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3 Phases of Social Media Network Success for Marketing

The social media landscape is complex.  Social media network analysis makes it easier to understand and navigate social media.

Using the NodeXL social media network analysis add-in for Excel from the Social Media Research Foundation, I have made a large collection of network visualizations and reports, many of which can be seen in the NodeXL Graph Gallery.

Now that I have seen many social media network maps I observe that marketers are often interested in building “broadcast” network patterns.  This is one of the six basic social media network patterns documented in the recent Pew Research Internet Project report about Mapping Twitter Topic Networks.

There are at least three phases of possible success for a social media marketing effort: phase 1, you get an audience of people who will retweet what you post.  Phase 2, some of your audience gets its own audience for the content they repost from you.  Phase 3, a dense web of relationships emerges, a community of relationships.  This is a desirable phase because it sustains the conversation event when new messages from the brand account are not created.

20141018-Three pahses of social media network success

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Guide: How to perform a social media analysis on a brand account using NodeXL Pro

The shape of the crowd is as important as its size.  Social media monitoring has focused on the volumes of activity in a social media stream.  New approaches go deeper to look at the structure of the crowd as well as its size.  Network insights into social media can drive better results, guiding engagement to the correct people, groups, and topics.  Smart data that leads to smart decisions is more than monitoring mentions of your competitors’ twitter handle or hashtags. Understand and quantify your strategies and tactics by harvesting data and performing network analysis.

Network analysis for brands is no easier than ever!  NodeXL automates the calculations and analysis for you, creating visual reports (network graphs) which identify key players in the social ecosystem surrounding a brand account, topic, hashtag, or term.

Monitor your competitors and your own brands and analyze the data using network methods to reveal:

  • Which brands in your industry hold the highest share of voice on social media.
  • What people are talking about in conversations about your industry and competitors.
  • What your industry is most buzzing about.
  • Which influencers are talking about your brand and competitor’s.
  • Which interactive activities [promotions,quizzes, tweet-to-enter competitions etc] are competitors leveraging to gain brand visibility.

and more.

See where your brand stands in relation to your industry, who the influencers of your niche are, which competitors are succeeding on social, and how they’re doing so.

Share of Voice Bench marking: To benchmark where your brand stands in your vertical in terms of ‘share of voice’ or ‘social market-share’, use NodeXL to examine how often competitors brand names and social handles are mentioned, the sentiment of what’s said, and other insights. This will help measure the success of your social media strategy over time.

Sentiment Analysis for Lead-Generation: Negative competitor-brand sentiment by their own customers may be an precursor to brand abandonment, and should be monitored to elicit possible hot leads via social outreach / engagement.

Identify niche influencers to work with on marketing & social media campaigns:
Brands working with the targed influncers are reporting profit of $6 for every $1 spent [Source:Adweek].

Use NodeXL to identify important actors in the social media networks related to your business – based on their position in the network (as opposed to basing influence on vanity metrics such as follower-count).

Measure your competitors’ success: See if a certain social campaign gave your competitor a big boost in engagement and mentions.

Find pockets of opportunity: What isn’t your competitor doing, or what are they doing that can be optimized for superior results? What are they doing which you could replicate for your own success, or to lean into their market share?

Case Study: Analyzing a Social Media Marketing Tool Brand @Buffer

Buffer is a social media management / scheduling tool boasting 4M+ users worldwide. Buffer operates in a competitive market with many competitors (eg Postplanner.com, Sendible.com, Hootsuite.com, Sproutsocial.com to name but a few).

Let’s analyse Buffer’s content + interactions to gather some data about what works for them in terms of building engagement with their target demographic, and see if we can isolate specific marketing campaigns / strategies they are employing, and if so we will try to quantify outcomes of @buffer’s social actions and interactions.

Step 1: Import brand related interaction data from Twitter

NodeXL has importers to harvest data from multiple sources, including Twitter, Facebook and YouTube. The Twitter importer enables you to specify as search query (as you would in Twitter’s own advanced search), and returns the data to NodeXL for analysis.

Let’s plug in @Buffer as the Twitter search term and gather data.

importing @buffer twitter data to nodexl - step 1
importing @buffer twitter data to nodexl - step 2

After calculating graph metrics, grouping by cluster and clicking on ‘Show graph’ in NodeXL we end up with the following [network] model of the social ecosystem related to the @Buffer account. The lines (or ‘edges’) represent interactions (Tweet, Retweet, Mention) and the images represent Twitter accounts (or ‘nodes’ in network-science parlance).

The graph represents a network of 1,938 Twitter users whose recent tweets contained

The graph represents a network of 1,938 Twitter users whose recent tweets contained “@buffer”, or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Tuesday, 30 May 2017 at 00:05 UTC.

Content Analysis & Influencer Analysis

Looking at the report we can see a number of data summaries + metrics, such as most shared content per group, top domains, top hashtags in the whole graph, and per group, top influencers (in terms of betweenness centrality) and more.

View graph & Report for this graph on the NodeXL Graph Gallery Website

Campaign Identification

Looking at the Tweet data in NodeXL and segmenting based on hashtag, we can different hashtags attributed to various different campaigns and tactics. For example, Tweets with the hashtag #socialsmarter can be attributed to an ‘education campaign’. Notice the #bufferchat hashtag is in the top 10 hashtags in the whole sociogram.

Campaign Analysis – #Bufferchat

#Bufferchat is a Weekly ‘Twitter Chat’.

A Twitter chat is a public Twitter conversation around one unique hashtag. This hashtag allows you to follow the discussion and participate in it. Twitter chats are usually recurring and on specific topics to regularly connect people with these interests. Buffer runs a weekly Twitter chat for marketers, using the branded hashtag #bufferchat.

#bufferchat is focused around a different marketing related topic weekly, takes a Q&A format (to stimulate interaction + visiblity) and usually has a different expert guest each week with some specialism in the current week’s topic.

Here’s a Tweet announcing the content of one such Twitter Chat.

Using NodeXL to create a sociogram which models the #bufferchat interactions (via a Twitter import for keyword ‘#bufferchat’) we end up with the following interaction graph:

Bufferchat analysis using NodeXL Pro

The graph represents a network of 357 Twitter users whose recent tweets contained “#bufferchat”, or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Monday, 29 May 2017 at 23:28 UTC.

The tweets in the network were tweeted over the 8-day, 19-hour, 37-minute period from Sunday, 21 May 2017 at 00:01 UTC to Monday, 29 May 2017 at 19:38 UTC. Click here view the full analysis report

Looking at the overall graph metrics we can see that during the course of this one hour Twitter Chat, 372 Accounts (vertices) interacted with the @buffer brand and with a number of online marketing micro-comminuities, resulting in 702 unique interactions (Unique Edges).

Looking at top URLs in the graph, it comes as no surprise that Buffer’s own app website ranks in the top 3

Top URLs in Tweet in Entire Graph:

  1. https://www.pscp.tv/w/a_RR5zgyMjg1NjV8MW1yR21nQWVXbnF4eVlGnYpXpaSBVmtvkac-5os9kRK-kvPp6f4tDuuF08_d
  2. http://womenspowerbook.org/contents-present-civilization-mankind-christian-book-revolutions.htm#.UF9ON1K3wwI
  3. https://blog.bufferapp.com/working-media-bufferchat-recap

Top influencers in the graph based on Betweenness Centrality

Campaign-Specific Brand Reach

Potential reach (sum of followers of all interactions) for this weekly twitter chat is 1,131,164 Twitter users [once we filter the vertices sheet to contain only Twitter accounts which performed a Tweet or @mention of #bufferchat or @buffer]


Case Study: Analyzing an iGaming Brand: @888Poker

Since Austrian bookmaker Intertops accepted the first ever online bet in 1996, the multi-billion dollar online gaming industry has boomed and become exceptionally competitive.

Let’s do a quick analysis of one of the top global poker brands – @888Poker, on Twitter.

Step 1: Import brand related interaction data from Twitter

Use NodeXL’s Twitter Network Importer to import Tweets containing ‘@888Poker’.

Step 2: Automatic Graph Metric Calculations

Use NodeXL to automatically calculate graph metrics, to group nodes by cluster. Then Set a suitablegraph layout before rendering the graph:

888 poker social network analysis
(‘@888Poker’ SNA Map for Monday, 29 May 2017 at 23:29 UTC)

Step 3: Drill into the Data.

The Full report tells us top content, top influencers, top domains, top hashtags, top hashtags per group and a lot more .

The large brand cluster (Group 1) contains lots of interactions which include the hashtag #888Series.

Campaign Identification

Inpection of the Tweet data containing this hashtag in NodeXL elicits the existence of a promotion by 888Poker which runs monthly and which involves the participants tweeting a predefined text which mentions the brand itself and niche related hashtags.

Step 4: Understand the Campaign Mechanics

Users must tweet the following to gain entry to a free poker tournament with cash prizes (‘Tweet for your seat‘):

My @888poker username is [X]. I want to play in the $888 Twitter Freeroll on [somedate] #888series #888poker #poker.

an entry into the competition

Filtering the data to only contain interactions related to this acquisition campaign specifically – we are left with interactions with 1100 Twitter accounts.

888 Poker Social Campaign Analysis
7 days of Tweets – 888 ‘Tweet for a Seat’ Campaign.

Step 5: Campaign Results: Estimate and Guesstimate.

One can estimate potential reach for the campaign (based only on the sum of followercounts of those engaged in the promotion) at 113,000 Twitter user impressions. This does not hoever account for impressions and clicks eaned via content discovery (when users on social network click on a hashtag like ‘poker’) which may be high.

‘Tweet for a Seat’ Campaign Summary:

With social network analysis we learned that 888 Poker turns their customers into advertising beacons, month after month, and that a steady flow of participation of their campaign ensures consistent brand visibility (via hashtag-enabled social discovery & via engagement) and social share of voice for a key term related to their business – ‘Poker’ – at a cost of $888 per month.

 

 

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Content Analysis & Ideation with NodeXL Pro

Social media networks tend to be “clumpy”. Here is the map of connections among people who tweeted the term “global warming”:

NodeXL-Twitter-Global Warming Labeled Groups Network

NodeXLPRO supports text and popularity analysis of content collected from social media data sources, enabling users to quickly find out what the top performing urls, topics and hashtags are for a specific search term or hashtag.

Clustering, Partitioning & Grouping

NodeXL applies social network clustering and then analyzes text that is grouped by social clusters. Connections among people who tweet about a topic, keyword or hashtag form patterns that can lead to the formation of sub-groups and clusters.  Multiple clusters are formed within a network when a sub-population of people link to one another far more than to people in other groups.

These regions of dense connections define the boundaries between sub-populations. Clusters often reflect the variation in interest in certain people and topics in the population. Some people and topics are more interesting to one group than others. Within these groups certain people and words get repeated more often than others.

Networks can be partitioned by many methods. A collection of vertices can be grouped by the user by applying labels to the vertex worksheet (“Group by vertex attribute”). Or a group of vertices can be determined by an algorithm that looks for differences in the density of connections and divides by the points of least association (“Group by cluster algorithm”).

Networks can also be grouped into separate isolated collections of nodes, called “connected components”.

Visualization

In NodeXL groups can be visualized in multiple ways. Groups can be collapsed into meta-vertices that stand-in for the members of that group (right-click the graph pane and select “Groups>Collapse all groups”). Group members can also be displayed within a “box” with the “group-in-a-box” feature (found in the layout selection menu in the Graph Pane – select “Layout Options”).

Within each group is a population of people along with the tweets they authored in the time period captured by the data set. Each group has a collection of tweets that can be analyzed. The contents of all the tweets in a network can be scanned and certain types of strings can be counted to measure its frequency of mention. These counts can be repeated for each group, allowing groups to be contrasted based on the relative rates strings like URLs, hashtags, and @usernames. Here is a sample of the worksheet NodeXL creates to display all the data about people, URLs, and hashtags frequently mentioned in each group:

Example: Twitter search network for “Global Warming”

NodeXL Twitter Search netowrk for 'global warming'

The worksheets displays top URLs, hashtags, and users across the entire network, and within each sub-group. The details offer insights into the people and topics of greatest interest.
For marketers: This data can be especially useful for planning media buys on websites which get highly targeted traffic.
For content marketers / content managers: this data can serve as content inspiration and a topic barometer.

Top Hashtags in Tweets in G7 G7 Count
globalwarming 24
climate 14
climatechange 10
environment 9
agw 6
books 6
glennbeck 6
rushlimbaugh 6
wildlife 5
science 5
Top Hashtags in Tweets in G5 G5 Count
tcot 13
teaparty 4
oil 4
globalwarming 4
p2 2
wrp 2
yyc 2
blameman 1
libtards 1
climatechange 1
Top Hashtags in Tweets in G4 G4 Count
ff 2
globalwarming 2
jokeswritethemselves 1
silverlining 1
ulooklikechazbonoonroids 1
jclogic 1
climatechange 1

Top URLs in Tweet, in Entire Graph Entire Graph Count
http://LiveScience.com 16
http://bit.ly/IdTUlC 14
http://ow.ly/apxEv 10
http://is.gd/ZSXuVT 10
http://stevengoddard.wordpress.com/2012/04/21/arctic-ice-area-approaching-abnormally-high-range/ 9
http://bit.ly/IbMs8o 9
http://www.financialpost.com/m/wp/fp-comment/blog.html?b=opinion.financialpost.com/2012/04/20/aristotles-climate 8
http://bit.ly/JwlWYw 8
http://yhoo.it/JdLq0Q 7
http://usat.ly/JdNKFh 7

This feature allows the content in sub-groups to be contrasted, thus answering the question: how is this sub-group the same or different from another sub-group?

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How to set up automated social media listening with NodeXL PRO

NodeXL PRO ships with a highly configurable scheduler which enables users to access NodeXL functionality (such as influencer analysis, content analysis, brand monitoring, sentiment analysis and leads identification) on demand, automatically.

NodeXL has a number of data importers that can create a network of connections from social media data sources like Twitter, YouTube, flickr, email, and the WWW (along with a number of other data import formats like GraphML, UCINet, CSV, and other Excel workbooks with data).

To create a network you just select the search terms and configurations you want from the NodeXL>Data>Import menu.

If you want to create the same network every day (or at any schedule), a recent feature (since version .125) of NodeXL can help. NodeXLNetworkServer.exe is an application that ships with NodeXL along with a sample configuration file called SampleNetworkConfiguration.xml. By editing the configuration file you can set NodeXL to collect anything available in the menu through Excel.  So far we have exposed the two Twitter data collectors (more on the way) so the configuration file asks you to select a search term or a user’s name, the size of the network and the details you want reported along with the location and name of the destination file that NodeXL will create.  Answer these questions by editing the config file and save it with a useful name that includes the search term.

Step by step details after the jump:

NodeXL ships with a SampleConfigurationFile.xml that you should copy, rename and edit.

You may want to create a directory to hold this configuration file if you expect that there will be many files.

The configuration file is written in XML but it it is very human readable and editable.  Here are the key elements of the file that depend on you to choose and configure the file appropriately.

NetworkType Specifies the type of network to get.  Must be one of the following values: TwitterSearch or TwitterUser

After setting NetworkType, you must also edit one of the following sections:

TwitterSearchNetworkConfiguration

TwitterUserNetworkConfiguration

TwitterSearchNetworkConfiguration is used only if NetworkType is TwitterSearch.

SearchTerm – What to search for.

WhatToInclude – What to include in the network.  This must be a combination of the following values, separated by commas:

Statuses – Include each person’s status (tweet).

Statistics – Include each person’s statistics.

FollowedEdges – Include an edge for each followed relationship.

RepliesToEdges – Include an edge from person A to person B if person A’s tweet is a reply to person B.

MentionsEdges – Include an edge from person A to person B if person A’s tweet mentions person B.

<WhatToInclude>Statuses,Statistics,FollowedEdges,RepliesToEdges,MentionsEdges</WhatToInclude>

MaximumPeoplePerRequest – The maximum number of people to request for each query, or leave empty for no limit.

NetworkFileFolder – Full path to the folder where the network files should be stored.

NetworkFileFormats – Specifies the file formats to save the network to.  This must be a combination of the following values, separated by commas:

GraphML – Save the network to a GraphML file, which can be imported into a NodeXL workbook.

NodeXLWorkbook – Save the network directly to a NodeXL workbook.  To use this option, the NodeXL Excel Template must be installed on this computer.

<NetworkFileFormats>GraphML,NodeXLWorkbook</NetworkFileFormats>

AutomateNodeXLWorkbook – Specifies whether the NodeXL Excel Template’s automate feature should be run on the workbook.  Must be true or false.  This is used only if NetworkFileFormats (above) includes NodeXLWorkbook.

If true, the automate options you most recently set in the NodeXL Excel Template are used to automate the workbook.  To set the automate options, do the following:

1. Open the NodeXL Excel Template.

2. In the Excel ribbon, Go to NodeXL, Graph, Automate.

Note that the “On this workbook” and “On every NodeXL workbook in this folder” selection in the Automate dialog box is ignored when automating the workbook from the NodeXL Network Server.

<AutomateNodeXLWorkbook>true</AutomateNodeXLWorkbook>

TwitterUserNetworkConfiguration

This section is used only if NetworkType is TwitterUser.

ScreenNameToAnalyze – The screen name of the Twitter user whose network should be analyzed.

WhatToInclude – What to include in the network.  This must be a combination of the following values, separated by commas:

FollowedVertices – Include a vertex for each person followed by the user.

FollowerVertices – Include a vertex for each person following the user.

LatestStatuses – Include each person’s latest status (tweet).

FollowedFollowerEdges – Include an edge for each followed relationship if FollowedVertices is specified, and include an edge for each follower relationship if FollowerVertices is specified,

RepliesToEdges – Include an edge from person A to person B if person A’s latest tweet is a reply to person B.

MentionsEdges – Include an edge from person A to person B if person A’s latest tweet mentions person B.

<WhatToInclude>FollowedVertices,FollowerVertices,LatestStatuses,FollowedFollowerEdges,RepliesToEdges,MentionsEdges</WhatToInclude>

NetworkLevel – Network level to include.  Must be One, OnePointFive, or Two.

MaximumPeoplePerRequest – The maximum number of people to request for each query, or leave empty for no limit.

NetworkFileFolder – Full path to the folder where the network files should be stored.

NetworkFileFormats -Specifies the file formats to save the network to.  This must be a combination of the following values, separated by commas:

GraphML -Save the network to a GraphML file, which can be imported into a NodeXL workbook.

NodeXLWorkbook – Save the network directly to a NodeXL workbook.  To use this option, the NodeXL Excel Template must be installed on this computer.

AutomateNodeXLWorkbook

Specifies whether the NodeXL Excel Template’s automate feature should be run on the workbook.  Must be true or false.  This is used only if NetworkFileFormats (above) includes NodeXLWorkbook. If true, the automate options you most recently set in the NodeXL Excel Template are used to automate the workbook.  To set the automate options, do the following:

1. Open the NodeXL Excel Template.

2. In the Excel ribbon, Go to NodeXL, Graph, Automate.

Note that the “On this workbook” and “On every NodeXL workbook in this folder” selection in the Automate dialog box is ignored when automating the workbook from the NodeXL Network Server.

One key configuration is this last choice to turn on the automated processing of the resulting data set.  If you set this to true, NodeXL constructs a Twitter social network and then performs all of the steps of automation that you define.  For example, NodeXL can calculate graph metrics, find clusters, create subgraphs, map a set of autofill column mappings of data to display attributes, set graph layouts and settings and render a graph without any human intervention.

Once you have a properly edited configuration file you can simply open a command line session (go to the Start menu, type in “CMD”) and type the following:

> NodeXLNetworkServer.exe SampleNetworkConfiguration.xml

And you will get a stream of messages about what parts of the network are being actively collected:

This is a simple example and not too useful: you may as well just do this through the NodeXL Excel interface.  But things get more interesting when one more piece is added: Windows Task Scheduler.  You may not see it that often but Task Scheduler is on almost every Windows desktop and can be used to automate the collection of NodeXL data sets from Twitter and other sources of social media networks.

To get to Task Scheduler just type its name into the Start Menu search box.  It should look like this:

Using this tool you can create new Tasks that will execute on a specific time and frequency.

Create and select a new folder (call it something like “NodeXL Data Collections”) under Task Scheduler Library to hold all your NodeXL data collections separate from other scheduled tasks.

Use the Create Task menu item on the far right.

You will get a Create a task dialog box:

Enter a Name and a Description that captures the search terms of your query and then select the “Actions” tab.

Actions are where the command that you want to execute is defined.  Select “New…” to create a new Action.

This will open a dialog box in which you can define the program to be run along with any settings.

Enter the complete path to the NodeXLNetworkServer.exe application in the “Program/script” field.

Add the complete path to the configuration file in the “Add arguments” field.

To easily capture the complete path (and the quotation marks needed if there are spaces in the path) hold the SHIFT key down while right clicking the name of the configuration file you want to schedule for collection.  You should see an option “Copy as Path” which will place the needed information into the clipboard.  After selecting “Copy as Path”, return to the “New Action” dialog box and paste the path into the “Add arguments” field.

Once the path to the NodeXLNetworkServer.exe file and the path to the configuration file have been entered into the New Action dialog, shift to the Triggers tab of the Create Task dialog.  Select “New…” to create a new trigger.

  /></p>
<p>Triggers are defined by many things, but we will focus on time.  When the New Trigger dialog is set to “On a schedule” you can choose the time and frequency to run the collection.  If you want to run a collection daily at 7:01:30AM each day the following settings should work:</p>
<p><img class=

Once the time and recurrence are set select OK and you have a new task!  You may want to create a task that starts in a minute or two to test that the event fires properly.  You can then schedule these collections to run at an hour when you will not be bothered by the interruption.  Multiple collections can be scheduled but several limits suggest that only a few can run simultaneously.  You may want to start collections only a few times a day or hour to allow one collection to complete before others begin.  When these tasks do execute you should see a collection session appear in a console window and report updates as it steps through the many stages of constructing a network dataset.

Collection is much faster if you have a rate limit lifted account, which you must request from Twitter.  With a credentialed rate-limit lifted account you can perform several queries per hour.  With a regular account with credentials (your Twitter login) you can get one or two queries per day depending on the size of the data collected.  In either case it is possible to reach the limit that Twitter will provide.  When that happens NodeXL will pause the collection and wait until the API query budget refreshes and Twitter is willing to serve more query results.  As a result, even accounts without rate-limits lifting can create large complex social media network maps, although at a much slower rate.

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How to measure influence in a [social] network using Social Network Analysis and NodeXL [borrowed from thnklink]

[stolen from think link] – or do we just re-use whatever content we have in the intro to SNA article ?

rewrite + add graphics + examples
can we repurpose existing example data + screenshots from chapter 4 thinklink?

Social network analysis takes a relational approach to examining social phenomena. Social networks’ metrics measure patterns of social connections at three main units of analysis: nodes (vertices), links (edges) and whole networks (graphs). Metrics, then, describe each of these units in terms of its connectivity.

VERTEX-LEVEL METRICS

Nodes or vertices are actors (individuals, organizations, etc.) and can take a variety of structural positions in a network. The structural position of vertices defines the role that specific users play in the network. Actors in structural positions are identified by measurements of connectivity. One of the most common sets of measurements are centrality measurements. Centrality refers to how prominently connected an actor is in a network. As the name implies, actors who are more important in a social network are “central.”

Centrality helps to explain the extent to which an individual or organization is connected to others in their network. NodeXL calculates a variety of node-level metrics. In this chapter, a range of centrality measurements will be discussed, followed by a discussion of reciprocity measurements.

Degree centrality

The most common and most intuitive measurement of centrality relies on the number of connections a node has in the network. Degree centrality, then, is calculated as the number of links a node has in the network. A node with a high degree centrality value is sometimes considered as a hub. A student with many (often a disproportionate number of) friends in school, Twitter users with many followers relative to others, or a blog with many links can all be considered hubs. These nodes are central in their networks because they are highly connected. • Open the Kite_Example file (Import -> From NodeXL workbook created by another computer). • Select the Vertices spreadsheet. You can see the names of all the actors in this network. • Click Show Graph on the graph pane (on the right). • Looking at the graph, who would you suggest has the highest degree centrality value? • Calculate degree centrality for all vertices: Analysis -> Graph Metrics -> Deselect All -> Check: Vertex Degree (Undirected Graphs Only). • Who has the highest degree value? Who has the lowest? What are these values?
While attributes of links will be discussed next, at this point it is worth noting that in directed networks, where each link has a direction, two types of degree centrality emerge: in-degree centrality and out-degree centrality.

In-degree centrality is based on ties or connections that others have initiated with a user, and out-degree centrality is based on the relationships one initiated with others. For example, in a network of advice seeking classmates, a student’s in-degree is the number of students who have asked for her advice, while the student’s out-degree would be the number of other students she approached for advice. On Twitter an in-degree would be the number of mentions, replies or followers one has in the network. A user’s out-degree is the number of other users she follows, mentioned or replied to. Note that one has more control over their out-degree than their in-degree, and a node with high in-degree is, therefore, considered a hub in that network. • Change the type of edges to Directed: Graph -> Type -> Directed. • Refresh the graph. Can you see arrowheads indicating the direction of links? • Looking at the graph, who would you suggest has the highest in-degree centrality value? Who has the highest out-degree centrality value? • Calculate degree centrality for all vertices: Analysis -> Graph Metrics -> Deselect All -> Check: Vertex In-Degree (Directed Graphs Only) and Vertex Out-Degree (Directed Graphs Only).
• Who has the highest in-degree value? Who has the lowest? Who has the highest out-degree value? Who has the lowest? What are these values? • Change the graph type back to Undirected.

Betweenness centrality

Betweenness centrality measures the extent to which an actor falls on the shortest path between other pairs of actors in the network. The more people that depend on an actor to make connections with others, the higher the actor’s betweenness centrality value. This value is therefore associated with bridging actors in a network. • Looking at the graph, who would you suggest has the highest betweenness centrality value? • Calculate betweenness centrality and closeness centrality values for all actors. • Who has the highest betweenness centrality value? Who has the lowest?

Closeness centrality

A different approach to measuring centrality is closeness centrality, which measures the average distance between a node and every other node in the network. The higher the values of closeness centrality, the more central a node is in the network. A node with high closeness centrality can connect with others across the network, directly or indirectly, via a small number of other nodes. A node with a low value of closeness centrality can be considered more peripheral in the network, as the reach between the node and most other users is distant. Users with high levels of closeness centrality are at the core of the network. • You should already have the closeness centrality values for your nodes. • Find the actors with the highest and lowest closeness centrality. • Look at the graph and try and make sense of why these actors have these values.

Eigenvector centrality

Eigenvector centrality evaluates a node’s importance in a network, based on their proximity to other important nodes. A score is assigned to each node based on its relative connectivity in the network. An algorithm is then used in conjunction with these these scores to calculate the eigenvector centrality of each node. In many large networks, only a few nodes are highly and disproportionately connected in the network while most are much less connected, if at all. In such networks, most users are unlikely to become one the few high degree users. In these networks, becoming close to important users is particularly valuable. • Calculate eigenvector centrality and identify the actors with the highest and lowest values. • Looking at the graph, can you understand why these actors gained these values?

Reciprocity

A link between two actors in a network is considered reciprocal, or mutual, if each node has initiated a tie with the other actor. Reciprocity is meaningful only in directed networks, as in undirected networks all links are mutual by definition. Reciprocal relationships between individuals may indicate a wide range of social attributes such as cooperation, trust, exchange of opinions and power balance. On social media platforms, users seek to attract attention to their posted content by giving attention to others (retweeting, linking, etc.). Levels of reciprocity can be used to measure the success of such strategies. Reciprocity can be measured at all networks levels, from edges to entire networks, as will be discussed later. At the node level, reciprocity is measured as the number of nodes one is connected with (alters) reciprocally over the total number of alters. Reciprocity values range from 0 (no reciprocated links) to 1 (all links between a node and its alters are reciprocated). • Save and close the Kite_Example file, and open the Kite_Example_Weighted file. • Make sure the graph type is designated as Directed. • Click the Show Graph button. The arrowhead indicates the direction of the links. Looking at the graph, can you identify the reciprocal links? Who do you think has the highest reciprocity value?

• Now, calculate Vertex reciprocated vertex pair ratio in Graph Metrics. Who has the highest reciprocity level? Who has the lowest? What are the values? • Visualize the graph so the vertices’ sizes correspond with their reciprocity values, using Autofill Columns.

EDGE-LEVEL METRICS

The heart of all network analysis is a dyad, a pair of nodes connected via a link or an edge. As network analysis captures patterns of connections, measurements at all levels are defined by patterns of links. Two individuals who are friends with one another constitute a dyad, whereas friendship is a link. Two users on Twitter who follow one another also constitute a dyad, whereas the following relationship is a link. Similarly, dyads and links can be found in two YouTube videos that share a tag, a blog posting a hyperlink to another blog, or two individuals who bought the same book on Amazon.com. A network is created by putting together a group of dyads.

Links are often considered binary: they either exist or are absent. Edge-level measurements, however, are key for understanding any network. The directionality of links was discussed earlier. Three other aspects of links hold key network insights: edge weight, edge type and edge reciprocity.

Edge weight

Relationships in social networks vary in terms of their strength. Consider your own social network; some friendships are stronger than others. For instance, kinship relationships are often considered stronger than work collegiality. In other words, the strength of a relationship can be defined by the researcher. Another way to evaluate the strength of a link is by considering its frequency. A link is considered stronger the more frequently an interaction takes place. For example, the strength of the link between Jane and Jon on a Twitter retweeting network can be calculated by the number of times Jane retweets Jon’s tweets. On Facebook, Rachel commenting on twenty of Jake’s posts constitutes a link weight of 20 connecting Rachel to Jake. James commenting on only one of Julie’s posts, on the other hand, would form a link weight of 1. • Open a new version of the Kite_Example_ Weighted file. • Change the Graph type to Undirected. • Look at the Edge spreadsheet. Some edges appear more than once. Let’s calculate the weight of links, by calculating the frequency of duplicated edges. • Data -> Prepare Data -> Count and merge duplicate edges • In the dialogue box, • Select Count multiple edges and insert the count to an edge weight column. • Select Merge duplicate edges. • Note that the default option is to use Vertex 1 and Vertex 2 to determine duplication, however you would add a third column (for example, edge type) to determine duplicates. For now, let’s stay with the default option. • Click OK. • In the Edge spreadsheet, on the right, a new column was added: Edge weight. • Note: because we set the graph to Undirected, NodeXL counts an edge between two nodes, in either direction, as a duplicate. If the graph was directed, NodeXL would have considered the direction of the edge. • Which edges are the strongest? Which are the weakest? • Visualize the edge weight: using the Autofill columns, set Edge Width to Edge Weight.

Edge reciprocity

A node’s reciprocity, as mentioned earlier, captures the extent to which a node’s ties with its alters are mutual. At the link level, a tie may or may may not be reciprocated. Tom may ask for Ted’s help on his homework, but Ted may ask for Tod’s help rather than Tom’s. If a link is mutual, it’s reciprocity value is 1; otherwise, its value is 0. More advanced measurements of link reciprocity take into consideration the extent to which a dyad is reciprocal. • Download and open a new copy of Kite_Example_ Weighted. Make sure the graph type is Directed. • Calculate Edge reciprocation (in Graph Metrics). • Find the Reciprocated column. Each edge has a value Yes if it is reciprocated, and No if it is not. • Visualize the edge reciprocity: use Autofill columns to set Edge color to Is Reciprocated? • Use the Options arrow next to Edge color, and select Categories for the The source column’s values are. • If you haven’t done it already, click Show graph.

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How To Install NodeXL PRO

NodeXL Pro

First Installation

Please note that the installation of NodeXL requires use of Microsoft Windows™ (XP, Vista, 7, 8, 10) and Microsoft Office™ (2007, 2010, 2013, 2016). You can find further information about system requirements on the FAQ page.

  1. Download the NodeXL Pro Excel Template from the NodeXL Graph Gallery or NodeXL Basic Excel Template from Microsoft´s CodePlex website and then run it.
  2. If you are asked to accept the “Microsoft Visual Studio 2010 Tools for Office Runtime (x86 and x64)”, click the Accept button.
  3. When you are asked “Are you sure you want to install this customization?”, click the “Install” button.
  4. In the Windows Start menu or Start screen, search for “NodeXL”, then click “NodeXL Excel Template” in the search results or look for the desktop shortcut named NodeXL Excel template.

Upgrade from NodeXL Basic to NodeXL Pro

  1. In order to run NodeXL Pro, please uninstall NodeXL Basic. If you don’t do this, NodeXL Pro Excel Template 2014 will install successfully, but you will get an error message when you attempt to run the new version. The error message includes the word “COMException.”
  2. Then download and install the NodeXL Pro Excel Template 2014

Some users have an issue that blocks reinstall of NodeXL Pro after an upgrade from NodeXL Basic. This issue can be resolved – please go to the following path before installing NodeXL Pro:

C:\Users\[user_profile]\AppData\Local\Apps\2.0

You should see two folders: one named Data and another folder with a strange name, that is where the old NodeXL is stored. Please delete the folder with the strange name and try reinstalling NodeXL Pro. You will be able to read any workbooks you have created in older versions of NodeXL Basic.


Upgrade from NodeXL Basic to NodeXL Pro

  1. In order to run NodeXL Pro, please uninstall NodeXL Basic. If you don’t do this, NodeXL Pro Excel Template 2014 will install successfully, but you will get an error message when you attempt to run the new version. The error message includes the word “COMException.”
  2. Then download and install the NodeXL Pro Excel Template 2014

Some users have an issue that blocks reinstall of NodeXL Pro after an upgrade from NodeXL Basic.  This issue can be resolved – please go to the following path before installing NodeXL Pro:

C:\Users\[user_profile]\AppData\Local\Apps\2.0

You should see two folders: one named Data and another folder with a strange name, that is where the old NodeXL is stored.  Please delete the folder with the strange name and try reinstalling NodeXL Pro.  You will be able to read any workbooks you have created in older versions of NodeXL Basic.


Trusted Sites

On some systems (often in large corporations or universities) access to websites needed by NodeXL are blocked. To resolve this issue, these web site addresses can be added to a list of trusted sites. NodeXL needs access to the NodeXLGraphGallery.org web site.

To enable access to this web site, from the Windows Start menu, search for “Internet Options” and then select “Security” and “Trusted Sites”. There is a button below the Trusted Sites menu: “Sites” – select this to add the NodeXLGraphGallery.org web site to the exception list.

The Trusted Sites dialog will allow you to enter the following three web addresses. NOTE: It is necessary to UNCHECK “Require server verification (https:) for all sites in this zone”.

Nodexl Trusted sites settings 1
Nodexl Trusted sites settings 2

License Activation

The activation process for NodeXL Pro takes just a few steps:

  1. From the email we sent you, download the attached file(s) containing your NodeXL Pro license(s) on a secure machine. (The file may currently be in your download directory).  Do not unzip or run this file.  It is not compressed.  It is not a program.
  2. Copy a single one of these NodeXL Pro license files (each of these files have an extension of .lic) to the machine that is going to run a copy of NodeXL Pro.
  3. Paste the license file (.lic) in any location on the local system. You may place this file in any accessible directory. You may want to create a special folder to hold theNodeXL Pro license key file – this can be a good way to ensure the file is not moved or deleted.
  4. Open NodeXL Pro by searching for “NodeXL” from the START menu in Windows. If it is not visible in the Start menu, try Windows Start>All Apps>”N”>NodeXL Excel Template then right-click and select “Pin to Start Menu”.  If NodeXL Pro has not been registered before, or if the NodeXL Pro license file has been moved or damaged, you will be asked to select the file. Use the Browse… option to open a file open dialog and navigate to the location of the license file that was sent to you. Then select OK.
  5. Your copy of NodeXL Pro is now licensed and should be operational! Enjoy!
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