Hey, good point! You’re right that talking about algorithms without understanding their foundations can lead to confusion. However, while we can’t know the exact formula (Google guards it closely), we can study the patterns in updates like E-E-A-T, link relevancy, and user experience signals.
PageRank is a foundational piece, sure, but these days, it’s more about how different elements work together — from semantic search to Core Web Vitals. What’s your perspective on how Google weighs different factors now?
That was what I always hear from any whitehat SEO exexpret... haha...
GSA SER, Xrummer are nothing for me, they working in circular ways, in fighting engine (???). I just think about send million traffic to your website. That's absolutely work.
Here is an explanation and formula for each of Google's publicly known algorithms:
PageRank
PageRank calculates a ranking score ( PR(A) ) for a page ( A ) based on the number and quality of links pointing to that page. The basic formula for PageRank is:
[ PR(A) = \frac{1 - d}{N} + d \sum_{i=1}^{n} \frac{PR(L_i)}{C(L_i)} ]
Where:
( PR(A) ) = PageRank of page ( A )
( d ) = damping factor (usually 0.85), which represents the likelihood that a user will continue browsing rather than quit
( N ) = total number of pages in the network
( L_i ) = pages that link to ( A )
( PR(L_i) ) = PageRank of page ( L_i )
( C(L_i) ) = number of outbound links from page ( L_i )
( \sum ) = sum over all pages that link to page ( A )
MapReduce
MapReduce works by splitting the data into key-value pairs ( (key, value) ) during the Map stage, then combining the values with the same key during the Reduce stage. Mathematically, MapReduce can be described as:
Map stage:
[ map(k_1, v_1) \rightarrow \langle k_2, v_2 \rangle ]
Reduce stage:
[ reduce(k_2, [v_2]) \rightarrow \langle k_2, v_3 \rangle ]
Where:
( k_1, v_1 ) = input key-value pairs
( k_2, v_2 ) = intermediate key-value pairs generated from the Map process
( [v_2] ) = set of values with the same key
( v_3 ) = final output value after the Reduce process
BERT (Bidirectional Encoder Representations from Transformers)
BERT uses the Transformer architecture which is based on a self-attention mechanism, where each word in a sentence is weighted based on its context. The main formula in the self-attention mechanism is:
[ Attention(Q, K, V) = softmax\left(\frac{QK^T}{\sqrt{d_k}}\right)V ]
Where:
( Q ) = query matrix (representation of the current input)
( K ) = key matrix (representation of all inputs)
( V ) = value matrix (representation of values taken from the input)
( d_k ) = dimension of the key vector (used for normalization)
( softmax ) = activation function that produces a probability distribution
The BERT algorithm is trained with masked language modeling (MLM) and next sentence prediction (NSP) techniques to understand the relationship between words in both directions.
