This training is for professional development purposes and the certificate obtained does not replace the MYK authorization certificate required in trainings where an MYK authorization certificate is mandatory.

Purpose of the Training

Organizational Network Analysis is like a powerful X-ray device that allows you to see the real interaction flow beyond a company’s “formal” hierarchy. It provides important insights especially for Human Resources professionals and managers. With Organizational Network Analysis, various communication and interaction networks at the individual level within an organization are measured, and hidden implicit relationships that cannot be observed visually are revealed and understood through the obtained network data. The aim is to reveal the collaboration capacity and strength of employees, departments, and the entire organization. Through this analysis, communication gaps and bottlenecks between managers, teams, departments, and employees are identified, and the analysis enables understanding their causes. It contributes to achieving core organizational communication goals such as coordination in the workplace, problem-solving, sharing necessary technical knowledge, and conflict resolution. In summary, this analysis method forms the basis of organizational improvement efforts.

Within this framework, the Organizational Network Analysis training program includes theoretical knowledge on how ONA should be prepared, implemented, and interpreted, as well as practical applications using one of the software packages used in practice, PAJEK.

Training Content

PART 1: THEORETICAL FRAMEWORK

1. Definition of Network

System, complex systems, network, role of networks, elements of a network, undirected and directed networks, valued and unvalued networks, single-mode, two-mode and multi-mode networks, static and dynamic networks, size, neighborhood, sub-networks/cliques, triads in directed and undirected networks, strong and weak ties, hub: central node, core-periphery structure.

2. Network Manipulation

Network reduction, categorical and numerical node attributes and their inclusion in analysis, local, global and contextual views, network components (strong and weak components) and detection, bicomponents, bridges, brokers, cut nodes, brokerage roles (coordinator, itinerant broker, representative, gatekeeper, liaison) and detection, structural holes, constraint, constraint index and its use in binary logistic regression with performance, domains.

3. Determination of Structural Features and Visual Inspection

k-core analysis, real-world networks (biological, physical, social networks) and their effects, network science and its purpose, topology, basic topology types, statistical properties used in topological research: diameter, degree distribution and clustering coefficient (local and global clustering coefficients), correlation between in-degree and status, statistical properties of basic topologies, power-law distribution.

4. Social Network Analysis

Social network, functions of social networks, elements of social networks, content of relationships, social network theory, Social Network Analysis (SNA), why and when SNA is used, differences between SNA and other social science methods, assumptions of SNA, effects of relational networks, what is done in SNA, levels of analysis, relational data, data collection tools, conceptual questions, sample questions and networks, survey design, privacy and ethics.

5. Passive and Active Social Network Analysis

Digital data and survey data, digital or survey data? Example scenarios and real application case.

6. Formal and Informal Structure

Position of organizational networks, organization types, goals of organizations, organizational structure, organizational charts, purposes of organizational charts, types of organizational charts, formal and informal structure, differences between hierarchy and network organizations, benefits of network organizations, analysis of informal structure, communication, benefits of workplace communication, knowledge sharing, benefits of information flow, trust, benefits of trust in workplace, conceptual questions.

7. Organizational Network Analysis (ONA)

What analysts should look for (bottlenecks, peripheral nodes, unused expertise, network disconnections), talent management, problematic structures and solutions, definition of ONA, questions answered in ONA applications, contributions of ONA, who uses ONA, why ONA is used (strategic benefits).

8. ONA Application Steps

Steps of ONA application, statistical properties of social networks: individual (node) statistics: degree centrality, in-degree, out-degree, total degree, closeness centrality, betweenness centrality, eigenvector centrality, local clustering coefficient; group (network) statistics: degree centralization, in-degree centralization, out-degree centralization, closeness centralization, betweenness centralization, eigenvector centralization, average clustering coefficient, transitivity, density, average degree, diameter, size, modularity.

9. ONA Examples, Importance and Management of Informal Structure

First ONA application, ONA case studies (oil organization and computer systems organization), importance and management of informal structure, importance for managers and employees, resources accessed through networks.

10. Institution-Specific Recommendations

Design proposal for ONA tailored to the institution, strategies for analysis, preparation of ONA report, practical implementation through mutual discussion.

PART 2: SOFTWARE APPLICATIONS

1. Introduction to PAJEK

PAJEK program, installation and interfaces (main screen, drawing screen, output screen), menus and usage, concept of objects in PAJEK, networks, partitions, vectors, permutations, clusters, hierarchy.

2. Data Management

Creating directed and undirected networks manually in PAJEK, saving data files, preparing data files, file types (directed, undirected, binary, valued, arclist, matrix, two-mode network files), editing, creating files via Excel and Word.

3. Visualization and Drawing Screen

Menus (Layout, GraphOnly, Default, Previous, Redraw, Next, Options, Export, Spin, Info, FishEye, Wait), layout energy algorithms (Kamada-Kawai, Fruchterman-Reingold), node/edge properties, coloring.

4. Random Network Generation

Erdős–Rényi, small-world and scale-free networks, degree distribution, histograms, distribution shapes.

5. Node Properties and Network Reduction

Node attributes, local/global/contextual views, working with node properties.

6. Finding Cohesive Subgroups

Density, average degree, strong and weak components, k-cores, cliques.

7. Centrality

Center-periphery, distance, geodesic, diameter, degree centrality, closeness, betweenness, eigenvector centrality, matching.

8. Brokerage

Bridges, bicomponents, cut nodes, constraint statistics, brokerage roles.

9. Ranking

Popularity, in-degree, correlation, domains (in, out, general, constrained in-degree).

10. Clustering

Local clustering coefficient, average clustering coefficient, transitivity, modularity.

The training is open to institutional cooperation (corporate packages only; individual applications are not accepted). The content can be redesigned according to the profile and needs of corporate participants. The scope and method (face-to-face / online) are determined through mutual agreement, and the process is completed accordingly. If agreement is reached, the date, time, and location of the training are planned according to the availability of your institution’s participants and our instructors.