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    <p>I'm not sure if you're asking how to make suggestions or detect friend distance. Making suggestions is easy, but will tend to explode in size.</p> <p>The first two cases can be covered by the same algorithm, and the third by a small extension.</p> <p>The first two are basically looking for all the people who your known friends mutually know:</p> <pre><code>FriendHash = {} foreach Friend in me.getFriends() foreach FriendOfFriend in Friend.getFriends() FriendHash{FriendOfFriend} += 1 foreach PotentialFriend in keys FriendHash if FriendHash{PotentialFriend} &gt; 1 me.suggestFriend(PotentialFriend) </code></pre> <p>In case 1, the link between friends 1 and 2 could be an additional constraint that would actually make the case a bit more complex to implement. By requiring friends 1 and 2 to have a link, you'd need to detect potential friends while iterating friend pairs, rather than once at the end.</p> <pre><code>foreach Friend in me.getFriends() foreach SecondFriend in me.getFriends() # skip already processed friends and Friend == SecondFriend if Friend.getFriends() contains SecondFriend foreach FriendOfFriend in Friend.getFriends() # skip already suggested friends if SecondFriend.getFriends() contains FriendOfFriend me.suggestFriend(PotentialFriend) </code></pre> <p>There's certainly some optimization that can be added in there that would skip repeated comparisons. In practice this probably isn't a useful search to run anyway. All you're going to do is exclude potential friends who are common to two distinct groups of friends.</p> <p>The last case modifies the first pseudo code segment by extending a friend suggestion to all the friends of your known friends mutual friends:</p> <pre><code>foreach PotentialFriend in keys FriendHash if FriendHash{PotentialFriend} &gt; 1 foreach ExtendedFriend in PotentialFriend.getFriends() me.suggestFriend(ExtendedFriend) </code></pre> <p>As commented by Neil Knight, you could filter each friend list and start by looking at the most active friends first. Or compute a similarity score that promotes those friends who have more friends in common with you.</p> <p>If you're actually looking at detecting the distance between a friend and a suggestion, this probably isn't relevant.</p>
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    1. COwell actually, i am asking both questions you specified. but for an imaginary users table. it means that is also includes table structure. at least i have some clues but hard to decide because of performance issue. is it like, users and friendship table or something else. and thanks for your answer
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