From a graph theory perspective, for each person who is 2-degree reachable from person A, we count how many distinct paths (with 2 connecting edges) exist between this person and person A. Rank this list in terms the number of paths and show the top 10 persons that person A should connect with.

We should how we can use Map/Reduce to compute this top-10 connection list for every person. The problem can be stated as: For every person X, we determine a list of person X1, X2 ... X10 which is the top 10 persons that person X has common friends with.

The social network graph is generally very sparse. Here we assume the input records is an adjacency list sorted by name.

```
"ricky" => ["jay", "peter", "phyllis"]
"peter" => ["dave", "jack", "ricky", "susan"]
```

We use two rounds of Map/Reduce job to compute the top-10 listFirst Round MR Job

The purpose of this MR job is to compute the number of distinct path between all pairs of people who is 2 degree separated from each other.

- In Map(), we do a cartesian product for all pairs of friends (since these friends may be connected in 2-dgrees). We also need to eliminate the pairs if they already have a direct connection. Therefore, the The Map() function should also emit pairs of direct connected persons. We need to order the key space such that all keys with the same pair of people with go to the same reducer. On the other hand, we need the pair of direct connection come before the pairs of 2 degree of separations.
- In Reduce(), we know all the key pairs reaching the same reducer will be sorted. So the direct connect pair will come before the 2-degree pairs. So the reducer just need to check if the first pair is a direct connected one and if so skip the rest.

```
Input record ... person -> connection_list
e.g. "ricky" => ["jay", "john", "mitch", "peter"]
also the connection list is sorted by alphabetical order
def map(person, connection_list)
# Compute a cartesian product using nested loops
for each friend1 in connection_list
# Eliminate all 2-degree pairs if they already
# have a one-degree connection
emit([person, friend1, 0])
for each friend2 > friend1 in connection_list
emit([friend1, friend2, 1], 1)
def partition(key)
#use the first two elements of the key to choose a reducer
return super.partition([key[0], key[1]])
def reduce(person_pair, frequency_list)
# Check if this is a new pair
if @current_pair != [person_pair[0], person_pair[1]]
@current_pair = [person_pair[0], person_pair[1]]
# Skip all subsequent pairs if these two person
# already know each other
@skip = true if person_pair[2] == 0
if !skip
path_count = 0
for each count in frequency_list
path_count += count
emit(person_pair, path_count)
Output record ... person_pair => path_count
e.g. ["jay", "john"] => 5
```

Second Round MR Job

The purpose of this MR job is to rank the connections for every person by the number of distinct path between them.

- In Map(), we rearrange the input records so it will be sorted before reaching the reducer
- In Reduce(), all the connections from the person is sorted, we just need to aggregate the top 10 to a list and then write the list out.

```
Input record = Output record of round 1
def map(person_pair, path_count)
emit([person_pair[0], path_count], person_pair[1])
def partition(key)
#use the first element of the key to choose a reducer
return super.partition(key[0])
def reduce(connection_count_pair, candidate_list)
# Check if this is a new person
if @current_person != connection_count_pair[0]
emit(@current_person, @top_ten)
@top_ten = []
@current_person = connection_count_pair[0]
#Pick the top ten candidates to connect with
if @top_ten.size < 10
for each candidate in candidate_list
@top_ten.append([candidate, connection_count_pair[1]])
break if @pick_count > 10
Output record ... person -> candidate_count_list
e.g. "ricky" => [["jay", 5], ["peter", 3] ...]
```

## 4 comments:

You are correct that my design here assumes bi-directional link. Doing twitter-style is pretty straightforward as well.

The basic idea is to do a Map/Reduce join and then count the number of paths.

I write another blog to cover this scenario.

Hello Ricky,

I got stuck at one point in your algorithm.

In the map() for the 1st round, the variable "friend2" is creating some confusion. Does it refer to the friends of friends of the person (ricky) ??

If that's the case then how do we ensure that the map task processing a person say ricky, has all the connection list for all of his friends..

Please help,

Thanks

Hi Rick have you implemented the Map/Reduce version of this algorithm for twitter-style ? Could you explain a little bit ?

Hi Ricky,

Good article which inspires me to implement slightly different implementation using hadoop with single M/R job.

https://www.dbtsai.com/blog/hadoop-mr-to-implement-people-you-might-know-friendship-recommendation/

Any feedback or comment is welcome.

Thanks.

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