CS651: Internet Algorithms
Nina Mishra

Course Overview

The Internet is arguably one of the most important inventions of the last few decades and we are only at the early stages of understanding what we created.   In this course, we discuss algorithms that have helped us harness and comprehend this large and highly dynamic entity.  The course begins by relating phenomena that we have empirically observed (e.g., small-world, power-law distribution) and also describes our efforts to model its behavior.   We then turn to algorithms for searching the Internet including page rank, hubs and authorities, and more recent topic-sensitive variants.  Algorithms for spreading epidemics and understanding the temporal evolution of web structure and web behavior are then covered.  Next, we analyze algorithms for discovering communities in the web graph.  Finally, the streaming nature of the data produced on the Internet calls for a shift in algorithmic thinking:  Running time is no longer the dominant issue; more important is how do we compute statistics over streams when the stream is too large to store?

 

This course should be of interest to graduate students in computer science and other related disciplines and is especially designed for those with an interest in understanding why algorithms work.  Familiarity with basic material in algorithms and probability at the level of the core undergraduate courses is useful.

 

Time and Location

Office Hours

Grading

Each student is responsible for submitting 1-page critiques for 3 papers related to the material covered in lectures (each 10%).  Grading for the class is primarily based on a class project including a project proposal (10%), progress report (10%), presentation (25%) and final report (25%).

Presentation Schedule

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Overview of Lectures (Tentative)

Reading List

Small-World

• Watts, D. J. and S. H. Strogatz. Collective dynamics of 'small-world' networks. Nature 393:440-42(1998).
• J. Kleinberg. The small-world phenomenon: An algorithmic perspective. Proc. 32nd ACM Symposium on Theory of Computing, 2000. Also appears as Cornell Computer Science Technical Report 99-1776 (October 1999). (In HTML and PDF.)
• J. Kleinberg. Small-World Phenomena and the Dynamics of Information. Advances in Neural Information Processing Systems (NIPS) 14, 2001.
• J. Kleinberg. Complex Networks and Decentralized Search Algorithms. Proceedings of the International Congress of Mathematicians (ICM), 2006 (to appear).

Power-Law Distributions

• M. Mitzenmacher A Brief History of Generative Models for Power Law and Lognormal Distributions. Internet Mathematics, vol 1, No. 2, pp. 226-251, 2004.
• R. Kumar, P. Raghavan, S. Rajagopalan, D. Sivakumar, A. Tomkins, and E. Upfal. Stochastic models for the Web graph. 41th IEEE Symp. on Foundations of Computer Science, 2000, pp. 57-65.
• W. Aiello, F. Chung, L. Lu. Random evolution of massive graphs. Handbook of Massive Data Sets, (Eds. James Abello et al.), Kluwer, 2002, pages 97-122.
• B. Bollobas, C. Borgs, J. Chayes, and O. Riordan Directed scale-free graphs Proceedings of the 14th ACM-SIAM Symposium on Discrete Algorithms (2003), 132-139.
• R. Kleinberg, J. Kleinberg, Isomorphism and Embedding Problems for Infinite Limits of Scale-Free Graphs. Proc. 15th ACM-SIAM Symposium on Discrete Algorithms, 2005.
• A. Fabrikant, E. Koutsoupias, C. Papadimitriou. Heuristically Optimized Trade-offs: A New Paradigm for Power Laws in the Internet. 29th International Colloquium on Automata, Languages, and Programming (ICALP), 2002.
• Noam Berger, Christian Borgs, Jennifer Chayes, Raissa D'Souza, and Robert Kleinberg. Competition-Induced Preferential Attachment. To appear in Combinatorics, Probability, and Computing. Extended abstract appeared in Proceedings of the 31st International Colloquium on Automata, Languages, and Programming (ICALP 2004), pages 208-221.
• M. Molloy and B. Reed. A Critical Point for Random Graphs with a Given Degree Sequence. Random Structures and Algorithms 6(1995) 161-180.

Search

• J. Kleinberg. Authoritative sources in a hyperlinked environment. Proc. 9th ACM-SIAM Symposium on Discrete Algorithms, 1998. Extended version in Journal of the ACM 46(1999). Also appears as IBM Research Report RJ 10076, May 1997.
• S. Brin and L. Page. The Anatomy of a Large-Scale Hypertextual Web Search Engine. Proc. 7th International World Wide Web Conference, 1998.
• S. Chakrabarti, B. Dom, D. Gibson, J. Kleinberg, S.R. Kumar, P. Raghavan, S. Rajagopalan, A. Tomkins, Mining the link structure of the World Wide Web. IEEE Computer, August 1999.
• A. Arasu, J. Cho, H. Garcia-Molina, A. Paepcke, S. Raghavan. Searching the Web. ACM Transactions on Internet Technology 1(1): 2-43 (2001)
• A. Borodin, J. S. Rosenthal, G. O. Roberts, P. Tsaparas, Finding Authorities and Hubs From Link Structures on the World Wide Web. 10th International World Wide Web Conference, May 2001.
• Dimitris Achlioptas, Amos Fiat, Anna Karlin, Frank McSherry, Web Search via Hub Synthesis. 42nd IEEE Symposium on Foundations of Computer Science, 2001, p.611-618.
• Davood Rafiei, Alberto Mendelzon. What is this Page Known for? Computing Web Page Reputations. Proc. WWW9 Conference, Amsterdam, May 2000
• Pedro Domingos, Matt Richardson. The Intelligent Surfer: Probabilistic Combination of Link and Content Information in PageRank. Advances in Neural Information Processing Systems 14, 2002.
• Taher H. Haveliwala. Topic-Sensitive PageRank. 11th International World Wide Web Conference, 2002.
• Alon Altman and Moshe Tennenholtz. Ranking Systems: The PageRank Axioms. Proceedings of ACM EC 2005.
• J. Kleinberg, P. Raghavan. Query Incentive Networks. Proc. 46th IEEE Symposium on Foundations of Computer Science, 2005.

Rank Aggregation

• Cynthia Dwork, Ravi Kumar, Moni Naor, D. Sivakumar. Rank Aggregation Methods for the Web. 10th International World Wide Web Conference, May 2001.
• Weiyi Meng, Clement Yu, King-Lup Liu. Building Efficient and Effective Metasearch Engines. ACM Computing Surveys 34(2002).
• William W. Cohen, Robert E. Schapire, and Yoram Singer. Learning to order things. Journal of Artificial Intelligence Research, 10: 243--270, 1999.
• T. Joachims. Optimizing Search Engines Using Clickthrough Data. Eighth International Conference on Knowledge Discovery and Data Mining, KDD-2002.

Finding Influential Nodes based on Cascade Models

• M. Gladwell. The Tipping Point. How Little Things Can Make a Big Difference, 2000.
• Pedro Domingos, Matt Richardson. Mining Knowledge-Sharing Sites for Viral Marketing. Eighth International Conference on Knowledge Discovery and Data Mining, KDD-2002.
• D. Kempe, J. Kleinberg, E. Tardos. Maximizing the Spread of Influence through a Social Network. Proc. 9th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 2003.

Epidemic Algorithms

• R. Karp, C. Schindelhauer, S. Shenker, B. Vocking. Randomized Rumor Spreading. 41st IEEE Symposium on Foundations of Computer Science, 2000.
• D. Kempe, J. Kleinberg, A. Demers. Spatial gossip and resource location protocols. Proc. 33rd ACM Symposium on Theory of Computing, 2001.

Sampling and the Web

• L. Lovasz. Random Walks on Graphs: A Survey. Combinatorics: Paul Erdos is Eighty (vol. 2), 1996, pp. 353-398.
• Ziv Bar-Yossef, Alexander Berg, Steve Chien, Jittat Fakcharoenphol, and Dror Weitz. Approximating Aggregate Queries about Web Pages via Random Walks. 26th International Conference on Very Large Databases (VLDB), 2000, pages 535-544.
• Soumen Chakrabarti, Mukul Joshi, Kunal Punera, and David M. Pennock. The structure of broad topics on the Web. 11th World Wide Web conference, May 2002.