The document introduces the field of network science through a range of applications including gene, social, and communication networks. It discusses key concepts such as network robustness, importance metrics, and community identification, along with relevant tools and software for analysis. The author, Colleen M. Farrelly, is a senior data scientist with expertise in various domains and is currently working on a two-part book on network science methods.

1. An Introduction to
Network Science
Colleen M. Farrelly,
Staticlysm/Datasembly

2. About Me
• Senior data scientist (Datasembly/Staticlysm)
with experience in education, healthcare,
biotech, advertising technologies, and
marketing data.
• Expertise in topological data analysis,
geometry-based machine learning, quantum
algorithms, network science, natural language
processing, and traditional fields of machine
learning.
• Currently working on a two-part book including
many methods in network science.

3. Networks Are Everywhere!
• Gene networks
• Social networks
• Travel networks
• Communications networks
• Contact tracing networks
• Linguistic networks
• Many more!
Vertex
Edge

4. Network Science Problems
• Network robustness
• Backbone identification
• Spread potential
• Importance of vertices and edges
• Influencers
• Bridges
• Community identification

5. Network Robustness: Spread Potential
• Examples
• Catching a disease
• Disseminating information
• Adopting of new products
• Promoting population-level
behavior change
• Tools
• Modularity measures
• SIR simulations

6. Network Robustness: Backbone Identification
• Examples:
• Resilience to power outages
in electrical grid
• Main spread route in epidemic
• Key connections in
information spread
• Tools:
• Forman-Ricci curvature
• Forman-Ricci flow

7. Importance Metrics: Local Measures
• Examples:
• Identification of neighborhood
influencers
• Identification of information hubs
• Tools:
• Degree centrality
• PageRank centrality
• Betweenness centrality

8. Importance Metrics: Global Measures
• Examples:
• Convergence of functions
imposed on a network to
measure quantities of interest
• Maximum travel distance to
estimate disease timings
• Tools:
• Eccentricity
• Spectral radius

9. Community Identification
• Examples:
• Identification of subgroups of a
connected population for targeted
marketing
• Identification of network pieces that
share similar network properties
• Tools:
• Community-finding algorithms
(Louvain clustering…)
• Clustering on local network metrics (k-
means…)

10. Network Analysis Software
• Python
• igraph
• NetworkX
• R
• igraph
• sna
• netdiffuseR
• Gephi
• GraphStream
• NetMiner4

11. References
• Bapat, R. B. (2010). Graphs and matrices (Vol. 27). London: Springer.
• Barabási, A. L. (2016). Network science. Cambridge university press.
• Lewis, T. G. (2011). Network science: Theory and applications. John Wiley & Sons.
• Newman, M. E. (2002). Spread of epidemic disease on networks. Physical review E,
66(1), 016128.
• Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of
the national academy of sciences, 103(23), 8577-8582.
• Valente, T. W. (2005). Network models and methods for studying the diffusion of
innovations. Models and methods in social network analysis, 28, 98-116.
• Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications
(Vol. 8). Cambridge university press.