# Finding Tribes: Identifying Close-Knit Individuals from Employment Patterns Lisa Friedland and David Jensen Presented by Nick Mattei.

## Presentation on theme: "Finding Tribes: Identifying Close-Knit Individuals from Employment Patterns Lisa Friedland and David Jensen Presented by Nick Mattei."— Presentation transcript:

Finding Tribes: Identifying Close-Knit Individuals from Employment Patterns Lisa Friedland and David Jensen Presented by Nick Mattei

Introduction Tribes – groups with similar traits in a large graph Distinguish those that work together and move together intentionally

Relationship Knowledge Discovery Exploit connections among individuals to identify patterns and make predictions Discover underlying dependencies Links must be inferred

Graph Mining Discover Hidden Group Structures Animal Herds, Webpages, Employees Time Series Analysis Co-integration (Economics) Security and Intrusion Detection Dynamic Networks

Motivation National Association of Securities Dealers Fraud Collusion 4.8 Million Records 2.5 Million Reps at 560,000 Firms 100 Years of Data

Complications Jobs not necessarily in order (or singletons) 20% of employees hold more than one job at a time 10% begin multiple jobs (up to 16) on one day Leave gaps between employment Mergers and acquisitions

Model

Finding Anomalously Related Entities Input: Bipartite Graph: G = (R  A, E) Entities: R = {r1, r2, …, rn} (People) Attributes: A = {a1, a2, …, am} (Orgs.) Entities should connect several attributes Model co-occurrence rates of pairs of attributes

Algorithm

Simple Model Measures JOBS = (Number of shared Jobs in the sequence) YEARS = (Number of Years of overlap)

Example Sequences

Probabilistic Model X = P(BrA -> BrB -> BrC -> BrD) = pa * tAB * tBC * tCD Estimate: P(start branch i) =(#reps ever at i) / (#reps in database) Tij = P(reps from i to j | #ever at i) =(#reps leave i to go to j) / (ever at i)

Probabilistic Model Null Hypothesis of Independent Movement Movement Not Random Split and Merge Markov Chains

Probabilistic Model (Different Paths) Tij becomes Vij Vij = P(move to branch j at any point after branch I | currently at i) = (# reps who go to branch j at any point after working at i) / (# reps ever at i) Now each vij >= tij and probabilities no longer sum to 1.

Probabilistic Model (Different Paths) Vij becomes Wij Wij = P (move to branch j at any point simultaneous to or after branch i | currently at i) = (# reps who start at j at any point simultaneous or after starting at i) / (# of reps ever at i) Now less precise in respect to direct transitions but more general

PROB - TIMEBINS Bins of 1 year or more 10 people worked at each branch in a bin period PiX = # reps ever at i during time X / # reps in DB yiXjY = # reps ever at I during time X and at j during time Y, where Y >= X / # reps ever at i during time X

PROB-NOTIME Ignores order of job moves Use original pi Zij = raw number of reps who are at both branches I and j during career Transition Pr from i to j: = (zij / # reps ever at i) != (zij / # reps ever at j) =transition Pr from j to i

Tribe Size

Pairs

Commonality of Job Sequence

Disclosure Scores

Homogenaity and Mobility

Discussion JOBS, PROB, PROB-TIME, PROB- NOTIME create tribes with higher than average disclosure scores PROB creates more cross zip code results PROB-TIME has higher phi-squared than all others PROB favors large firms

Discussion JOBS and YEARS compute larger connected components JOBS and PROB find same number of tribes but pick different groups as tribes

Conclusions With no explicit knowledge we can discover: Job transitions Geography Career track

Conclusions Needed: Ongoing process Multiple affiliations Arbitrary times Time is a paradox in domain

Thanks! Time for: Questions Comments Smart Remarks

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