Modeling, Searching, and Explaining Abnormal Instances in Multi-Relational Networks

1. Modeling, Searching, and Explaining Abnormal Instances in Multi-Relational Networks Chapter 1. Introduction Speaker: Cheng-Te Li 2007 . 7 . 9

2. Outline • Introduction • Problem Definition • Multi-relational Networks • The Importance of Abnormal Instances • Explanation • Design Considerations • Objective and Challenges • Approach • Contributions

3. Introduction • A discovery is said to be an accident meeting a prepared mind.– Albert Szent Gyorgyi • For CS, to model the discovery process via AI • Motivation: “Natural Selection” • The discovery process

4. Outline • Introduction • Problem Definition • Multi-relational Networks • The Importance of Abnormal Instances • Explanation • Design Considerations • Objective and Challenges • Approach • Contributions

5. Problem Definition • Essentially, how to model through AI? • Our general framework • Three key features • Multi-relational network (MRN) • Abnormal Instances • Human-understandable explanation

6. Multi-relational Networks • Definition • Nodes : objects of different types • Links : binary relationships between objects • Multi-relational : multiple different types of links • Attributes • Encode semantic relationship between different types of object • E.g. Bibliography network

7. Multi-relational Networks (con’t) • More examples • Kinship network (親屬網絡) • WWW : incoming, outgoing, and email links • WordNet : lexical relationship between concepts • Multiple relationship types carry different kinds of semantic information to compare and contrast • PageRank, Centrality Theory • Cannot deal with relation types in a network

8. Abnormal Instances • Discovery from a network • Identify central nodes, recognize frequent subgraphs, learn interesting property • Our goal is to discover those look different ! • Attraction of “light bulb” • An unheard-of anomaly detection via relational data • Potential applications : • Information Awareness and Homeland Security • Fraud Detection and Law Enforcement • General Scientific Discovery • Data Cleaning

9. Explanation • The difficulty of verification • To find something previously unknown • False positive problem may exists even if high precision and high recall, which likes unsupervised discovery • Explanation-based discovery • Human-understandable explanation • Intuitive validation by user • Further investigation

10. Outline • Introduction • Problem Definition • Multi-relational Networks • The Importance of Abnormal Instances • Explanation • Design Considerations • Objective and Challenges • Approach • Contributions

11. Design Considerations • Three strategies to identify abnormal instances

12. Design Considerations (con’t) • System Requirements

13. Outline • Introduction • Problem Definition • Multi-relational Networks • The Importance of Abnormal Instances • Explanation • Design Considerations • Objective and Challenges • Approach • Contributions

14. Objectives & Challenges • Objectives • Discovery stage : identify abnormal nodes •  Explanation stage : produce descriptions for nodes found • e.g. organized crime network • Challenges •  Make anomaly detection obey previous requirements • Identify suspicious instances in MRN : rule-based, supervised • Conventional unsupervised algo. for propositional or numerical data • PageRank, HITS, Random Walk : not consider link types •  Consider understandable explanations as discovery • Need a complex-enough and not-over-complicated model

15. Approach •  Design a model capturing the semantic of nodes • Select a set of relevant path types as semantic features • Compute statistical dependency between nodes and path types as feature values •  Find nodes with abnormal semantics • Distance-based outlier detection with semantic profiles •  Explain them ! • Apply a classification to separate abnormal from others • Translate generated rules into natural language

16. Contributions

17. Q & A Thanks for your listening !