Mini$-$Case Google$$s PageRank $(3-4$ Pages With Abstrac$)$

Google$$s PageRank is an algorithm that attempts to inform you where people are coming from when they land on your website and which sources are the most frequent. Note that, as its name suggests, a page rank is an index estimated page by page; it$$s not an overall website assessment. Think of the Internet as a big network $($duh$),$ where the links aren$$t all equal. Just as with people, some of your connections are $$more important$($in achieving whatever goals$)$ than others. With clickstreams, if you$$re getting a lot of web traffic, is it coming from somewhere impressive like Amazon or from Joe$$s$-$Shoes$-$and$-$Tshirts.com? The algorithm weights the more important $($more connected$)$ links more than the less important $($less connected$)$ links.

Say you$$re trying to determine the rank of your home page. You figure that$$s a good start, and customers can navigate more precisely once they$$re in your domain. The ranking model begins by checking all the incoming links to the home page over some given duration $($say$,$ the last $24$ hours or the last week, depending on the site traffic and how current the information must be$).$ Customers can land on the home page starting from many links, and the links generating traffic to you differ in their importance. In particular, the influence of the incoming pages varies, as weighted by two factors:

the page rank of the source link $($higher is better$)$ and

how many outbound links that source page contains $($a lower number is better, in that the link to your home page is therefore more selective$).$

Thus, if page A contains a link to your home page, and it has a high page rank of its own and relatively few out$-$reaching links, it carries more weight than page B with its lower page rank and more outreach links.

How would you describe this algorithm in network terms?

What would you do to enhance the chances that some content that you post will $$go viral$?$