Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Structural Link Analysis from User Profiles and Friends Networks: A Feature Construction Approach William H. Hsu, Joseph Lancaster, Martin S. R. Paradesi, Tim Weninger Monday, 26 March 2007 Laboratory for Knowledge Discovery in Databases Kansas State University

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Link Analysis in Social Networks: The K-State Corpus

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Outline Background, Related Work and Rationale Technical Objective: Link Mining in Social Networks Methodology: Graph Feature Extraction Experimental Results: K-State LJMiner Corpus Continuing Work: Statistical Relational Models

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Problem Definition  Given: records of users of weblog or social network service  Discover  Features of entities: users, communities  Relationships: friendship, membership, moderatorship  Explanations and predictions for relationships Goals  Boost precision and recall of link existence prediction  Find relevant features Significance: Recommendations (Friendship, Membership) Problem Statement: Link Mining in Social Networks

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Related Work: Link Mining Getoor and Diehl (2005) - Graphical model representations of link structure Ketkar et al. (2005) - Data mining techniques vs graph-based representation Sarkar & Moore (2005) - Change in link structure across discrete time steps Popescul & Ungar (2003) - ER model to predict links Hill (2003), Bhattacharya & Getoor (2004) – Statistical Relational Learning to resolve identity uncertainty Resig et al. (2004) - Predicting IM online times using friends graph degree McCallum et al. (2005) - Inferring roles and topic categories based on link analysis

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Rationale Limitations of Current State of the Art  Do not take graph features into account  Limited ability to select, extract features Novel Contribution: Link Mining System  Extracts, computes features of network model  Towards dependent types for relational link mining Rationale  Desired functionality: infer new links from old  Evaluation: precision, recall for link existence

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Outline Background, Related Work and Rationale Technical Objective: Link Mining in Social Networks Methodology: Graph Feature Extraction Experimental Results: K-State LJMiner Corpus Continuing Work: Statistical Relational Models

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) K-State Test Bed: LJMiner Corpus User Contact Info User Interest, Schools, Friends Community Membership Info

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) LiveJournal Topology [1]: Tools and Security Model LJMindMap.com © 2004 mcfnord © 2007 Denga, Inc.

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) LiveJournal Topology [2]: Definitions

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Outline Background, Related Work and Rationale Technical Objective: Link Mining in Social Networks Methodology: Graph Feature Extraction Experimental Results: K-State LJMiner Corpus Continuing Work: Statistical Relational Models

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Graph Features [1]: Node, Pair, Link-Dependent uvuvuvuvuv Node-Dependent Features: specific to one node (vertex) within candidate pair Indegree (u) “Source popularity” Outdegree (u) “Source fertility” Outdegree (v) “Target fertility” Indegree (v) “Target popularity” Pair-Dependent Features: specific to one candidate pair of nodes (vertices) Link-Dependent Features: specific to one link (edge) in directed graph uv Common entities: interests, friends, schools, etc. Attributes of common entities Computed from relational query on entities u, v Past, predicted duration Diagnosed cause Computed and stored with relationship set

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Graph Features [2]: Node and Pair Features in LJMiner Graph Features Interest-Related Features

Computing & Information Sciences Kansas State University LJCrawler System Design  Data acquisition: client, injector, parser  Ancillary issues  Multi-threading  Distribution  Storage  Analytical postprocessing: LJClipper, LJStats Distinguishing features of LJCrawler Results  200 users/second maximum, 5 users/second allowed  Approximately 2 million pages crawled Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007)

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Outline Background, Related Work and Rationale Technical Objective: Link Mining in Social Networks Methodology: Graph Feature Extraction Experimental Results: K-State LJMiner Corpus Continuing Work: Statistical Relational Models

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Network Statistics: Graph Distance 1000 nodes 4000 nodes

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Interpretation of Results 941-node graph (Hsu et al., 2006): LJCrawler v1 output node graphs: LJCrawler v2 output

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Outline Background, Related Work and Rationale Technical Objective: Link Mining in Social Networks Methodology: Graph Feature Extraction Experimental Results: K-State LJMiner Corpus Continuing Work: Statistical Relational Models

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Results Establishing an Interdisciplinary Research Initiative  K-State / KU / UNL collaboration  Resources: Linguistic Data Consortium  NIST evaluations Involving End Users of Machine Translation  Document users  Machine learning, data mining, info extraction researchers Novel Applications  Social networks and collaborative recommendation  Gisting and beyond

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Information Extraction and Intelligent IR  Learning models for IE: ontologies  Latent semantic analysis Machine Learning  Natural language learning  Time series learning and understanding  Relational and first-order models Automated Reasoning  Probabilistic  Case-based and analogical Data Mining and Warehousing Grid Computing Continuing Work

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) References Knight, K. What’s New in Statistical Machine Translation. Invited Talk, International Joint Conference on Artificial Intelligence (IJCAI-2005), Edinburgh, UK, August, Knight, K. & Graehl, J. (2005). An Overview of Probabilistic Tree Transducers for Natural Language Processing. In Proceedings of CICLing 2005, p Chiang, D. A hierarchical phrase-based model for statistical machine translation. In Proceedings of the Conference of the Association for Computational Linguistics (ACL 2005), p. 263–270. Koehn, P., Och, F. J., & Marcu, D. (2003). Statistical Phrase-Based Translation. In Proceedings of HLT-NAACL 2003, the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, May 27 - June 1, 2003, Edmonton, CANADA.

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Acknowledgements K-State Lab for Knowledge Discovery in Databases  Vikas Bahirwani  Tejaswi Pydimarri  Andrew King Social Networks, Graph Theory, Graph Algorithms  Kirsten Hildrum (IBM T. J. Watson Labs)  Todd Easton (K-State, Industrial and Manufacturing Systems Engineering) Machine Learning  Dan Roth, Cinda Heeren, Jiawei Han (University of Illinois at Urbana-Champaign)  AnHai Doan (University of Wisconsin – Madison)

Computing & Information Sciences Kansas State University Boulder, Colorado First International Conference on Weblogs And Social Media (ICWSM-2007) Questions and Discussion