synthesizing social proximity networks by combining subjective surveys with digital traces
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IEEE C SE2013. Synthesizing Social Proximity Networks by Combining Subjective Surveys with Digital Traces. Christopher Barrett*, Huadong Xia*, Jiangzhuo Chen*, Madhav V. Marathe *, Henning S. Mortveit * and Marcel Salathe # - PowerPoint PPT PresentationTRANSCRIPT
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Synthesizing Social Proximity Networks by Combining Subjective Surveys with Digital Traces
Christopher Barrett*, Huadong Xia*, Jiangzhuo Chen*, Madhav V. Marathe*, Henning S. Mortveit* and Marcel Salathe#
* The Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute at Virginia Tech, USA# Center for Infectious Disease Dynamics, Penn State University, USA
IEEE CSE2013
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We thank our external collaborators and members of the Network Dynamics and Simulation Science Laboratory (NDSSL) for their suggestions and comments.This work has been partially supported by DTRA Grant HDTRA1-11-1-0016, DTRA CNIMS Contract HDTRA1-11-D-0016-0001, NIH MIDAS Grant 2U01GM070694-09, NSF PetaApps Grant OCI-0904844, NSF NetSE Grant CNS-1011769.
Acknowledgement
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• Close proximity relations between people are critical in understanding the diffusion of influenza-like epidemics.
• Those close proximity relations are modeled collectively as a social contact network.
• Existing solutions in constructing social contact networks:– Digital devices to detect proximity between
people: RFID tags, cell phones, motes, etc.– Subjective assessment and survey information
Background: Model Close Proximity Relations Between People
Modeling
Social contact network
Social contact network
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Solution 1: Digital Devices to Detect Proximity Between People
Free of human error
High quality
Expensive
Nontrivial to generalize
700-student contact Network => 1000-student contact Network?
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Solution 2: Subjective Assessment and Survey Information
Complete Graph
G(n,p)
Geometry Random Graph
Subjective Assessment
… …
Inexpensive
Easy to generalize
Sublocation interactions remains a black box
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• A hybrid methodology that combines both subjective surveys and digital traces:– Generic pattern exists in a very small location: conference room, class
room, restaurant at different hours.
• As a Showcase: School networks
New Solution: A Hybrid Methodology
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• Data sets
• Objective 1: understand In-class contact networks– Identifying class intervals– Extracting class networks
• Objective 2: generative network model that model the digital trace network
• Objective 3: comparison study on the impact of detailed sublocation structure
Outline
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• Populations:– NRV population: 150K– High school population: 2.5K
• We collected class schedules for 3 schools in New River Valley Region
Data Sets: Surveys
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• Digital trace data– Collected from an American high school– 788 participants, including 655 students, 73 teachers and 55 staff
members, and 5 other people (94% of the school population)– Each participant carry a mote for an entire typical school day. – Their motes detect other motes within 3 meters for every 20 seconds,
stored as CPRs in the data set• CPR: close proximity records• CPI: close proximity interaction, a continuous sequence of CPRs• Contacts: a contact is the sum of all CPIs between two motes.
– 2,148,991 CPRs, 762,868 CPIs and 118,291 contacts
Data Sets: Digital Trace Data
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• Data sets
• Objective 1: understand In-class contact networks– Identifying class intervals– Extracting class networks
• Objective 2: generative network model that model the digital trace network
• Objective 3: comparison study on the impact of detailed sublocation structure
Outline
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• Formation of school networks:
• Step to identify class networks:– Identify class periods– For each identified class period, identify within-class contact networks
Structure of School Networks
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• Motes are anonymized and the class schedules are unknown.• Mote Signals are highly volatile
– Directional– Unstable device
Challenges (1)
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Classes and Breaks Reveal Quite Different Patterns
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Use the Algorithm to Plot Time Zone for Class Breaks
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Challenges (2): Isolate In-Class Contact Networks
• Interference exists for sensor Signals!– A very large Connected Component for any snapshot contact
networks– Sensor Signals can traverse the wall (via windows/doors)?
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Isolate In-Class Contact Networks
• CPIs within the same class interval comprise a relative stable contact network, even if CPIs are volatile --- foundation for us to analyze
• CPIs traverse across classrooms but we hypothesize:
– CPIs between classrooms are short and unstable An “test and try” algorithm to remove noises
– CPIs between classrooms are sparser than withinModularity based Community Detection Algorithm
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Detect School Communities: Modularity Based Algorithm
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• Students in the class typically form into one or multiple groups; students of the same group are highly connected.
• Duration of CPIs follow a power law like distribution
Analyze In-class Contact Network
47 nodes
21 nodes
32 nodes
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• Data sets
• Objective 1: understand In-class contact networks– Identifying class intervals– Extracting class networks
• Objective 2: generative network model that model the digital trace network
• Objective 3: comparison study on the impact of detailed sublocation structure
Outline
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• G(n,p) model is not appropriate:– Cannot: match degree, match clustering coefficients– Can: match n; match the sum of edge weights by adjusting p
• Chung-Lu model: match both degrees and edge weights– List of degree kv of each node v from a digital trace template– Chung-Lu model connect each node pair (v, u) with probability
where m is the total edge number– We adjust the edge weight for each generated edge, so that the edge
weight follow a power law distribution.• ERGM model:
– more powerful candidate– complex compared to Chung-Lu model
Use Theoretic Graph Models to Fit Digital Trace Templates
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• Spectral Gap of a network: the difference between the largest two eigenvalues of the network adjacency matrix
• A larger spectral gap means the disease is easier to spread on the network.
Compare Spectral Gaps between Digital Trace Templates and Graph Models
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• Data sets
• Objective 1: understand In-class contact networks– Identifying class intervals– Extracting class networks
• Objective 2: generative network model that model the digital trace network
• Objective 3: comparison study on the impact of detailed sublocation structures
Outline
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• Aim: To compare three in-class models within a realistic context, we use the three models to construct three types of high school networks, and further embed school networks within the larger regional network
• Input: – High school populations in NRV region– The NRV population in NRV regions
• Output:– Three types school networks based on three in-class models
respectively– Three types of NRV Network based on three in-class models
respectively
School Networks and the Region Network
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• The school network based on calibrated ChungLu model seems a good match to that based on digital trace templates, structurally.
Structural Properties of School Networks Embedded with Different In-class Models
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Disease Spread in a Social Network
• Within-host disease model: SEIR
• Between-host disease model:– probabilistic transmissions along edges of social contact network– from infectious people to susceptible people
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Simulation to ILI without Intervention
Vaccine high degree nodes Vaccine high degree nodes +social distance
Epidemic Dynamics of School Networks Embedded with Different In-class Models
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ANOVA
peakday Sum of Squares df Mean Square F Significance
Between Groups 14424.800 2 7212.400 3.848 .025*
Within Groups 163069.300 87 1874.360
Total 177494.100 89
Epicurve Difference with Different In-class Models
Multiple Comparisons
Dependent Variable: peakday
Tukey HSD (I) groups (J) groups Mean Difference (I-J) Significance
G(n,p)Digital trace 30.200* .022*
ChungLu 9.000 .701
Digital traceG(n,p) -30.200* .022*
ChungLu -21.200 .146
ChungLuG(n,p) -9.000 .701
Digital trace 21.200 .146*. The mean difference is significant at the 0.05 level.
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• The digital trace based templates capture network structures that are critical in understanding the role of interventions, and not available in previous theoretic sublocation models such as G(n,p)
• It is possible to capture a faithful structural features or dynamics by tuning appropriate theoretic graph models like Chung-Lu to the real digital trace templates, at least under some limited scenarios.
• ERGM could possible serve as a good model, but Chung-Lu model seems like a reasonable fit for now.
Summary of the Comparison Study
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• We show a hybrid methodology that combines subjective survey with digital trace data.
• In-class contact structure is important in understanding epidemics and intervention strategies.
• Our methodology is generic, applicable to other template networks– Office building– Military bases– Hospital rooms– … …
Conclusions
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Questions?
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Extra slides
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Similarity between Community Division
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• Types of classroom organization: teacher-centered or peer-based (internet source: Research Unit for Multilingualism and Cross-Cultural Communication)
Illustration to Class Network Topology Structure
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Construction of a High School Network
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Embed School Networks Within a Larger Regional Network