1. A Survey on Social Network Search Ranking

2. Web vs. Social Networks WebSocial 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?)

3. 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

4. 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:

5. 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

6. 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 tf u (d,t) is typically 0 or 1

7. 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 P A (D)=max(1/10,0)=1/10 F A (D)=0.1*1/5+(1- 0.1)*1/10=0.11 Similarly, F A (A)=0.92, F A (B)=0.47, F A (C)=0.245, F A (E)=0.47 sf A (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:

8. 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

9. Efficient Search Ranking in Social Networks: Approaches Pre-compute all distance b2n any pair – Trivial – Non-scalable: 40 million users  40,000,000 2 =16*10 14 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

10. 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