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

2 Fractional Immunization in Hospital-transfer Graphs B. Aditya Prakash 1, Lada A. Adamic 2, Theodore Iwashyna 2, Hanghang Tong 3, Christos Faloutsos 1 1 Carnegie Melon University, 2 University of Michigan, 3 IBM

3 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 two settings

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

Fractional Asymmetric Immunization 5 Fractional Effect Asymmetric Effect

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

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

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

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

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

Problem Statement 11 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

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

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!

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

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

simulation results 16

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

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 18

Time series analysis of network co-evolution 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? Chun-Yuen Teng, Liuling Gong, Avishay Livne, Lada Adamic Twitter data

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

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

The role of groups in information diffusion 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 David Huffaker, Chun-Yuen Teng, Liuling Gong, Matthew Simmons, Lada Adamic

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

cultural differences in co-evolving communication patterns corporate communication – In Asia, individuals use different channels for different contacts Jiang Yang 1, Zhen Wen 2, Lada Adamic 1, Mark Ackerman 1 1 U. Michigan, 2 IBM

cultural differences in sentiment expression