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U. Michigan participation in EDIN Lada Adamic, PI

U. Michigan participation in EDIN Lada Adamic, PI. E 2.1 fractional immunization of networks E 2.1 time series analysis approach to correlating structure and content, and co-evolving structure E 2.3 role of groups in information diffusion E 2.3 cultural differences in communication structure.

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U. Michigan participation in EDIN Lada Adamic, PI

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  1. U. Michigan participation in EDINLada Adamic, PI • E 2.1 fractional immunization of networks • E 2.1 time series analysis approach to correlating structure and content, and co-evolving structure • E 2.3 role of groups in information diffusion • E 2.3 cultural differences in communication structure INARC

  2. Fractional Immunization in Hospital-transfer Graphs B. Aditya Prakash1, Lada A. Adamic2, Theodore Iwashyna2,Hanghang Tong3, Christos Faloutsos1 1Carnegie Melon University, 2University of Michigan,3IBM

  3. two settings hospital setting • Hospitals harbor highly resistant bacteria • These bacteria can hitch a ride when patients are transferred from hospital to hospital communication network setting • individuals may propagate misinformation or malicious computer viruses

  4. one problem complete immunization is not feasible • all prior work on immunization on networks assumes complete immunization our approach: fractional immunization • allocating resources to nodes reduces their probability of becoming infected • e.g. allocating r units of resource corresponds to reducing Prob(infection) to

  5. Fractional Asymmetric Immunization Fractional Effect Asymmetric Effect

  6. Fractional Asymmetric Immunization  Edge weakened by half Fractional Effect [ f(x) = ] Asymmetric Effect

  7. Fractional Asymmetric Immunization   Only incoming edges Fractional Effect [ f(x) = ] Asymmetric Effect

  8. Fractional Asymmetric Immunization # antidotes = 3 Fractional Effect [ f(x) = ] Asymmetric Effect

  9. Fractional Asymmetric Immunization # antidotes = 3 Fractional Effect [ f(x) = ] Asymmetric Effect

  10. Fractional Asymmetric Immunization # antidotes = 3 Fractional Effect [ f(x) = ] Asymmetric Effect

  11. Problem Statement • Hospital-transfer networks • Number of patients transferred • Given: • The SI model • Directed weighted graph • A total of k antidotes • A weakening function f(x) • Find: • the ‘best’ distribution which minimizes the “footprint” at some time t

  12. Naïve way • How to estimate the footprint? • Run simulations? • too slow • takes about 3 weeks for graphs of typical size!

  13. Our Solution – Main Idea • The SI model has no threshold • any infection will become an epidemic • But • can bound the expected number of infected nodes at time t • Get the distribution which minimizes the bound!

  14. Our Solution – Main Idea • NP-complete! • We give a fast, effective near-optimal algorithm - GreedyResync • O(km/r + kN)

  15. Simulations US-MEDICARE Hospital Patient Transfer network Our algorithm, near optimal Lower is better

  16. simulation results

  17. Resource allocation few ICU beds fewer resources many ICU beds more resources

  18. fractional immunization: summary • Targeted resource allocation is 16x more effective than uniform • Best strategy: heavily concentrate resources at a few particularly important hospitals • Greedy algorithm is near-optimal

  19. Time series analysis of network co-evolution Chun-Yuen Teng, Liuling Gong, AvishayLivne, Lada Adamic • Can the evolution of network structure reveal attributes of the content? • imagine that pattern of who communicates with whom is easy to discern, but acquiring content is costly (paying informant, decrypting, etc.) • Can the structure suggest when it would be appropriate to • Can the evolution of one network predict how another network over the same nodes will evolve in the future? Twitter data

  20. contemporaneous correlation between structure and content correlation between textual and structural features predicting the similarity between non-linked pairs using textual and structural variables

  21. measuring co-evolution • temporal conductance • degree of unexpectedness • recent and frequent edges, or those that close recent and frequent paths, are expected • Second Life data: • low conductance (network is novel) corresponds to lower entropy in exchanged assets • “free” asset transfer network time series predicts, via temporal conductance, paid transaction time series

  22. The role of groups in information diffusion David Huffaker, Chun-Yuen Teng, Liuling Gong, Matthew Simmons, Lada Adamic • Main findings: • group variables help to explain adoption • e.g. overlap of groups an individual and previous adopters belong to • group variables are more predictive than # of adopting contacts, etc. • group structure is predictive of amount of exchange • e.g. higher clustering

  23. group structure conducive to exchange low rates of adoption high rates of adoption

  24. cultural differences in co-evolving communication patterns Jiang Yang1, Zhen Wen2, Lada Adamic1, Mark Ackerman1 • corporate communication • In Asia, individuals use different channels for different contacts 1U. Michigan, 2IBM

  25. cultural differences in sentiment expression

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