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

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 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 Rule-based learning Pattern-matching e.g. “abnormal if it doesn’t cite any other people’s papers” Supervised Learning Manual labeling for training and classification Merit :  high precision Demerit :  domain dependent  expensive to create  sensitive to human bias  can only find expected, not for novel Unsupervised Learning Comparison-based due to our definition Property :  Easily adapted to new domain without training  More suitable to security-related problems

12 Design Considerations (con’t) System Requirements  Utilize information of MRN, e.g. type of links  Adapt to different domains, no training  Explainable  Scalable  Provide high-level bias  Support different levels of detail for explanations

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  An unsupervised way to identify abnormal in MRN  Outperform state-of-the-art algo. by a large margin  Generate understandable explanations  Do complex data analysis accurately and efficiently  Generality and applicability

17 Q & A Thanks for your listening !