1 Efficient Search Ranking in Social Network ACM CIKM2007 Monique V. Vieira, Bruno M. Fonseca, Rodrigo Damazio, Paulo B. Golgher, Davi de Castro Reis, Berthier Ribeiro-Neto Date:2008/10/02 Speaker: Hsu, YuWen Advisor: Dr. Koh, JiaLing

2 Outline Introduction The ranking function Algorithm for computing the ranking Seeds-based Ranking Algorithm for computing seeds-based ranking Results Conclusion

3 Introduction Social networks: interact and share social experiences through the exchange of multimedia objects (text, audio, video) associated with the people themselves and their actions. MySpace ( with over 100 million registered users, Orkut ( with over 40 million, Facebook and Friendster.

4 Introduction (cont.) In a social network with a large number of users, a common operation is to search for people. Randomly selected 750 queries from the Orkut logs, all of them specify just user names. counted the average number of answers per query. exact matches: avg. result per query is 48 partial matches : avg. result per query is 6,034

5 Introduction (cont.) Designing a ranking function which takes as input distances in a friendship graph nodes : users edges : friendship relationship based on a set of pre-selected landmark nodes (called seeds). Our results show that effective ranking can be attained, while keeping query processing time small enough to be practical.

6 The ranking function Figure 1: Friendship graph: John is at distance 1 of ‘ Maria A ’, at distance 2 of ‘ Maria B ’, and at distance 3 of ‘ Maria C ’. We argue that any search ranking function should favor first ‘Maria A’ and second ‘Maria B’.

7 The ranking function (cont.) This reasoning leads us to propose the following simple ranking function: :the shortest path between John and Maria :the rank of the user Maria,with the regard to the query ‘Maria’ posed by John

8 Algorithms for computing the ranking * Pre-compute all distances between any pair of users and indexing them do not work : 40 million users be stored is in the order [ ], which is too large. if each user has 100 friends on average, then the average number of friends at a distance 3 or smaller of a given user is, resulting in a still very large index size in [ ] pre-computing all friendship pairs presents no scalability. The number of friendship distances that needs to be stored becomes overwhelming.

9 Algorithms for computing the ranking * On-the-fly Ranking calculating all the distances on-the-fly at scoring time. simply run a breadth first search for Maria starting from John avg. 100 friends per user, need to expand more than users if we limit ourselves to people at most 3 hops from John superior alternative run a bidirected breadth-first search, looking for intersections in the list of users reached in the new level.

10 Algorithms for computing the ranking * Co-friends Ranking mix of the first two: index their list of friends and at scoring time intersect both lists. advantage: fast, little space disadvantage: only captures friends or friends-of-friends relations. pre-compute a co-friends list and intersect it with the list of friends of the user submitting the query. friendship distances up to 3  the space and time requirements are pretty high  unfeasible to be used cost effectively in a huge Web live service

11 Seeds-based Ranking key purpose: to produce results of high precision but that can be computed efficiently based on estimates, approximations of shortest paths and has a more complex composition

12 Seeds-based Ranking *Seed Distances: Approximating Shortest Paths run a breadth first search reaching out to all nodes in the network annotate it with its distance to the seed started the breadth first search from a vector of distances to seeds is associated with each node of the graph These vectors of seed distances are computed offline.

13 Seeds-based Ranking *Seed Distances: Approximating Shortest Paths Figure 2: Friendship graph with three pre-selected seeds: S1, S2, S3. S1 S3 S2 Seed distances vectors for users D John = [2, 1, 1] D Maria A = [1, 1, 2] D Maria B = [4, 3, 1] D Maria C = [1, 2, 4] The higher the number of seeds, the better the approximation tends to be for a higher number of nodes.

14 Seeds-based Ranking *Seed-based Ranking (cont.)

15 Seeds-based Ranking *Seed-based Ranking : the number of seeds whose sum of distances to both John and Maria is exactly i. :the number of seeds with a finite distance to Maria and is used as a normalization factor. : the rank of the answer ‘Maria’ with regard to the query posed by John.

16 Seed distances vectors for users DJohn = [2, 1, 1] DMaria A = [1, 1, 2] DMaria B = [4, 3, 1] DMaria C = [1, 2, 4] ‘Maria A’ > ‘Maria B’ > ‘Maria C’ S1 S3 S

17 Algorithm for computing seeds-based ranks *Sparse Seed Distances Vectors

18 If a seed distance is higher than 3, we consider it to be infinite. these vectors become more sparse. a large number of seed distances is set to infinite no need to be stored in the seed distances vectors fast computation and reduces query processing time producing high precision rankings D John = [2, 1, 1] D Maria A = [1, 1, 2] D Maria B = [∞, ∞, 1] D Maria C = [1, 2, ∞]

19 Algorithm for computing seeds-based ranks *Map-reduce Computation of Seed Distances

20 Algorithm for computing seeds-based ranks *computing seeds-based ranks match user names to the query and retrieve those that partially match the query terms. generate the seeds-based ranks.

21 Result * Precision Results compare Ranking Precision(crP) the ideal answer set : the set of documents most likely to be relevant to the users On-the-fly Ranking algorithm as the ideal answer set and compute the metric for the Seeds- based Ranking algorithm, we get a precision value of 90%.

22 Definition of the ideal answer set: : the set of top 10 users returned by the On-the-fly Ranking algorithm : the 10 th answer in the result set : the distance of to the user who posed the query A : the set of all answers : the i th answer in the result set : For any, the distance between and the user who posed the query Define the subset as The ideal answer set of I is define as

23 Since the set I includes no relevance judgements, we call our metric compare- rankings Precision (also cr-precision). refers to the compare-rankings precision at the 10 th position of the ranking.

24 Seeds-based Ranking algorithm 100,000 seeds :avg. cr-precision starts at 71.48% 2,000,000 seeds: avg. cr-precision reaches the 90% range Co-friends Ranking algorithm pretty good avg. cr-precision of 87.02%. summary : high precision can be attained using ranking functions that require much less computational resources the Seeds-based Ranking is highly competitive in terms of precision.

25 In addition to standard metric, we also present our results based on generalized precision. In the context of generalized precision, the relevance decision is not binary. Instead, weights are assigned to the answers as follows.

26 Definition: generalize compare-rankings precision ( gerP) : On-the-flying Ranking : the alternative ranking we want to evaluate, :the i th answers in the respective rankings, :the friendship distances from and

27 Seeds-based Ranking algorithm 100,000 seeds :avg. cr-precision starts at 60% 2,000,000 seeds: avg. cr-precision reaches the 83-85% range Co-friends Ranking algorithm avg. precision: 80-82% range. summary : high precision is attained by ranking algorithms that require less computational resources the Seeds-based Ranking algorithm produces the best results, when 2,000,000 seeds or more are used.

28 Result * Performance Results Only related to the overhead generated by the ranking of the results summary: For the Co-friends Ranking and the On-the-fly Ranking algorithms, average query execution times are more magnitude higher than those of the Seeds-based Ranking algorithm using 100,000 seeds.

29 While there is no need for an extremely fast procedure, as it is carried out offline, this still needs to be fast enough to be usable in a live Web system 2,000,000 seeds of distances can be computed in basically 12 minutes (725seconds)

30 Result * Space Results On-the-fly Ranking Space Requirements This graph takes roughly 16GB of space. the number of machines in the cluster, N = 128. the space required is actually 128 * 16GB= 2,048GB. Co-friends Ranking Space Requirements user id :4 bytes integer. the list of friend-of-friends :40 million users each user has avg. 90 friends the space requirements are roughly 40M * 90* 90 * 4 = 1,260GB.

31 Result * Analysis Of the Results

32 Conclusion Social Networks are a new and important trend in the Web how to add this new signal in a distributed architecture, and how the strategy we have developed for that outperforms naive approaches, both in terms of precision and performance we moved from a text based ranking, almost meaningless to users, to a ranking where one can easily recognize the people in the search results.