Download Orbital Mobility Profiles and it`s Applications

Spatiotemporal Data Mining
on Networks
Taehyong Kim
Computer Science and Engineering
State University of New York at Buffalo
University of Buffalo The State University of New York
Table of Contents
 Introduction
 Overview
 Networks Data Mining
 Spatiotemporal Data
Mining
 Applications
 Quality of Bone
(osteoporosis) as a
Network Dynamics
 Amazon Deforestation
 Studies
 Spreading and Defense
model in Networks
 Fixed-random network
 Spreading Model
 Defense Model
 Avian Influenza
Outbreaks
 Modeling
 Mining parameters
University of Buffalo The State University of New York
Overview
 Most of real world relationships and communications
could be represented on networks (graphs).
 Understanding the behavior of such systems starts
with understanding the topology of the
corresponding network.
Collaboration network
Yeast PPI network
University of Buffalo The State University of New York
AT&T Web Network
Overview
 Recent studies on various networks
 Social network
 Author network, School relationship Network
 Technical network
 Cell network, Internet, Electric power network
 Biological network
 Protein network, Metabolic network, Disease Network
 Focuses on network attributes
 Number of nodes and edges
 Weight on nodes and edges
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Overview
 nodes and edges
Bridge node
edge
Hub node
node
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Networks Data Mining
 Networks Data mining has been done
 Prediction of unknown protein functions in proteinprotein interaction networks
 Resilience test of networks against attacks
 Prediction of people relationships in social
networks
 Drug targeting on cell networks
 Etc.
University of Buffalo The State University of New York
Spatiotemporal Data Mining
 Networks are changed as time goes by
 World wide web is evolving by itself
 Interactions among proteins are changed in PPI
networks
 Size of cities and inter-state free ways are
changed
 Structure of bone is changed
 Information of location and time is also
important factors for further understanding on
any given networks
University of Buffalo The State University of New York
Spatiotemporal Data Mining
 Spatiotemporal Data Mining: knowledge
extraction from large spatiotemporal
repositories in order to recognize
behavioural trends and spatial patterns for
prediction purposes
 What is the relationship between the spread of
epidemics and the number and location of houses
and schools by time?
 What is the connection between the size of
Buffalo city and thruway traffics on I-90 by an
year?
University of Buffalo The State University of New York
Spatiotemporal Data Mining
Normal
Osteoporosis
Drugs
University of Buffalo The State University of New York
Amazon Deforestation 2003
Deforestation 2002/2003
Deforestation until 2002
University of Buffalo The State University of New York
Fonte: INPE PRODES Digital, 2004.
Amazon in 2015?
University of Buffalo The State University of New York
fonte: Aguiar et al., 2004
Modelling Complex Problems
 Application of interdisciplinary knowledge to
produce a model.
If (... ? ) then ...
Desforestation?
University of Buffalo The State University of New York
Table of Contents
 Introduction
 Overview
 Networks Data Mining
 Spatiotemporal Data
Mining
 Applications
 Quality of Bone
(osteoporosis) as a
Network Dynamics
 Amazon Deforestation
 Studies
 Spreading and Defense
model in Networks
 Fixed-random network
 Spreading Model
 Defense Model
 Avian Influenza
Outbreaks
 Modeling
 Mining parameters
University of Buffalo The State University of New York
Spreading and Defense model in
Networks
 Fixed-radius random network




Cellular transmission tower
Interstate free ways
Epidemics on communities
Sensor networks
 How we can defend if there are attacks or
breaks from the center of the networks?
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Fixed Radius Random Network
 400 random points on 1*1 square unit
 Calculating distance between each point
 If two points are in a certain radius, creating
an edge between points
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Fixed Radius Random Network
 Fixed-radius of random
network (r = 0.01 ~ 0.14)
Fixed-Radius
400 nodes, 2366 edges
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Simulation on network
 Network dynamics are studied based on
fixed-radius random network
 Simple spreading model and defense model
is implemented for simulation
 Mining important parameters on this model of
network dynamics
 Mining optimal values of parameters on this
model of network dynamics
University of Buffalo The State University of New York
Spreading Model
 Simulating disease spreading or message
spreading
 Starting from center point (0.5*0.5)
 Affecting edges which are in a spreading
radius (ROI) from center
 Spreading radius grows or reduces based on
how many edges are damaged
University of Buffalo The State University of New York
Spreading Model
 Region of radial
distance of spreading
model (ROIt=0 = 0.1)
 Spreading starts from
center (0.5, 0.5)
ROI
Center
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Spreading Model
 Probability of affecting
rate of edges (Pa = 0.33)
 11 edges are in ROI
 In this case, 4 out of 11
edges are affected
(Spreading will affect
edges about 33%
probability)
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ROI
Defense Model
 Simulating defense system of disease
spreading or message spreading
 Signaling to neighbor nodes in order to inform
(disease) spreading
 Activated when the affection of spreading (#
of signals from neighbor nodes) is over
threshold
 Removing edges which are in a radius (f)
from activated neighbor nodes in order to
stop spreading
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Defense Model
 Circular region of
programming Cell
Death (f=0.2~3.6)
 When signals from
neighbor nodes are
over the Td, edges in
the circular region are
removed by defense
process
Region of defense process
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Defense Model
 Probability of
Programming Cell
Death (Pp = 1)
 If Pp is 1, all edges in
circular regions are
dead
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Result (visualization)
Time 
Time: 0
Time: 10
Total Damage
Intermediate
Contained
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Time: 50
Average Fractional of Edges Damaged
Result
A
1
0.8
0.6
0.5
0.4
0.4
0.3
0.2
0.2
0
0
0.06
0.12
0.18
0.24
f
University of Buffalo The State University of New York
0.3
0.36
Result
A
Average Fractional Damage
1
0.8
0.6
Total
0.4
Defense
0.2
Offense
0
0
0.05
0.1
0.15
0.2
0.25
f
University of Buffalo The State University of New York
0.3
0.35
Summary
 Containment strategy on epidemics and virus
spreads
 Mining important parameters
 Mining optimal values of important parameters
 Understanding dynamics on human tissues
and bones
 Development of diseases (osteoporosis)
 Drug effects on cell networks
University of Buffalo The State University of New York
Table of Contents
 Introduction
 Overview
 Networks Data Mining
 Spatiotemporal Data
Mining
 Applications
 Quality of Bone
(osteoporosis) as a
Network Dynamics
 Amazon Deforestation
 Studies
 Spreading and Defense
model in Networks
 Fixed-random network
 Spreading Model
 Defense Model
 Avian Influenza
Outbreaks
 Modeling
 Mining parameters
University of Buffalo The State University of New York
Avian Influenza
 AI outbreaks are frequently occurring around the
world recently
 H5N1 type has high infection and mortality rate
 Chickens and ducks are main victims of AI
 Mortality rate of H5N1 could reach 90-100% within 48
hours
 Threat from AI has greatly increased for human
beings
 There are several reports showing human infection of AI
 People could get infected by contacting excretion of
contaminated birds
University of Buffalo The State University of New York
AI outbreaks
 Outbreaks in South Korea 2008
University of Buffalo The State University of New York
AI outbreaks
 Outbreaks in South Korea 2008
4 days
28 days
12 days
36 days
20 days
44 days
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Challenges
 Strategies are needed for AI containment
 Early identification of the first cluster of cases
 Warning system from contaminated area to neighbor areas
are needed
 Effective quarantine plan should be existed
 Containment model helps plan effective strategies
 Prediction of damage with certain environment parameters
 Mining important parameters to control outbreaks
 Measurement of effective values of important parameters
University of Buffalo The State University of New York
Modeling
 A group of chickens and ducks are nodes
 2231 nodes for a group of chickens and 808 nodes for a
group of ducks
 76 (1x1 square) units (1 unit = 37.5 Km)
 Parameters
 A node can interact with other nodes in range g
 A susceptible node become a infected node by infection
probability t
 A Infected node become a activated node by incubation
period m and n
 Nodes are culled in quarantine radius l
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Modeling
300Km
37.5Km
487.5Km
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Visualization
 Visualization of simulations based on AI outbreaks in
South Korea 2008
4 days
14 days
24 days
34 days
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44 days
Important Parameters
 Effect of Increased Quarantine Range
 Quarantine radius: 0.0 ~ 0.32 unit
 Effects of Increased Incubation Period
 Incubation Period: 0 ~ 17 days
 Effects of Increasing the Infection probability
 Infection probability: 0.0 ~ 1.0
University of Buffalo The State University of New York
Quarantine Radius
 Effect of Increased Quarantine Radius
 Quarantine radius: 0.0 ~ 0.32 unit
 Infection probability: 0.1, 0.4, 0.7 and 1.0
 Research on effective quarantine radius by
Infection probability
 Optimal quarantine radius
Infection
Probability
0.1
0.4
0.7
1.0
Optimal
Radius
0.04
0.10
0.16
0.22
University of Buffalo The State University of New York
Quarantine Radius
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Incubation Period
 Effects of Increased
Incubation Period
 Incubation Period: 0 ~ 17
days
 Quarantine Range: 0.0,
0.04, 0.11 and 0.18 unit
 For mid level control, almost
89% of poultry farms are
healthy when incubation
period is one day whereas
only 11% of poultry farms
are healthy when incubation
period is 17 days.
University of Buffalo The State University of New York
Infection probability
 Effects of Increasing
the Infection probability
 Infection probability: 0.0
~ 1.0
 Quarantine Range: 0.0,
0.04, 0.11 and 0.18 unit
 The large numbers of
poultry farms eliminated
by the aggressive culling
procedure with max
control
University of Buffalo The State University of New York
Summary
 Modeling AI dynamics based on statistic data
 Modeling of AI outbreaks and spreads
 Modeling of defense strategies
 Mining important parameters and values in
order to contain AI outbreaks in early stage
 Quarantine radius, infection rate, incubation
period
 Damage predictions with important parameters
 Mining defense strategies for future outbreaks
University of Buffalo The State University of New York
Thank you!
University of Buffalo The State University of New York