Link Analysis

Increasing use of blogs and forums for comments and reviews of products has driven companies to analyze them carefully. Users increasingly inquire the Web and are influenced by opinions and suggestions of other users.

Moreover, companies should not only focus on the users’ network but also relationship between websites and on the importance of every forum within the “blogosphere”. In fact, just like for web-users, we can classify blogs in the following way:

- “opinion leader”, these blogs influence the network

- “follower”, a blog influenced by surrounding network

- well-connected into network

- withdrawn, completely independent from Web

- renowned on Social Networks or having a good Google PageRank

- unknown in the Web

W. Pareto, through 80/20 rule, affirms that analyzing 20% of most important sources, it is possible to know 80% of the phenomenon.

Link analysis applied to the blogosphere focuses on this theme: it studies connections among websites (of a specific area), analyzing visibility on search engine, social shares and presence of links to/from other websites.

The process evolves in 4 phases.

1) It is necessary to surf the Net, exploring blogs to create a wide sample. The example reported below is on 41 blogs about comments and reviews on motorcycle.

2) Every single blog is then analyzed to provide some useful indicators about site:

- Google PageRank

- Number of links to the site

- Number of domains that link to the site

- Number of shares/likes on Social Network

- List of all links that point to the site

3) All of the above information is useful to understand the importance of each website within the Net. An N x N matrix (41 x 41 in this example) is the created, which describes links between websites, indicating the number of hyperlink of every blog to the others. All these links are put in a matrix M x 2, to list all the M connections among all the nodes of the network.

4) Subsequently, through applications specific for link analysis, it is possible to re-create the blogosphere and personalize it for our own aims.

The Motorcycle blogosphere we analyzed had these features:

- Oriented graphic

- node’s size depending of number of links

- node’s color depending of number of shares/likes on Social Network

- arrows’ size depending of number of links between blogs

Below is the graph of the blogosphere regarding motorcycles.


Obviously it’s possible to enlarge the image and analyze every nodes of the network, focusing on rows, node’s features and position in the network.

Moreover link analysis produces some interesting and useful indicators to understand network connections and dynamics:

- in-degree: number of ingoing arrows

- out-degree: number of outgoing arrows

- betweeness centrality: skill of connection among different nodes (bridge capacity)

- closeness centrality: skill of interaction with other vertices

- eigenvector centrality: indicator of centrality in the network

- pagerank: score of the node in the blogosphere

- clustering coefficient: indicator of the presence of connections among close nodes

- reciprocated vertex pair ratio: ratio between ingoing and outgoing connections

It’s possible to affirm that link analysis is an ex-ante studying of blogs and websites, to understand blog dynamics even better.

This analysis can be very useful because it permits to focusing attention only on relevant websites (“opinion leader”) and to ignoring unknown blogs.

Otherwise another solutions is to introduce weight to posts of any blogs, according on their importance in the network.

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