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…)
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.