Finding Tribes: Identifying Close-Knit Individuals fromEmployment 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)

ip

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