1. Maximizing the Spread of Influence through a Social Network
Kempe, Kleinberg, Tardos 2003
Ellie Clougherty

2. Name: Ellie Clougherty
Goal: Which subset of individuals are the most influential? How can we select a subset of nodes that will have the greatest impact?
Name: Ellie Clougherty

7. Can you find degree centrality?
distance centrality?

8. Network Data
Co-authorships of high-energy physics papers Node: Author Edges: Degree of co-authorship

9. Goal: Select the most influential nodes
An NP-Hard optimization problem!
Solution:
Naturally-greedy, hill-climbing, submodular algorithm obtains 63% of the optimal solution

10. Weight: Number of Edges / Co-Authorships
Two Models
Weight: Number of Edges / Co-Authorships
Linear Threshold Model A node becomes active when the total weight of it’s active neighbors exceeds a random threshold Independent Cascade Model An activated node has one chance to activate an inactive neighbor with a weighted probability

12. Also takes into account degree: edges with high degree nodes are assigned smaller probabilities

15. Context Matters
Consider the dynamics of information in a network, not just the structure
Weighting the influence of one node to another depends on the context and nature of the data
Future Steps:
Generalized Framework: combine both models
Non-progressive models: activation is bi- directional

17. Additional Sources
Node Grid: Indian Grid: indias-electric-grid John Kelly Protesting: assault-law-now-includes-language-on-same-sex-violence UVA Protestors: university-of-virginia-to-hold-special-meeting.html?smid=fb- nytimes&smtyp=cur&bicmp=AD&bicmlukp=WT.mc_id&bicmst= &bic met= EM Slide: els%20II.pdf Truthy: Business Insider: 5