a social network intelligence tool for visual analysis of...

© Net Business Center / 1©2007 IKM,SYSEDV, TU Berlin

A Social Network Intelligence ToolA Social Network Intelligence Toolfor visual Analysis offor visual Analysis of

electronic Communication Networks electronic Communication Networks

27.03.2007 (GOR), M. Trier, IKM Research, SYSEDV, TU Berlin

(Kontakt: [email protected])

„Coming together is a beginning; keeping together is progress; working together is success“

Henry Ford

© Net Business Center / 2©2007 IKM, SYSEDV, TU Berlin, / 2

AGENDA

1. Motivation and ObjectiveCommunication and online networks

2. Research ApproachesModeling networks as CommunigraphsApproach of Dynamic Network AnalysisIT-Support: Commetrix Project

3. Applications and BenefitsAlgorithms and Methods applied to a newsgroup the corporate e-mail network of Enron


Content vs. Communication

Content Communication

Social Content

PersistentConversation

CommunityBuilding/

Networking

User generatedcontent

CollaborativeContent Development

(Wiki)

GroupsSocialization

Emergent Structures

Systems

Networks

Collective Intelligence

Micro Publishing(Blogs)

RSS-Feeds

(cf. Trier, 2007, Content und Kommunikation, In: Herzog (Ed.): Medienproduktion, in Press)


The changing world of online communication: Communication and Networks

Electronic communication:

novel combinatorial forms of content and communication.

micro-publishing, social contents, or collaborative content development.

Larger groups of people contribute user-generated content.

(read-and-write-web, bring-your-own-content)

increased perception of the link between contents and authors.

users connect, form allies and as a group support each other.

giving advice, directing the attention, or by collectively filtering

How can virtual social architectures be captured, modeled and understood?

*cf. Source: Trier (2007): Content und Kommunikation. In: Herzog (2007): Medienproduktion. In press.

*

*


Application of Social Network Analysis

Definition Social Network Analysis (SNA):Framework for the analysis of structured social relationships [WaFa94]; Authority relationships, informal communication, information exchange, affection

Current Measures:Size, relationship strength, roles (broker, hub, isolate, transmitter), activity, prominence, symmetry, centrality,…

Many Fields of Application:Interorganizational collaboration/information and knowledge flow – between core processes, practice areas, hierarchical levels, organizations, teams, departments, projects, partners, expertsTrace important networkers and multiplicatorsMap planned and living processes or structuresNetwork integration after structural change (e.g. merger)Information transfer bottlenecksMapping online communication within social architectures

*cf. Source: Trier (2007): Content und Kommunikation. In: Herzog/Sieck (2007): Medienproduktion, in press.


Shortcomings by current structural outcomes (static SNA)

Questions:Did the actor react to external events?Has the actor a long steady or a short but quick growth in his degree?Is the network stable or eventually already decaying again? Is a cluster stable or just an additive artefact?Which clusters do form and decay?Which are the most important nodes?How can I elicit the core structures? How are the dynamics of adopting a new topic?Where is the locus of novel ideas?How similar is the discussion at different locations in the network?How embedded are certain ego-networks within the overall network?Change can average out between two states of a network and thus can sometimes be poorly captured with statistical comparison

A

B

sending

receiving


Methods: Towards Dynamic Network Visualization and Analysis

labellabellabel

label

label

NodeActors

NodeattributesNames, Function, Locations, Evaluations

EdgeMessages, Contacts, Helprequests, Shared Objects

EdgeattributesEvent/Timestamp, Reference, Types, Content, Topics

Communigraph:Size, color, rings, brightness, position, label

Time window

Time interval

a) cumulative b) time-window/flipchart-book /decay

Model Elements


IT-Support: Commetrix Software Tool

Grafische Modelle und

AuswertungenE-Mail, Discussion, IM, Collab. Tool, VoIP, PMS

Mining & Visualization

Evolution of Virtual Groups

Topic Analysis,Expertise Search &

Expertise Maps

Elicitingcorestructuresand people incomplexnetworks www.commetrix.de


1) Project Initiation

2) Selecting relevant network – defining scope

3) capture and refine data / (multiple sources)

4) Visualization & Analysis

structure

contents

static dynamic

Volume of Communication, Network Size, Identification of Clusters and Subnets, Importance of individual actors (activity, connectedness, brokering position), Identification of isolated/weakly connected actors or subnets, strength/symmetryof relationshipsCumulative or time-window based longitudinal development, growth of thenetwork, frequence analysis, peaks, identification of time-based patterns

Topic-similarity or dissimilarity, cluster formation, categorization, identification of relevant topics, relate actors and links to the overall enterprise contextDissemination of information through the network, finding influencing factors fortime-based patterns by analyzing collaboration- and communication contents and corporate contexts

6) Execute and Implement Actions

5) Deriving Concept and Action Plan

Source: Trier/Bobrik(2007): Systemanalyse und Wissensmanagement. In: Krallmann et al. (2007): Systemanalyse im Unternehmen, in press.

Method for IT-supported Dynamic Network Analysis


The evolution of the most central author’s position in the network

a) 07/01/2000 b) 02/26/2001 Time

c) 10/24/2001 d) 06/21/2002 Time

degree centrality


Evolution of the most central author’s position in his ego-network

a) 02/05/2002 b) 02/07/2002

EGO Vice President Employee Manager President Trader CEO Lawyer


The Application

also see: www.commetrix.de


Measuring the level of network change (as ∆activity)

0

2

4

6

8

10

12

14

Sep 99

Nov 99

Jan 00

Mrz 00

Mai 00

Jul 00

Sep 00

Nov 00

Jan 01

Mrz 01

Mai 01

Jul 01

Sep 01

Nov 01

Jan 02

Mrz 02

Mai 02

Average Relationship Strength

Linear (Average Relationship Strength)

0

50

100

150

200

250

300

350

400

450

500

Sep-99

Nov-99

Jan-00

Mar-00

May-00

Jul-00

Sep-00

Nov-00

Jan-01

Mar-01

May-01

Jul-01

Sep-01

Nov-01

Jan-02

Mar-02

May-02

Number of Active Authors

Number of Active Relations

Time Window = 1mth


Example: Visual Trace of Brokering Actions in the Enron Discourse

a) 02/27/2000 b)

c) d) 02/28/2000

BrokeringActivity

BetweennessCentrality

BrokeringActivity

BetweennessCentrality

1Source: Trier/Bobrik(2007): Analyzing the Dynamics of Community Formation using Brokering Activities. Proceedings CCT 2007. Springer, in press.


That‘s it! Thanks for your attention!

Next issues:

Substantiating dynamic analysis methodsExtending topic analysisDeveloping complex evaluation indicators and visualization ideas!

Collaborations on new network sources and application fields (e.g. social software, corporate applications, virtual communication of online learners) Finding complementary partners for jointresearch funding applications?

Please contact:

Dr.-Ing. Matthias [email protected]

Technical University Berlin, GermanyInstitute for Business Informatics, http://www.sysedv.tu-berlin.de

www.commetrix.deDevelopment Team:

Annette BobrikMatthias Frank Tilmann Bartels

Andreas HoffmannDaniel Mueller

Matthias WerlitzMarkus WallaDanny Graef

Nicola GalanovNancy Kiesel

u.a.

a social network intelligence tool for visual analysis of...

Documents