1. Non-competitive VM – Parting Shots Where do we go from here?

2. Life isn’t always progressive Product purchase, technology/innovation adoption, political campaigns are reasonably approximated by progressive models. However, subscription of services (ISP, mobility, apps) is often non-progressive: subscribers may switch in and out. How do you model and what do you optimize?

3. Spread isn’t the only thing that matters (Expected) revenue, or better yet profit is what a business cares about. Influence doesn’t always imply adoption. What if we wanted to minimize the seeding expenses to achieve a given target spread? What if we wanted to achieve a given target spread under a given seeding expense budget in the quickest possible time?

4. Other Perspectives on Influence Propagation Minimizing Budget: Given a target expected spread, find the smallest seed set that achieves the target. Minimizing Propagation Time: Given a target expected spread and a budget on #seeds, find the best seed set under budget that achieves the target in the least possible time. 4

5. Minimizing Seeds 5

6. Minimizing Propagation Time 6

7. 7

8. Summary Spread, revenue, profit, adoption – hard to optimize on two levels. In addition to n/w structure, influence probs matter and need to be learned properly from data. Greedy + MC simulation + CELF++ -- guaranteed approx. but doesn’t scale. Replacing simulation by SimPath/PMIA etc. dramatic speedup with little loss of accuracy on various data sets. Credit distribution model allows to predict spread from data directly. MINTSS, MINTIME – very hard problems in general. Potential for further research. Non-progressive – some recent progress! Competition under non-progressive models – largely unexplored territory.