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

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?

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?

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

Minimizing Seeds 5

Minimizing Propagation Time 6

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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.