1. CSE 522 – Algorithmic and Economic Aspects of the Internet Instructors: Nicole Immorlica Mohammad Mahdian

2. Today More about advertisement auctions Some open questions

3. Generative models Have Seen  Probabilistic models for power law graphs and small world networks  Network formation games Open Questions  More realistic models for the Internet graph, hierarchical (i.e. hosts and pages)  Combining ideas from both probabilistic models and game theoretic models

4. Search in Social Networks Have Seen  Kleinberg’s decentralized search  Generalizations to arbitrary geographic distributions Open Questions  New Kleinberg paper, FOCS 2005, discusses payment for search in social networks  Generalizations: general graphs, cost for forwarding information, response times

5. Link Analysis Algorithms Have Seen  Link analysis for web graph – PageRank, HITS  Web spam  Axiomatic approaches to PageRank Open questions  Collusion in PageRank: Algorithms for boosting PageRank of a particular node that locally look “normal”

6. Weblogs A social network  Time-stamped links  Frequent updates  RSS feed Open Questions  Generative models  Finding “discussions”  Blogs to predict trends (sales, etc.), finding correlated topics (also in query logs) 12 34 4321 Picture from Kumar et al.

7. Query Logs Income Tax

8. Query Logs Greeting Cards

9. Query Log Analysis Correlation between terms

10. Query Log Analysis Data streams (top gainers & top losers)  Two streams S 1 and S 2 of items from universe U={1,…,n} S 1 : 1 1 1 2 5 4 2 4 2 2 1 2 1 S 2 : 1 5 5 1 4 1 2 5 4 1 5 1 3  Find item i with approximately largest (or smallest) count(i,S 1 )-count(i,S 2 )  One pass, sublinear space  Charikar et al. give 2-pass algorithm

11. Clustering and Communities Have Seen  Spectral clustering (use eigenvectors of adjacency matrix to well-connected components)  Correlation clustering (min cut positive edges and uncut negative edges)  Metric labeling (assign labels to vertices to min assignment plus separation cost) Open Questions  Combine correlation clustering and metric labeling  Clustering in directed graphs  Finding dense bipartite subgraphs in the web (Kumar et al. have heuristic for complete subgraphs which finds small communities)

12. Reputation Systems Have Seen  Equilibrium analysis of a model with one seller and multiple buyers Open Questions  Better game-theoretic analysis of existing systems (multiple sellers, identity fraud, buyer reputation)  Better methods for aggregating feedbacks (taking into account value of transactions; reputation of the person leaving the feedback)

13. Peer-to-peer networks Have Seen  Models (Napster, Gnutella, Chord)  Content storage, search, and dynamics Open Questions  Spread of viruses, honeypots  Incentives for providing content  Self-stabilization

14. Recommendation Systems Have Seen  History-based systems Two models, used assumptions about clustering of topics, small number of clusters, existence of orthogonal dominant user types  Query-based systems Two models, relied on “committees”, also assumed existence of small number of user types Open Questions  Economic incentives for recommendations

15. The End!