A Survey on Social Network Search Ranking

Web vs. Social NetworksWeb Social Network

Publishing •Place documents on server •Post contents on social network sites

Locating •Via search engine •Navigate through the social network•Browse contents recommended by other users

• Limitations of web (hyperlink-based) search– It underestimates recently published content– It has a bias in favor of large community (e.g.,

Michael Jordan, the basketball player or the computer scientist?)

Roadmap for the following

• PeerSpective 1.0 (HotNet ‘06)– Demonstrate why social network search matters

• Network-Aware Searching (VLDB ‘08)– Query + Importance of user (relative to the query

user)

• Efficient Search Ranking in Social Networks (CIKM ’07)– Propose some challenges of social (network-

aware) searching

PeerSpective 1.0 (HotNet ‘06)

• An experiment uses social nets to search the Web

• Idea: users can query their friends’ viewed pages

• Results from friends appear alongside Google results

• Ranking:

usereach for

core]PageRank_SreLucene_Sco)before page thereaduser ([I

PeerSpective Experimental Results

• Run PeerSpective with 10 users for 1 month– 51,410 distinct URLs viewed– 1,730 Google searches

• Google contains only 62.5% URLs• 30.4% of URLs previously viewed by someone in

network• 13.3% of URLs previously viewed but not in Google• 7.7% of (top 10) result clicks are on PeerSpective-

only results

Network-Aware Searching (VLDB ‘08)

• The query content + Importance of users (relative to the query user)– Overlap-based similarity– Indirectly connected users

– Add a uniform background– Social frequency

• tfu(d,t) is typically 0 or 1

|)'(||)(|

|)'(|2),(

utagsetutagset

uutagsetuuO

1

01

...path

),()( max0

k

iii

uuuuu uuOuP

k

1( ) (1 ) ( )

| |u uF u P uU

( , ) ( ) ( , )u u uu U

sf d t F u tf d t

Network-Aware Searching Example

• O(A,A)=1, O(A,B)=2/4, O(B,C)=2/4, O(C,D)=2/5, O(A,E)=2/4, O(E,D)=0/5

• PA(D)=max(1/10,0)=1/10

• FA(D)=0.1*1/5+(1-0.1)*1/10=0.11

• Similarly, FA(A)=0.92, FA(B)=0.47, FA(C)=0.245, FA(E)=0.47

• sfA(z,a)=0.92*1 + 0.47*1 + 0.245*0 + 0.11*1 + 0.47*0 = 1.5

A

E

D

CB

a,b

a,c c,d

a,d,e

b,c

Tags of document z by user A, B, C, D, and E:

Efficient Search Ranking in Social Networks (CIKM ’07)

• Consider usernames as query terms only• Idea: search ranking is based on the path

length• Challenge: large size of SN prevents efficient

computation of shortest path at query time– Orkut: 40 million– Facebook: more than 200 million active users

Efficient Search Ranking in Social Networks: Approaches

• Pre-compute all distance b2n any pair– Trivial– Non-scalable: 40 million users 40,000,0002=16*1014

• On-the-fly ranking– BFS in real-time– Each user has 100 friends, distance 3 1,000,000 users

• Co-friend ranking– Mixture of above two– Store “friends of friends” for each user and search from

the list

Conclusion

• Network aware search is not a big problem

• However, how to search “in real time”?– Search limited number of hops– Approximated shortest path– Pre-compute (partial) data