CS673 Reading List

This list is much more extensive than what we will cover in class, in order to provide additional background, and give you a chance to learn about topics in more depth. It will also likely be updated as the semester moves along.

1. Background
   • V. Bush. As We May Think. Atlantic Monthly, July 1945.
   • A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, J. Wiener. Graph structure in the web. 9th International World Wide Web Conference, May 2000.
   • World Wide Web Consortium. A Little History of the World Wide Web, 1945-1995.


2. Web Search using Links, and Spectral Analysis of Data
   • 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, L. Page. The Anatomy of a Large-Scale Hypertextual Web Search Engine. Proc. 7th International World Wide Web Conference, 1998.
   • P. Domingos, M. Richardson. The Intelligent Surfer: Probabilistic Combination of Link and Content Information in PageRank. Advances in Neural Information Processing Systems 14, 2002.
   • T. Haveliwala. Topic-Sensitive PageRank. 11th International World Wide Web Conference, 2002.
   • H. Zhang, A. Goel, R. Govindan, K. Mason, B. Van Roy. Making eigenvector-based reputation systems robust to collusion. Workshop on Algorithms and Models for the Web Graph (WAW) 2004.
   • S. Deerwester, S. Dumais, T. Landauer, G. Furnas, R. Harshman. Indexing by latent semantic analysis. Journal of the Society for Information Science, 41(6), 391-407 (1990).
   • Y. Azar, A. Fiat, A. Karlin, F. McSherry, J. Saia. Spectral Analysis of Data. 33rd ACM Symposium on Theory of Computing, 2001.
   • K. Bharat, A. Broder, M. Henzinger, P. Kumar, S. Venkatasubramanian. The Connectivity Server: fast access to linkage information on the Web. Proc. 7th International World Wide Web Conference, 1998.
   • B. Amento, L. Terveen, W. Hill. Does "Authority" Mean Quality? Predicting Expert Quality Ratings of Web Documents. 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2000.
   • B. D. Davison. Recognizing Nepotistic Links on the Web. AAAI Workshop on Artificial Intelligence for Web Search, 2000.
   • A. Arasu, J. Novak, A. Tomkins, J. Tomlin PageRank Computation and the Structure of the Web: Experiments and Algorithms. 11th International World Wide Web Conference, 2002.
   • G. Pinski, F. Narin. Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics. Information Processing and Management, 12(1976), pp. 297--312.
   • L. Katz. A new status index derived from sociometric analysis. Psychometrika 18(1953).
   • C.H. Hubbell. An input-output approach to clique identification. Sociometry 28(1965).
   • P. Bonacich. Power and Centrality: A family of measures. American Journal of Sociology 92(1987).
   • D. Cohn, H. Chang Probabilistically Identifying Authoritative Documents. 17th International Conference on Machine Learning, 2000.
   • R. Lempel, S. Moran. The Stochastic Approach for Link-Structure Analysis (SALSA) and the TKC Effect. 9th International World Wide Web Conference, May 2000.
   • 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.
   • B. D. Davison. Topical Locality in the Web. 23rd Annual International Conference on Research and Development in Information Retrieval, 2000.
   • F. Menczer. Links tell us about lexical and semantic Web content. arXiv cs.IR/0108004.
   • M.E. Frisse. Searching for information in a hypertext medical handbook. Communications of the ACM 31(7).
   • J. Boyan, D. Freitag, T. Joachims. A Machine Learning Architecture for Optimizing Web Search Engines. AAAI Workshop on Internet Based Information Systems, 1996.
   • M. Marchiori. The Quest for Correct Information on the Web: Hyper Search Engines. Proc. 7th International World Wide Web Conference, 1998.
   • K. Bharat, M. Henzinger. Improved algorithms for topic distillation in a hyperlinked environment. 21st International Conference on Research and Development in Information Retrieval (SIGIR 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.
   • D. Cohn, T. Hofmann, The Missing Link -- A Probabilistic Model of Document Content and Hypertext Connectivity. Advances in Neural Information Processing Systems (NIPS) 13, 2000.
   • S. Chakrabarti, M. Joshi and V. Tawde. Enhanced topic distillation using text, markup tags, and hyperlinks. 24th Annual International Conference on Research and Development in Information Retrieval, 2001.
   • D. Rafiei, A. Mendelzon. What is this Page Known for? Computing Web Page Reputations. Proc. WWW9 Conference, Amsterdam, May 2000
   • J. Dean, M. Henzinger. Finding Related Web Pages in the World Wide Web. 8th International World Wide Web, 1999.
   • R. Lempel, A. Soffer. PicASHOW: Pictorial Authority Search by Hyperlinks on the Web. 10th International World Wide Web Conference, May 2001.
   • D. Achlioptas, A. Fiat, A. Karlin, F. McSherry, Web Search via Hub Synthesis. 42nd IEEE Symposium on Foundations of Computer Science, 2001, p.611-618.
   • C. Papadimitriou, P. Raghavan, H. Tamaki, S. Vempala. Latent Semantic Indexing: A Probabilistic Analysis. 17th ACM Symposium on the Principles of Database Systems, 1998.
   • A. Y. Ng, A. X. Zheng, and M. I. Jordan. Link analysis, eigenvectors, and stability. International Joint Conference on Artificial Intelligence (IJCAI), 2001.
   • A. Y. Ng, A. X. Zheng, and M. I. Jordan. Stable algorithms for link analysis. 24th International Conference on Research and Development in Information Retrieval (SIGIR 2001).
   • D. Donoho. High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality. Notes to accompany lecture at AMS Conference on Mathematical Challenges of the 21st Century, August 2000.


3. Community Structure
   • M. Granovetter. The strength of weak ties. American Journal of Sociology, 78(6):1360-1380, 1973.
   • M. Charikar. Greedy Approximation Algorithms for Finding Dense Components in Graphs. Proceedings of APPROX 2000.
   • G. Gallo, M. Grigoriadis, R. Tarjan. A fast parametric maximum flow algorithm and applications. SIAM Journal on Computing 18/1, 2/1989, pp. 30-55.
   • Gary Flake, Steve Lawrence, C. Lee Giles, Frans Coetzee. Self-Organization and Identification of Web Communities. IEEE Computer, 35:3, March 2002.
   • G. Flake, K. Tsioutsiouliklis, R.E. Tarjan. Graph Clustering Techniques based on Minimum Cut Trees. Technical Report 2002-06, NEC, Princeton, NJ, 2002.
   • R. Gomory, T.C. Hu. Multi-Terminal Network Flows Journal of the SIAM 9/4 (12/1961), pp.551-570.
   • R. Kumar, P. Raghavan, S. Rajagopalan, A. Tomkins. Trawling the web for emerging cyber-communities. 8th International World Wide Web Conference, May 1999.
   • D. Gibson, J. Kleinberg, P. Raghavan. Inferring Web communities from link topology. Proc. 9th ACM Conference on Hypertext and Hypermedia, 1998.
   • M. Newman. Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 (2004).
   • A. Clauset, M. Newman, C. Moore. Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004).
   • N. Bansal, A. Blum, S. Chawla. Correlation Clustering. Machine Learning 56(1-3):89-113, 2004.
   • M. Charikar, V. Guruswami, A. Wirth. Clustering with Qualitative Information. Proc. 44th IEEE FOCS (2003).
   • R. Andersen, F. Chung. Detecting Sharp Drops in PageRank and a Simplified Local Partitioning Algorithm. In Proc. TAMC 2007.
   • R. Andersen, F. Chung, K. Lang. Using pagerank vectors to locally partition a graph. Internet Mathematics 4 (2007), pp. 35--64.
   • N. Ailon, M. Charikar, A. Newman. Aggregating Inconsistent Information: Ranking and Clustering JACM 55(5), 2008.
   • S. Chawla, K. Makarychev, T. Schramm, G. Yaroslavtsev. Near Optimal LP Rounding Algorithm for Correlation Clustering on Complete and Complete k-partite Graphs. In Proc. of STOC 2015.
   • A. Wirth. Correlation Clustering. In Encyclopedia of Machine Learning, 2010.
   • P. Drineas, R. Kannan, A. Frieze, S. Vempala, V. Vinay "Clustering large graphs via the Singular Value Decomposition." Machine Learning 56, pp. 9-33, 2004.
   • R. Agrawal, R. Srikant. Fast Algorithms for Mining Association Rules. 20th Int'l Conference on Very Large Databases (VLDB), 1994.
   • S. Dill, R. Kumar, K. McCurley, S. Rajagopalan, D. Sivakumar, A. Tomkins. Self-similarity in the Web. 27th International Conference on Very Large Data Bases, 2001.
   • M. Girvan, M. Newman. Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 8271-8276 (2002).


4. Link-Based Classification
   • J. Kleinberg, E. Tardos. Approximation Algorithms for Classification Problems with Pairwise Relationships: Metric Labeling and Markov Random Fields. Proc. 40th IEEE Symposium on Foundations of Computer Science, 1999.
   • O. Veksler. Efficient Graph-Based Energy Minimization Methods in Computer Vision. Ph.D. Thesis, Cornell University, 1999.
   • J. Besag. Spatial interaction and the statistical analysis of lattice systems. J. Royal Statistical Society B, 36(1974).
   • S. Chakrabarti, B. Dom, P. Indyk. Enhanced hypertext categorization using hyperlinks. Proceedings of the ACM International Conference on Management of Data, SIGMOD 1998, pages 307-318.
   • A. Gupta and E. Tardos. A Constant Factor Approximation Algorithm for a Class of Classification Problems. Proc. of ACM STOC, 2000.
   • C. Chekuri and S. Khanna and S. Naor and L. Zosin. Approximation Algorithms for the Metric Labeling Problem via a New Linear Programming Formulation. Proc. SODA 2001.
   • A. Broder, R. Krauthgamer, and M. Mitzenmacher. Improved Classification via Connectivity Information. ACM-SIAM Symposium on Discrete Algorithms, 2000.
   • A. Blum, S. Chawla. Learning from Labeled and Unlabeled Data using Graph Mincuts. International Conference on Machine Learning (ICML), 2001.
   • T. Joachims, N. Cristianini, and J. Shawe-Taylor. Composite Kernels for Hypertext Categorisation. International Conference on Machine Learning (ICML), 2001.
   • B. Taskar, P. Abbeel and D. Koller. Discriminative Probabilistic Models for Relational Data. Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI 2002).


5. Rank Aggregation and Meta-Search
   • K. Arrow. Social Choice and Individual Values. Wiley, 1951.
   • C. Dwork, R. Kumar, M. Naor, D. Sivakumar. Rank Aggregation Methods for the Web. 10th International World Wide Web Conference, May 2001.
   • R. Fagin, R. Kumar, D. Sivakumar. Comparing top k lists. SIAM Journal on Discrete Mathematics 17/1, 2003, pp. 134-160.
   • C. Boutilier, I. Caragiannis, S. Haber, T. Lu, A. Procaccia, O. Sheffet. Optimal Social Choice Functions: A Utilitarian View Proc. EC 2012.
   • E. Anshelevich, O. Bhardwaj, J. Postl. Approximating Optimal Social Choice under Metric Preferences. Proc. AAAI 2015.
   • D. Richards, B. McKay, W. Richards. Collective Choice and Mutual Knowledge Structures. Advances in Complex Systems 1, 1998, pp. 221-36.
   • H.P. Young. Condorcet's Theory of Voting. American Political Science Review 82(1988), pp. 1231-1244.
   • E. Selberg, O. Etzioni. The MetaCrawler Architecture for Resource Aggregation on the Web. IEEE Expert, 1997.
   • W. Meng, C. Yu, K. Liu. Building Efficient and Effective Metasearch Engines. ACM Computing Surveys 34(2002).
   • W. Cohen, R. Schapire, Y. 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.


6. Power-Law Distributions
   • A.-L. Barabasi, R. Albert, and H. Jeong. Mean-field theory for scale-free random networks. Physica A 272 173-187 (1999).
   • M. Mitzenmacher. A Brief History of Generative Models for Power Law and Lognormal Distributions. Allerton Conference 2001.
   • 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.
   • A. Fabrikant, A. Luthra, E. Maneva, C. Papadimitriou, S. Shenker. On a Network Creation Game. Proc. of PODC, 2003.
   • M. Faloutsos, P. Faloutsos, C. Faloutsos. On Power-Law Relationships of the Internet Topology. ACM SIGCOMM 1999.
   • B. Huberman, L. Adamic. Growth dynamics of the World-Wide Web. Nature, 399 (1999) 130.
   • R. Kumar, P. Raghavan, S. Rajagopalan, D. Sivakumar, A. Tomkins, 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 in massive graphs. Handbook of Massive Data Sets, (Eds. James Abello et al.), Kluwer, 2002, pages 97-122.
   • M. Steyvers, J. B. Tenenbaum. The large-scale structure of semantic networks: statistical analyses and a model of semantic growth. Cognitive Science 29/1, 2005.
   • R. Albert and A.-L.Barabasi. Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47 (2002).
   • Q. Chen, H. Chang, R. Govindan, S. Jamin, S. Shenker, W. Willinger. The Origin of Power Laws in Internet Topologies Revisited. Proc. of IEEE Infocom 2002.
   • J. Carlson, J. Doyle. Highly Optimized Tolerance: A Mechanism for Power Laws in Designed Systems. Physical Review E 60:2(1999).
   • M. Molloy, B. Reed. A Critical Point for Random Graphs with a Given Degree Sequence. Random Structures and Algorithms 6(1995) 161-180.
   • M. Newman, S. Strogatz, D. Watts, Random graphs with arbitrary degree distributions and their applications. Phys. Rev. E 64, 026118 (2001).


7. Decentralized Search and the Small-World Phenomenon
   • S. Milgram. The small world problem. Psychology Today 1(1967).
   • J. Kleinfeld. Could it be a Big World After All? The `Six Degrees of Separation' Myth. Society, April 2002.
   • D. Watts, S. 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.
   • J. Kleinberg. Small-World Phenomena and the Dynamics of Information. Advances in Neural Information Processing Systems (NIPS) 14, 2001.
   • I. Abraham, S. Chechik, D. Kempe, and A. Slivkins. Low-distortion Inference of Latent Similarities from a Multiplex Social Network ACM-SIAM Symp. on Discrete Algorithms (SODA), 2013.
   • P. Sarkar, D. Chakrabarti, A. W. Moore. Theoretical Justification of Popular Link Prediction Heuristics. Intl. Conference on Learning Theory (COLT), 2010.
   • J. Travers and S. Milgram. An experimental study of the small world problem. Sociometry 32(1969).
   • P. Killworth and H. Bernard, Reverse small world experiment. Social Networks 1(1978).
   • G. Manku, M. Naor, U. Wieder. Know thy Neighbor's Neighbor: The Power of Lookahead in Randomized P2P Networks. Proceedings of STOC 2004.
   • D. J. Watts, P. S. Dodds, M. E. J. Newman Identity and Search in Social Networks. Science, 296, 1302-1305, 2002.
   • F. Menczer. Growing and Navigating the Small World Web by Local Content. Proc. Natl. Acad. Sci. USA 99(22): 14014-14019, 2002


8. Diffusion of Information Through Networks
   • F. Bass. A new product growth model for consumer durables. Management Science 15:215-227, 1969.
   • T. Schelling. Micromotives and Macrobehavior. Norton, 1978.
   • M. Granovetter. Threshold models of collective behavior. American Journal of Sociology 83(6):1420-1443, 1978.
   • S. Morris. Contagion. Review of Economic Studies 67 (2000), 57-78.
   • E. Berger. Dynamic Monopolies of Constant Size. Journal of Combinatorial Theory Series B 83(2001), 191-200.
   • D. Kempe, J. Kleinberg, E. Tardos. Maximizing the Spread of Influence in a Social Network. In Proceedings of KDD 2003.
   • E. Mossel, S. Roch. Submodularity of Influence in Social Networks: From Local to Global SIAM Journal on Computing 39(6):2176-2188, 2010.
   • H. Peyton Young. The Diffusion of Innovations in Social Networks. Santa Fe Institute Working Paper 02-04-018.
   • C. Asavathiratham. The Influence Model: A Tractable Representation for the Dynamics of Networked Markov Chains. Ph.D. Thesis, MIT 2000.
   • M. Richardson, P. Domingos. Mining the Network Value of Customers. Proc. KDD 2001.
   • P. Domingos, M. Richardson. Mining Knowledge-Sharing Sites for Viral Marketing. Eighth International Conference on Knowledge Discovery and Data Mining, KDD-2002.


9. Epidemic Algorithms in Networks
   • R. Karp, C. Schindelhauer, S. Shenker, B. Vocking. Randomized Rumor Spreading. 41st IEEE Symposium on Foundations of Computer Science, 2000.
   • D. Kempe, A. Dobra, J. Gehrke. Computing Aggregate Information using Gossip. In Proceedings of FOCS 2003.
   • B. Haeupler. Analyzing Network Coding Gossip Made Easy. In Proceedings of STOC 2011.
   • A. Demers, D. Greene, C. Hauser, W. Irish, J. Larson, S. Shenker, H. Sturgis, D. Swinehart, D. Terry. Epidemic Algorithms for Replicated Database Maintenance. Operating Systems Review 22(1): 8-32 (1988)
   • R. van Renesse. Scalable and secure resource location. Hawaii International Conference on System Sciences, 2000.
   • R. van Renesse, K. Birman, W. Vogels. Astrolabe: A Robust and Scalable Technology For Distributed System Monitoring, Management, and Data Mining. ACM Transactions on Computer Systems 21/2, May 2003, pp. 164-206.
   • M. Harchol-Balter, T. Leighton, D. Lewin. Resource Discovery in Distributed Networks. ACM Symposium on Principles of Distributed Computing (PODC), 1999, pp. 229-238.
   • S. Kutten, D. Peleg Deterministic Resource Discovery in Distributed Networks. ACM Symposium on Principles of Distributed Computing (PODC), 2000.
   • D. Kempe, J. Kleinberg, A. Demers. Spatial gossip and resource location protocols. Journal of the ACM 51/6, November 2004, pp. 943-967.
   • D. Kempe, J. Kleinberg. Protocols and Impossibility Results for Gossip-Based Communication Mechanisms. In Proceedings of FOCS 2002.
   • S. Boyd, A. Ghosh, B. Prabhakar, D. Shah. Gossip Algorithms: Design, Analysis and Applications. Infocom 2005.


10. Decentralized Search in Peer-to-Peer Networks
    • T. Hong. Performance. Peer-to-Peer: Harnessing the Power of Disruptive Technologies. (A. Oram, editor), O'Reilly and Associates, 2001.
    • E. Cohen, S. Shenker. Replication Strategies in Unstructured Peer-to-Peer Networks. SIGCOMM 2002.
    • L. Adamic, R. Lukose, A. Puniyani, B. Huberman. Search in Power-Law Networks. Phys. Rev. E, 64 46135 (2001).
    • A. Goel, H. Zhang, and R. Govindan. Using the Small-World Model to Improve Freenet Performance. IEEE Infocom, 2002.
    • G. Plaxton, R. Rajaraman, A. Richa. Accessing Nearby Copies of Replicated Objects in a Distributed Environment. ACM Symposium on Parallel Algorithms and Architectures, SPAA 1997.
    • S. Ratnasamy, P. Francis, M. Handley, R. Karp, S. Shenker. A Scalable Content-Addressable Network. ACM SIGCOMM, 2001
    • A. Rowstron, P. Druschel. Pastry: Scalable, distributed object location and routing for large-scale peer-to-peer systems. 18th IFIP/ACM International Conference on Distributed Systems Platforms (Middleware 2001).
    • I. Stoica, R. Morris, D. Karger, F. Kaashoek, H. Balakrishnan. Chord: A Scalable Peer-to-peer Lookup Service for Internet Applications. ACM SIGCOMM, 2001.
    • B. Zhao, J. Kubiatowicz, A. Joseph, Tapestry: An Infrastructure for Fault-Tolerant Wide-Area Location and Routing. UC Berkeley Computer Science Division, Report No. UCB/CSD 01/1141, April 2001.
    • M. Naor, U. Wieder. Novel Architectures for P2P Applications: the Continuous-Discrete Approach. Proceedings of SPAA 2003.
    • S. Ratnasamy, S. Shenker, I. Stoica. Routing Algorithms for DHTs: Some Open Questions. 1st International Workshop on Peer-to-Peer Systems (IPTPS), 2002.
    • D. Malkhi, M. Naor, D. Ratajczak. Viceroy: A Scalable and Dynamic Emulation of the Butterfly. ACM Symposium on Principles of Distributed Computing, 2002.