Free Backlink Tools Encyclopedia

Backlink Tools Encyclopedia will introduce 16 Free Backlink Tools on the internet, these tools will surely help you to optimize your websites and boost your SERPs.
Here we listed the most useful and popular 16 backlink tools:

* Yahoo Site Explorer – This one may be the most popular and well-known outbound links detected and analyzed tool. Yahoo Site Explorer is the quickest tool for discovering site backlinks. It’s free and easy to use, just open yahoo homepage and enter “link:yourdomain.com” like “link:onlinearnings.com” (No quotes) to start.
* Analyze Backlinks – A tools for analyzing your competitors backlinks.
* Backlink Watch – Backlink Watch is a popolar backlinks checking tool. It can detect your backlink Urls in just one step, very convenient.The particular is it will marked out the “nofollow” links.
* Google (link:yoursite.com) – Same with Yahoo Explorer, usually google backlinks result is much less than Yahoo but the results from google are all the one which affected your site more.
* Alexa – The links which display on Alexa backlinks checker are the one which do most help to your site, however, Alexa update is slower.
* Bad Neighborhood Text Link Tool – A tool that check the backlinks stats, both outbound and inbound links.
* SoloSEO Link Search Tool – A particular backlink tool. When you enter a keyword, it will create a series of backlink Footprints, include blogs, directories, forums, etc.
* Webconfs’ Back link Anchor Text Analysis – A tool for analyzing you and your competitors websites backlink anchor text.
* Webconfs’ Back link Builder – Enter your keywords, the tool will help you find out the backlink resources which you need, it judged by words like “Add site”, “Add URL”, “Submit link”, etc.
* Firefox SEO Plugin – Powered by SEObook. It can analyzed the site’s PR, backlink quality and quantity, .gov, .edu, social networking links quantities, and the most important is when a link is “Nofollow”, it will be marked out as RED.
* Google Webmaster Tools
* Link Harvester – Another tool powered by SEObook.
* Search Combination – A tool for finding backlink resources. similar with Webconfs’ Back link Builder
* Recip Links – Friend links checking tool.
* Link Appeal Tool – Friend links checking tool.

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Algorithms And Data Structures

Alright, so I've decided that I'll try to start writing up some lessons on Algorithms and Data Structures to help with my studies. Hopefully those of you who are new to programming or would like to learn more about algorithms and performance can learn from these.

I'll start by saying that in order to understand what's going on you'll have to have a decent understanding of how to write code (arrays, primitive data types, etc.). Anyway, lets get to business...

Now you may be wondering, why should I study algorithms and performance?

Well you see, performance is often what draws the line between what is feasible and what is impossible.
Algorithms help us understand scalability
And Algorithmic mathematics provide us a language for talking about program behavior

Now that that little introduction is out of the way... Lets discuss Sorting.

What is sorting?

Basically you take an input, a sequence of numbers a1,a2,...,an , and output a permutation such that the first number in the sequence is less than the next.

Example:
Input: 8 2 4 9 3 6
Output: 2 3 4 6 8 9

Lets examine Insertion Sort with the following pseudocode



Code:
Insertion-Sort(A,n)
for j = 2 to n
do key = A[j]
i = j-1
while i > 0 and A[i] > key
do A[i+1] = A[i]
i = i-1
A[i+1] = key

That was simple, but how does it work?

Here's how:
Code:
Step 1: 8 2 4 9 3 6 (Insert 2 and front shift rest right)
Step 2: 2 8 4 9 3 6 (Insert 4 after 2 and shift rest right)
Step 3: 2 4 8 9 3 6 (No swap)
Step 4: 2 4 8 9 3 6 (Insert 3 after 2 and shift rest right)
etc.
Final Step: 2 3 4 6 8 9 (Success)


Okay that's cool, it works, lets examine the running time.

Running time depends on the input. Obviously, an already sorted sequence is easier to sort.

Short sequences are easier to sort than long ones

We need to try and find the upper bound on the running time to guarantee our performance.

Generally we analyze only worst-case or average-case, best case is never used.

So what is insertion sorts worst-case time? In general we ignore machine-dependent constants. Otherwise it's impossible to compare algorithms.

We try to analyze the growth as the size approaches infinity. This is called Asymptotic analysis.

From an engineering standpoint, when analyzing time complexities we drop low-order terms and ignore leading constants.

Eg. 3n^3 + 90n^2 - 5n + 6054 = O(n^3)

When n is large enough an O(n^2) algorithm will ALWAYS beat an O(n^3) algorithm.

Going back to insertion sort.

In the worst case, where input is reverse sorted, the time complexity is of O(n^2).
This holds to be the same for the average case as well (dropping low order terms, leading constants)

So now you should be wondering... Is this fast?

For small n (small sequences), moderately so
For large n, definitely not.

Okay that's great, but if that's not fast for large sequences what do I use instead?

Okay, well lets check out Merge Sort.

Code:
T(n) |MERGE-SORT A[1 . . n]
O(1) | 1. If n = 1, done.
2T(n/2)| 2. Recursively sort A[ 1 . . ⎡n/2⎤ ] and A[ ⎡n/2⎤+1 . . n ] .
O(n) | 3. “Merge” the 2 sorted lists.


I wont go into details as to how this works right now. But this picture should basically describe how it works.


This is linear time. How many times does it do this? Well as you can see it recursively splits the array into two smaller arrays of half the size.

Basically what we have to do is solve:
Code:
T(n) = 2T(n/2) + cn, where c > 0 is constant
T(n) = 2(T(n/4) + cn/2) + cn ....
T(n) = 2(2(T(n/8) + cn/4) + cn/2) + cn
...

We end up with a recursion tree that looks like this



What we get is that merge sort as an upper bound of O(nlg(n)).


Is this good? Well yes. As n gets large nlg(n) grows more slowly than n^2

Asymptotically merge sort beats insertion sort on the worst case.

In general merge sort beats insertion sort for n > 30~ or so.













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Meta tags

The Meta Title tag is used to declare the title of the page. The Meta Title tag would normally have the same title
as contained in the tag.

An example of the Meta Title tag:



Recommendation for the Meta Title tag: It is recommended to use the Meta Title tag with caution. According to
testing, Yahoo! and MSN index the Meta Title tag, but its effect will depend on the algorithm which is inconsistent.
Careful planning of the meta title can boost page rankings.

Meta Description

The Meta Description tag gives a description of the document. The Meta Description tag is a short, plain language description of the document, usually consisting of 20-25 words or less. Search engines that support the Meta Description tag will use the information to publish on their search results page, normally displaying below the Title of your site listing.

An example of a Meta Description tag:



Recommendations of the Meta Description tag: It is recommended to always use the Meta Description tag. Make
your Meta Description as compelling as you can, as your description often is the difference between getting your
listings clicked in the search results. The Meta Description tag is particularly important if your document has very
little text, is a frameset, or has extensive scripts at the top.

Meta Keywords

The Meta Keywords is used to list keywords that define the content of your site. Keywords are used by search
engines to properly index your site in addition to words from the title, document body, and other areas. The
Meta Keywords tag is typically used for synonyms and alternates of title words.

An example of the Meta Keywords tag:



Recommendations for the Meta Keywords tag: It is recommended to use the Meta Keywords tag with caution.
Make sure to only use keywords that are relevant to your site. Search engines are known to penalize or blacklist
your site for abuse.

Which search engines like what in the Meta Info

If we closely look at the main search engines, like Google, Yahoo, Bing, MSN, Ask, AOL, you will discover that they do not all react the same to the meta information. What Google likes doesn't necessary mean Yahoo would like and visa versa. We have ways to please all the engines with our meta tag information strategy.



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