Does Google still use PageRank anymore? The search results are so bad these days, I'm dubious on how many other websites reference the top results they show.
PageRank algorithm for graph databases
21–30 of 42 posts
Re: PageRank algorithm for graph databases
#22Earlier quoted context omitted.
Thank you for your use case! I'll take a look at your thesis! :)
The linked paper is not my thesis but one of the foundational works, I based my thesis on. Mine can be found here: https://git.vbrandl.net/vbrandl/masterthesis/raw/branch/mast...
Great stuff, thanks for sharing.
Re: PageRank algorithm for graph databases
#23Re: PageRank algorithm for graph databases
#24Does Google still use PageRank anymore? The search results are so bad these days, I'm dubious on how many other websites reference the top results they show.
There are known link structure metrics besides PageRank that both scale better and are harder to game, though I'm not sure how their effectiveness in ranking compares to PageRank in the un-gamed case.
I used to work on Google web search indexing, almost 20 years ago, and (1) it has been public knowledge since around the time I left that PageRank was just one of a plethora of ranking signals going into the Learn To Rank page ranking ML and (2) I strongly suspect that PageRank itself has been replaced by a somewhat similar reverse link weighting algorithm.
I strongly suspect that something roughly PageRank-like is still there deep in the bowels of ranking, and due to the non-linear nature of ML, its importance probably varies greatly page to page.
Re: PageRank algorithm for graph databases
#25Does Google still use PageRank anymore? The search results are so bad these days, I'm dubious on how many other websites reference the top results they show.
There are known link structure metrics besides PageRank that both scale better and are harder to game, though I'm not sure how their effectiveness in ranking compares to PageRank in the un-gamed case. I used to work on Google web search indexing, almost 20 years ago, and (1) it has been public knowledge since around the time I left that PageRank was just one of a plethora of ranking signals going into the Learn To Ra…
>There are known link structure metrics besides PageRank that both scale better and are harder to game
Any examples?
Re: PageRank algorithm for graph databases
#26BTW, is there a good "graph layer" for SQLite? I understand that graph databases use specific data structures to optimize for graph queries instead of row-oriented but sometimes you need something in the middle: representing graphs and doing basic queries.
I was building something like this a few years ago - NetworkXum [1].
But now we just use a pure Graph implementation with NetworkX interface - UKV [2].
[1]: https://github.com/unum-cloud/networkxum [2]: https://github.com/unum-cloud/ukv
Re: PageRank algorithm for graph databases
#27Wanting to load Javascript from 18 domains sounds like record spam to me. More than on your typical russian movie streaming backdooring attempts
Re: PageRank algorithm for graph databases
#28BTW, is there a good "graph layer" for SQLite? I understand that graph databases use specific data structures to optimize for graph queries instead of row-oriented but sometimes you need something in the middle: representing graphs and doing basic queries.
Cozo (cozodb.org) is a new embedded graph database that has many available storage backends, including SQLite.
It’s not a thin layer, though, if that’s what you’re looking for. Cozo has its own query system and uses a datalog-like query language.
Re: PageRank algorithm for graph databases
#29Does Google still use PageRank anymore? The search results are so bad these days, I'm dubious on how many other websites reference the top results they show.
PageRank was never as important as people thought it was.
Think of it this way: a search engine needs a relevance score that connects a query to a document. If the number of documents is vast (e.g. billions and billions) a search engine also benefits from a document-dependent quality score.
The first is more important than the second. You'd rather get a poor quality document that is relevant to the topic than a high quality document which isn't relevant.
It took several years before papers in the literature came out that found PageRank useful in search results, the key thing is that you need a real excess of documents. With millions of documents you are better off without it (being more effective at finding relevant documents improves performance), you really need 100 million + to reach the point where you have so many relevant documents for typical queries that filtering on quality doesn't get in the way of relevance.
PageRank can be thought of as simulating a Markov process where a user clicks a random link on a page most of the time but with some probability jumps to an entirely random page. PageRank is proportional to the probability that a user visits the page, or alternately how much traffic a page gets.
Google very quickly developed a few ways to sample this directly, such as (1) making Google analytics almost ubiquitous, (2) making Google ads almost ubiquitous, (3) analytics from the Chrome browser.
Google denies using the above for ranking, but they've been known to lie about Google's relevance factors before. Even a small sample from the above 3 could be used to calibrate models based on other info.
Re: PageRank algorithm for graph databases
#30page rank is just one of many centrality measures of graphs.