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Social Network Analysis forIntelligence and Analytics

Rohan Baxter

Corporate Analytics

Office of the Chief Knowledge Officer

Australian Taxation Office

24 May 2011

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Social Network Mining 2

Overview

What is a Social Network?

Analytics vs Intelligence/Visualisation

A scalable network-finding algorithm

Four case studies (abstracted, de-identified)

A method for representing networks

Future work/lessons learnt

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Social Network Mining 3

What is a Social Network

Network consists of nodes and links

Real Social Network: nodes are people and links

mean is a friend of

Node type examples: forms, companies,

individuals, various asset classes

Link type examples: share an attribute,

distributes to, is owner of

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Social Network Mining 4

Social Network Analysis Uses

Issues around network data collection

Data Analysis

Low volume, manual Specific targets, Complex

Analysis

(for Intelligence or Audit work)

High volume, automated risk of assessment of

large populations of networks

(for Analytics for larger scale treatments)

Visualisation (usually low volume, but see later)

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Social Network Mining 5

Social Network Risk Assessment

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Social Network Mining 6

SQL Implementation

Consolidation of internal implementations in a

variety of systems and languages (e.g. Visual

Basic, Python, SAS, R, Netmap )

SQL implementation is about 45 lines, follows

Union-Find algorithm.

Demonstrated advantages: Scalable, Correct,

Concise

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Social Network Mining 7

Algorithm has Fast Convergence

Network Dete ction Algorithm Convergen

1

10

100

1000

10000

100000

1000000

10000000

1 2 3 4 5 6 7 8 9

Numbe r of Iteration

Log(Number

of

Components

to

Merge)

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Social Network Mining 8

Case Studies

Purpose No. of Nodes No. ofLinks

Filter low-risk entities from a

list of high risk entities

2,000 8,000

Using starting-list of identifiedhigh-risk entities and find

related unknown high riskentities

300-2,000

2,000-16,000

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Social Network Mining 9

Case Studies

Purpose No. of nodes

No. ofLinks

Find non-agent tax

returns that appear tohave a common

guiding mind

1.2m

Networks ofinterest:

6,000

20m

Find high-risk entitynetworks involvingcompany structures

2m 18m

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Social Network Mining 10

A High Risk Social Network

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Social Network Mining 11

Network Representation in a database field

(coy -> ind)

(ptr -> ind)

(trt->ind unk)

(coy -> (coy -> ind))

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Social Network Mining 12

Network Representation in a Database Field

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Social Network Mining 13

Power laws: No. of Networks vs Network Size

1

10

100

1000

10000

0 20 40 60 80 100

Network Size (Trus ts-Beneficiarie

NumberofNetworks(

scale)

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Social Network Mining 14

Risk Differentiation for Networks

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Social Network Mining 15

Related Work

PWC Research Centre, San Jose, Ca: Used networkdetection algorithm to assist with financial accounts

audit, by highlight high-risk entries in a general ledger

Internal Revenue Service(IRS), US: Built database of

company networks and a graphical tool to query the

networks with known scheme structures

Detecting securities fraud based on network of

relationships between brokers

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Social Network Mining 16

Lessons Learnt

Continued awareness of network -level risk

assessments in forward plan of risk assessment work

Using links has helped reduce false positive rate fordiscovering non-compliant entities

Network discovery is intuitive and reinvented atleast 6 times across organisation, but advantages in

corporate approach to get it scalable and correct