Network Mining (Blockseminar)

Ulf Brefeld, Steffen Bickel, Michael Brückner, Uwe Dick, Laura Dietz, Isabel Drost, Peter Haider, Tobias Scheffer

• Die Co-Refererenten sind jeweils hinter den Vortragenden zu finden.
• Termin des Blockseminars ist Mo, 12. Februar; Humboldt Kabinett, 9.00 Uhr . Die Ausarbeitungen bitte spätestens eine Woche vorher (also bis zum 5.2.2007) abgeben.
  
• Die meisten Themen sind bereits vergeben; wer noch mitmachen möchte findet freie Themen unten.
• Hier die Folien aus der Einführungsveranstaltung: [pdf]
  
• Durch die Verlegung des HK Maschinellen Lernens treffen wir uns am Mittwoch, 18.10. von 15-17 Uhr im ESZ Raum 1305!
• Die Themenvergabe findet am Ende des 1. Treffens statt, Themen und Literaturhinweise s.u.
• Den genauen Termin des Blockseminars legen wir in Absprache mit den Teilnehmern fest (Januar/Februar).
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• PageRank und HITS: Frank Habermann - Co Referent: Daniel Renz (Michael Brückner)
• C. Ding, X. He, P. Husbands, H. Zha, H. D. Simon. PageRank, HITS and a unified framework for link analysis, Proc. ACM SIGIR Conf. 2001
  
• L. Page, S. Brin, R. Motwani, T. Winograd, The PageRank citation ranking: Bringing order to the Web
  
• Jon M. Kleinberg: Authoritative Sources in a Hyperlinked Environment. Stanford Digital Library Technologies Project. Journal of the ACM. 1999.
• Diligenti et al.: A Unified Probabilistic Framework for Web Page Scoring Systems, 2004.
• Amy N. Langville, Carl D. Meyer: A Survey of Eigenvector Methods for Web Information Retrieval.

• Pagerank-Erweiterungen: Themen-Pagerank, Zeit-Pagerank, Block-Pagerank : Daniel Renz - Co Referent: Charlotte Pix (Ulf Brefeld)
• Yen-Yu Chen, Qingqing Gan, Torsten Suel: I/O-Efficient Techniques for Computing Pagerank.
• Taher H. Haveliwala: TopicSensitive PageRank.
• Bing Liu, Philip S. Yu, Xin Li: On the Temporal Dimension of Search.
• Matthew Richardson, Pedro Domingos: The Intelligent Surfer: Probabilistic Combination of Link and Content Information in PageRank.
• Sepandar D. Kamvar, Taher H. Haveliwala, Christopher D. Manning, Gene H. Golub: Exploiting the Block Structure of theWeb for Computing PageRank.
• Claudia Hauff and Leif Azzopardi: Age Dependent Document Priors in Link Structure Analysis.

• Information Retrieval mit probabilistischem HITS: Alexandra Rostin - Co Referent: Frank Habermann (Uwe Dick)
  
• Learning to Probabilistically Identify Authoritative Documents  Cohn D, Chang H
• The missing link - a probabilistic model of document content and hypertext connectivity, Cohn D, Hofmann T

• Link Spam-Erkennung und -Unterdrückung: Florian Holzhauer - Co Referent: Christian Krowiorsch (Isabel Drost)
  
• Fetterly et al.: Spam, Damn Spam, and Statistics: Using Statistical Analysis to Locate Spam Web Pages. 2004.
  
• Drost and Scheffer: Thwarting the nigritude ultramarine: learning to identify link spam. ECML 2005.
• Wu and Davison: Identifying Link Farm Spam Pages. WWW 2005.
• Gyongyi et al.: Combating web spam with TrustRank. VLDB 2004.
  
• AIRWeb 2005, First International Workshop on Adversarial Information Retrieval on the Web

• Query-Log Ranking: Charlotte Pix - Co Referent: Franziksa Brosy (Isabel Drost)
  
• Z. Zhuang, S. Cucerzan, and C. Lee Giles: Network Flow for Collaborative Ranking. ECML 2006
• R. Kannan, S. Vempala, and A. Vetta. On clusterings: good, bad and spectral. FOCS 2000
  
• J. Shi and J. Malik. Normalized cuts and image segmentation. PAMI, 2000
• J. Luxemburger and G. Weikum: Query-Log Based Auhthority Analysis for Web Information Search. WISE 2004
• Soumen Chakrabarti and Alekh Agarwal: Learning Parameters in Entity-relationship Graphs from Ranking Preferences., ECML 2006
  
• Almeida, Rodrigo B, Almeida, Virgilio A FA Community-Aware Search Engine

• Probabilistic Relational Models: Franziska Brosy - Co Referent: Alexandra Rostin (Ulf Brefeld)
• Nir Friedman et al.: Learning Probabilistic Relational Models, 1999.
  
• L. Getoor, N. Friedman, D. Koller, B. Taskar, Learning probabilistic models of link structure, JMLR 3, 2002.
• Kubica J, Moore A, Schneider J, Yang Y, Stochastic link and group detection

• Community Detection mit kürzesten Wege-Analyse: Björn Schümann - Co Referent: Jochen Heyden (Laura Dietz)
  
• Girvan Newman Paper
• Tyler, Wilkinson and Huberman: Email as Spectroscopy: Automated Discovery of Community Structure within Organizations
• Finding community structure in very large networks
  Clauset A, Newman MEJ, Moore C

• Community Detection mit HITS: Jochen Heyden - Co Referent: Björn Schümann (Laura Dietz)
  
• Gibson D, Kleinberg JMK, Raghavan P,Inferring Web Communities from Link Topology
• Flake et al.: Efficient Identification of Web Communities, SIGKDD 2000
  
• Flake et al.: Methods for Mining Web Communities: Bibliometric, Spectral, and Flow, 2003.

• Collective Classification: Sebastian Schütze - Co Referent: Florian Holzhauer (Peter Haider)
• B. Taskar, P. Abbeel, D. Koller, Discriminative probabilistic models for relational data, Proceedings of UAI2002
  
• J. Neville, D. Jensen, Collective Classification with Relational Dependency Networks
• P. Domingos, M Richardson, Mining the Network Value of Customers, Proc. International Conference on Knowledge Discovery and Data Mining, 2001
• Lu Q, Getoor, Link-based Text Classification

• Halbüberwachte Klassifikation auf vernetzten Daten: Christian Krowiorsch - Co Referent: Sebastian Schütze (Uwe Dick)
• M. Belkin, P. Niyogi, Semi-supervised learning on riemannian Manifolds, Machine Learning 56 (1-3), 2004
  
• Dengyong Zhou, Bernhard Schölkopf, and Thomas Hofmann, Semi-supervised Learning on Directed Graphs, Advances in Neural Information Processing Systems 2005
• Xiaojin Zhu, Zoubin Ghahramani, John Lafferty, Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions , ICML 2003
• Zhou, D., J. Huang and B. Schölkopf: Learning from Labeled and Unlabeled Data on a Directed Graph. Proceedings of the 22nd International Conference on Machine Learning

• Einführung in natürliche/zufällige Graphen: Habib Shakhawat (Peter Haider)
• Ausschnitt aus Wasserman and Faust
• J. Leskovec, J. Kleinberg, C. Faloutsos, Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations, KDD 2005
• Andrei Broder, Ravi Kumar, Farzin Maghoul, Prabhakar Raghavan, Sridhar Rajagopalan, Raymie Stata, Andrew Tomkins, Janet Wiener, Graph structure in the web, Computer Networks, International Journal of Computer and Telecommunications Networking, 2000
• Watts D, Strogatz S, Collective dynamics of small-world networks, Nature, 1998
• Characterizing and Mining the Citation Graph of the Computer Science Literature, Knowledge and Information Systems, Vol. 6, No. 6. (November 2004), pp. 664-678.
• Simkin, Roychowdhury, A mathematical theory of citing 
• Broder A, Kumar R, Maghoul F, Raghavan P, Rajagopalan S, Stata R, Tomkins A, Wiener J, Graph structure in the Web
• Newman, Forrest, Balthrop, Email networks and the spread of computer viruses

• Bayessche Netze: Benjamin Werner (Ulf Brefeld)
  
• E. Charniak, Bayesian Networks without Tears, AI magazine, 1991
• P. Smyth. Belief networks, hidden Markov models, and Markov random fields: a unifying view, Pattern Recognition Letters, 1998.

• Linkvorhersage und Community-Mining mit Stochastic Block Models: Manuel Hertlein (Steffen Bickel)
  
• Estimation and Prediction for Stochastic Blockstructures Journal of the American Statistical Association, Vol. 96, No. 455.  September 2001, pp. 1077-1087.
  
• Kemp, Griffiths and Tenenbaum Discovering Latent Classes in Relational Data (2004)

• Email-Spam-Filterung mit Hilfe von sozialen Netzen: Alexey Grachev (Michael Brückner)
• Golbeck, Hendler: Reputation Network Analysis for Email Filtering
• Comparative Graph Theoretical Characterization of Networks of Spam and Legitimate Email
• Scalable and Reliable Collaborative Spam Filters: Harnessing the Global Social Email Networks
• Boykin, P., & Roychowdhury, V. (2004).Personal email networks: an effective anti-spam tool. Preprint,

• Frequent Subgraph Mining: Christian Gebhardt
• L. T. Thomas, S. R. Valluri, K. Karlapalem, MARGIN: Maximal Frequent Subgraph Mining, Tech Report, 2006
• T. Horvath, J. Ramon, S. Wrobel, Frequent subgraph mining in outerplanar graphs, International Conference on Knowledge Discovery and Data Mining, 2006

• Klassifikation von Graphen: Peter Siemen (Ulf Brefeld)
• T. Gärtner, A Survey of Kernels for Structured Data, SIGKDD Explorations 2003
• K.M. Borgwardt, H-P Kriegel, Shortest-path kernels on graphs, ICDM 2005