More Related Content Similar to MCPL2013 - Social network analyses in organizations: challenges and approaches for studying work networks (20) More from Vagner Santana (13) MCPL2013 - Social network analyses in organizations: challenges and approaches for studying work networks1. © 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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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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Results – Shift vs. Severity
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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
Editor's Notes sysadmin that work in same work shift tend to be more similar than the ones that work in different work shift connection among sysadmins during the work is higher to sysadmins that work in same department in same work shift, no matter the costumer or the tickets’ severity sysadmin that solves same type of ticket tend to be similar to each other most of tickets with severity one occurs in work shift 3 - night most of the tickets with severity three (Figure 3 (c) red) occurs in work shift 1 – morning