “Kahaani” Limit : 300 We effectively found out the two people who were most effective in the conversation
- 15. Search word: “Kahaani” Limit : 300 We effectively found out the two people who were most effective in the conversation
- 16. Search word: “Kahaani” Limit : 300 We effectively found out the two people who were most effective in the conversation
- 17. Search word: “Kahaani” Limit : 300 We effectively found out the two people who were most effective in the conversation
- 18. Flexible Import and Export Import and export graphs in GraphML, Pajek, UCINet, and matrix formats Direct Connections to Social Networks Import social networks directly from Twitter, YouTube, Flickr and email, or use one of several available plug-ins to get networks from Facebook, Exchange and WWW hyperlinks. Zoom and Scale Zoom into areas of interest, and scale the graphs vertices to reduce clutter. Flexible Layout Use one of several "force-directed" algorithms to lay out the graph, or drag vertices around with the mouse. Have NodeXL move all of the graphs smaller connected components to the bottom of the graph to focus on whats important. Easily Adjusted Appearance Set the color, shape, size, label, and opacity of individual vertices by filling in worksheet cells, or let NodeXL do it for you based on vertex attributes such as degree, betweenness centrality or PageRank. Dynamic Filtering Instantly hide vertices and edges using a set of sliders—hide all vertices with degree less than five, for example. Powerful Vertex Grouping Group the graphs vertices by common attributes, or have NodeXL analyze their connectedness and automatically group them into clusters. Make groups distinguishable using shapes and color, collapse them with a few clicks, or put each group in its own box within the graph. "Bundle" intergroup edges to make them more manageable. Graph Metric Calculations Easily calculate degree, betweenness centrality, closeness centrality, eigenvector centrality, PageRank, clustering coefficient, graph density and more. Task Automation Perform a set of repeated tasks with a single click
- 19. WHAT IS A NETWORK? “A network or graph is defined as a collection of n nodes connected by m edges. A network can be ◦ Directed: The edges point in one direction ◦ Undirected: The edges go in both directions Hypergraph :The graphs where the edges can join more than two vertices together. The edges can be weighted, contain self loops, and have different properties within the edges or nodes
- 20. VERTICES In graph theory, a vertex (plural vertices) or node is the fundamental unit out of which graphs are formed: an undirected graph consists of a set of vertices and a set of edges (unordered pairs of vertices), while a directed graph consists of a set of vertices and a set of arcs (ordered pairs of vertices) EDGES A set of two vertices or an ordered pair, in the case of a directed graph An edge (a set of two elements) is drawn as a line connecting two vertices, called endpoints or (less often) end-vertices An edge with end-vertices x and y is denoted by xy (without any symbol in between) The edge set of G is usually denoted by E(G), or E when there is no danger of confusion. The size of a graph is the number of its edges, i.e. |E(G)|
- 21. TYPES OF NETWORK ANALYSIS METRICS AGGREGATE NETWORK METRICS A number of metrics describe entire networks In some cases, a single network is broken into several disconnected pieces, called components Example: Network density can be used to systematically compare communities, helping analysts decide which communities are highly connected and which are sparse VERTEX SPECIFIC NETWORK METRICS Identifies individuals’ positions within a network Paramount among these is the set of centrality measures, which describe how a particular vertex can be said to be in the “middle” of a network. It emerges from the concept that A person with fewer connections might have more “important” connections than someone with more connections The following centrality metrics provide quantifiable measures for these concepts: ◦ Degree Centrality ◦ Betweenness Centralities: Bridge Scores for Boundary Spanner ◦ Closeness Centrality: Distance Scores for Broadly Connected People ◦ Eigenvector Centrality: Influence Scores for Strategically Connected People
- 22. Degree Centrality ◦ Degree centrality is a simple count of the total number of connections/edges linked to a vertex ◦ It can be thought of as a kind of popularity measure, but a crude one that does not recognize a difference between quantity and quality ◦ For directed networks, there are two measures of degree: In-degree and Out-degree Betweenness Centralities: Bridge Scores for Boundary Spanner ◦ The distance between vertices who are not neighbors is measured by the smallest number of neighbor-to-neighbor hops from one to the other ◦ Geo-desic Distance: the shortest path ◦ Example: a broker Closeness Centrality: Distance Scores for Broadly Connected People ◦ capturing the average distance between a vertex and every other vertex in the network Eigenvector Centrality: Influence Scores for Strategically Connected People ◦ allows for connections to have a variable value, so that connecting to some vertices has more benefit than connecting to other ◦ Example: Page Rank Algorithm by Google Search Engine
- 23. HOW ARE THE DATA REPRESENTED? ◦ Matrix ◦ Edge list WHAT ARE THE TYPES OF NETWORK? ◦ Full and Partial Network ◦ Egocentric Network ◦ Unimodal Network ◦ Bimodal or Affiliation Network ◦ Multimodal Network ◦ Multiplex Network
- 24. HOW CAN NODEXL HELP USERS? 1. GRAPH METRICS: a) Insights about a person’s position within the network, helping to identify important or “central” people: analysts and managers can better know who to contact or influence or bring to the table when trying to implement new programs or gain broader understanding b) identify cliques or persistent social roles that show up in many communities 2. CLUSTERING a) can help identify competing or complementary groups, potential allies to form a powerful group, and individuals who can connect you to a new group. 3. NATURE OF THE EGO a) Social Media is a very low-cost advertisement medium provided you know the track record of your ego

No public clipboards found for this slide

×
### Save the most important slides with Clipping

Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.

Be the first to comment