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Emerging Trends in KDD Research: Insights into Graph Mining, Social Networks, and Recommender Systems

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oscar-bailey
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This overview presents the latest trends in Knowledge Discovery and Data Mining (KDD) research, focusing on areas such as graph mining, social network analysis, and recommender systems. Highlights include advancements in data mining algorithms, methodologies for capturing and analyzing social dynamics, and the application of collaborative filtering and content-based approaches in personalized recommendations. The discussion covers challenges faced in these areas, including handling high-dimensional data and the dynamic nature of social networks, alongside emerging applications in marketing and biological data analysis.

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Emerging Trends in KDD Research: Insights into Graph Mining, Social Networks, and Recommender Systems
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  1. Outlook • Outline • Trends in KDD research • Graph mining and social network analysis • Recommender systems • Information extraction SFU, CMPT 741, Fall 2009, Martin Ester

  2. Trends in KDD Research • KDD 2000 Conference • New Data Mining Algorithms • Efficiency and Scalability of Data Mining Algorithms • Interactive Data Exploration • Visualization • Constraints and Evaluation in the KDD Process SFU, CMPT 741, Fall 2009, Martin Ester

  3. Trends in KDD Research • KDD 2002 Conference • Statistical Methods • Frequent Patterns • Streams and Time Series • Visualization • Web Search and Navigation • Text and Web Page Classification • Intrusion and Privacy • Applications SFU, CMPT 741, Fall 2009, Martin Ester

  4. Trends in KDD Research • KDD 2004 Conference • Frequent Patterns / Association Rules • Clustering • Mining Spatio-Temporal Data • Mining Data Streams • Dimensionality Reduction • Privacy-Preserving Data Mining • Mining Biological Data • Applications (Web, biological data, security, . . .) SFU, CMPT 741, Fall 2009, Martin Ester

  5. Trends in KDD Research • KDD 2006 Conference • Clustering • Classification / supervised ML • Privacy • Web / Graph Mining • Web / Text Mining • Frequent Pattern Mining • Structured Data SFU, CMPT 741, Fall 2009, Martin Ester

  6. Trends in KDD Research • KDD 2008 Conference • Text Mining • Data Integration • Social Networks • Graph Mining • Distance Functions and Metric Learning • Active and Semi-supervised Learning • Pattern Mining • Collaborative Filtering SFU, CMPT 741, Fall 2009, Martin Ester

  7. Trends in KDD Research • Some Hot Topics • Social Networks THE hot topic of KDD 08 topic of the only panel • Graph mining • Text mining and information extraction / integration • Collaborative Filtering more general, recommender systems $1M NetFlix prize SFU, CMPT 741, Fall 2009, Martin Ester

  8. Graph Mining and Social Network Analysis • Motivating Applications • Social network analysis • What communities exist? • How does information about a new product spread? • What customers should be targeted to maximize the profit of a marketing campaign? • Analysis of biological networks o What are the functional modules of an organism? o How do biological networks evolve in the course of time? o What protein should be targeted to inhibit some virulent bacteria? SFU, CMPT 741, Fall 2009, Martin Ester

  9. Graph Mining and Social Network Analysis • Methods • Frequent subgraph mining • frequent pattern mining approach • Graph clustering e.g., normalized cut, i.e. minimize number of edges between graph components / clusters • Graph generative models probabilistic models that generate graphs similar to real graphs / networks SFU, CMPT 741, Fall 2009, Martin Ester

  10. Graph Mining and Social Network Analysis • Challenges • Complexity of graph algorithms • Many graph mining problems are NP-hard. • Real graphs tend to be extremely large.  need efficient algorithms • Attribute data • Many graphs have attributes associated with the nodes. • Transformation into weighted graph looses a lot of information.  need new models / algorithms considering relationship and attribute data SFU, CMPT 741, Fall 2009, Martin Ester

  11. Graph Mining and Social Network Analysis • Challenges • Dynamics of social networks • Social networks tend to be very dynamic. • What are the stable communities? • What are the significant changes over time? • Rich types of relationships • Multi-graphs. • Hypergraphs. • Uncertain graphs. •  new data mining problems and algorithms SFU, CMPT 741, Fall 2009, Martin Ester

  12. Recommender Systems • Motivating Applications • MotivationoThe internet provides a flood of information on all kinds of items.o There is a great need for personalized recommendations. o The internet also provides a wealth of item ratings / reviews. • Typical applications • Movie recommendation • Product recommendation • Keyword recommendation SFU, CMPT 741, Fall 2009, Martin Ester

  13. Recommender Systems • Methods • Collaborative filteringoUses only a database of user – item ratings.o Recommendation based on ratings by users with similar rating patterns. • Content-based recommender systems • o Uses information about the content of items and / or the properties of users. • o Recommends items that have content similar to items liked by user. • Trust-based recommender systems • Assume a social network / trust network. Trust can be defined explicitly or implicitly. • Recommendation based on ratings by trusted neighbors. SFU, CMPT 741, Fall 2009, Martin Ester

  14. Recommender Systems • Challenges • High dimensionality and sparsity of dataoThe overwhelming majority (> 99%) of user item ratings is unknown.o Recommendation especially hard for cold start users and controversial items. •  dimensionality reduction, model based methods, trust-based approach • Fraud • o Memory-based collaborative filtering can be easily manipulated by adding fraudulent ratings. •  trust-based approach more robust to fraud • Privacy issues with trust network data • o only very few trust networks are public domain SFU, CMPT 741, Fall 2009, Martin Ester

  15. Information Extraction • Motivating Applications • Importance of unstructured text data oThe overwhelming majority (>= 80%) of human generated information is not in structured form, but in unstructured text. • Biomedical literature • o Contains a wealth of valuable information that cannot be processed / searched automatically. • o Extraction of entities and relationships such as proteins and their localizations. • Online product reviews • o A lot of product „reviews“ available online in community databases or blogs. • o Companies want to know what customers think of their products. SFU, CMPT 741, Fall 2009, Martin Ester

  16. Information Extraction • Methods • Basic NLP methods oPart-of-speech tagging • o Lexica, ontologies, . . . • Machine learning methods • o Typically, supervised classification. • o CRFs and similar methods are state-of-the-art. • Bootstrapping approach • o Using a small labeled training dataset, find textual extraction patterns. • o Using these patterns, extract further entities / relationships and continue. SFU, CMPT 741, Fall 2009, Martin Ester

  17. Information Extraction • Challenges • Text data is hard to understand oMany of the NLP problems are still essentially unsolved.  relatively simple NLP methods often sufficient for information extraction • Portability across domains • o Extraction methods need to be portable from one domain to another. • o Knowledge engineering approach (domain expert defines rules) is labor-intensive and expensive. •  machine learning methods • Entity mentions need to be resolved • o Information extraction produces strings referencing an entity of a given type. • o Without mapping to known real world entities, extracted information is of limited usefulness. need to integrate extracted information with existing databases SFU, CMPT 741, Fall 2009, Martin Ester

  18. References • Graph mining • X Yan & Karsten Borgwardt, "Graph Mining and Graph Kernels", Tutorial KDD 08 • Jure Leskovec and Christos Faloutsos, “Mining Large Graphs: Models, Diffusion and Case Studies”, Tutorial ECML/PKDD 2007 • Recommender systems • Joseph Konstan, “Introduction to Recommender Systems”, Tutorial SIGMOD 2008 • Information extraction - Eugene Agichtein & Sunita Sarawagi, “Scalable Information Extraction and Integration”, Tutorial KDD 06 • - AnHai Doan & Raghu Ramakrishnan & Shiv Vaithyanathan,“Managing Information Extraction”, Tutorial SIGMOD 2006 SFU, CMPT 741, Fall 2009, Martin Ester