This document summarizes a study on social network analysis in organizations. The study analyzed work networks at a large IT service organization using data from 108,050 support tickets over 10 months. Four features were considered for each of the 315 sysadmins: work shift, department, customer, and problem severity. Networks were built connecting sysadmins based on similarity across these features. The results showed distinct networks emerged for each feature combination, such as separate networks for sysadmins based on similarity in service lines vs shift or severity. The document concludes that network analysis is a promising approach to uncover relationships in complex work environments and more research is needed on methods for very large multi-edge networks.

1. © 2013 IBM Corporation
Social network analyses in organizations:
challenges and approaches for
studying work networks
Ana Paula Appel, Vagner F. de Santana, Rogério A. de Paula
Claudio S. Pinhanez, Victor F. Cavalvante
IBM Research Brazil, Service Systems
MCPL 2013 - Sep 11-13, 2013
IBM Research

2. © 2013 IBM Corporation
Agenda
 Context
 Related works
 Challenges
 Study
 Results
 Final remarks
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3. © 2013 IBM Corporation
Context
 The spread of online Social Networks (SN) use is responsible for the
current high interest in complex network analyses, e.g.:
 “People you may know” feature
 How people get connected?
 SN are networks in which
 Nodes represent people, or groups of people
 Edges some form of social interaction
 Social Network Analysis (SNA) allows to diagnose problems and identify
opportunities in organizations
 SNA allows to identify collaborative hot spots and cold spots
 In organizations, connecting people with similar skills, interests, or profiles
can be worth for employees in many ways
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4. © 2013 IBM Corporation
Context
 Our work took place at a large IT service delivery organization, Big
Service Factory (BSF)
 Employees are very specialized, often focused on specific tasks
 Sysadmins are responsible for some of the most critical functions
necessary to maintain the customers’ IT infrastructures
 Tickets describe particular incidents
 Tickets have attributes indicating work shift, department, customer, date,
descriptions of the problem, among others
 Challenges in the context involve
 Assignment time
 Data insertion by hand
 …
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5. © 2013 IBM Corporation
Related works
 The foundation of the field is attributed to Jacob Levy Moreno who
conducted the first long-range sociometric study from 1932-1938
 Monge and Contractor [1987] utilized social network analysis to study the
communication patterns among workers
 Properties identified:
 The power-law degree distributions as showed in Albert et al. [1999] and
Adamic et al. [2000]
 The diameter shrinkage present in evolving networks presented by Leskovec
et al. [2005]
 The Small World phenomenon studied by Milgram [1967]
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6. © 2013 IBM Corporation
Challenges
 How to combine data sources, build a very large network, and mine it?
 Each data source can become a network and all networks can be
combined making one large weighted or multi-edges network, but…
 How do we define the weight for each graph?
 When using multi-edge graphs, the number of available algorithms/techniques
is an issue.
 It is hard to define the mining task for multi-edge graph
 Combining different type of sources to build one very large graph requires
 New algorithms
 New range of methodology and definitions of graph mining area
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7. © 2013 IBM Corporation
Study
 108,050 tickets
 Collected from the past 10 months
 315 sysadmins
 Four features where considered
 Work shifts (morning, afternoon and night)
 Department (four departments)
 Customers (around 70)
 Problem severity (1, the hardest one to solve)
 We consolidate the monthly data for each sysadmin into a percentage
representing the weight of each of customers, service lines, shifts, etc.
 Each sysadmin was represented by a feature vector with 80 positions
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8. © 2013 IBM Corporation
Study
 To build the networks, we compute the cosine distance among each
sysadmin using a K-NN (K Nearest Neighbor) search, using k = 10
 Who are the 10 most similar sysadmins?
 We explored two types of network
 Using the entire feature vector to compute the distance
 Splitting the feature vector into four independent vector, each one
representing one feature, building four networks, and then merging them into
one multi-edge network
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Results – Severity , Shift, Department, and Customer
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Results – Service Lines vs. Shift
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Results – Service Lines vs. Severity
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12. © 2013 IBM Corporation
Results – Shift vs. Severity
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13. © 2013 IBM Corporation
Final remarks
 Unveiling underlying emerging networks is a promising approach
 Integrating social analytics and qualitative research has the potential to
reveal "hidden" practices of daily distributed complex tasks
 Using network as a model to connect sysadmin is important specially in
crises situation
 Future work involves
 Field work
 Integrating ticket information with sysadmins’ communication patterns
 New methods and tools are needed to process and display very large
networks with parallel edges
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14. © 2013 IBM Corporation
Thank you!
VagSant@br.ibm.com
@santanavagner