Transcript Slides PPT

DRIMUX: Dynamic Rumor Influence
Minimization with User Experience in
Social Networks
Biao Wang1, Ge Chen1, Luoyi Fu1, Li Song1, Xinbing Wang1,
Xue Liu2
1Shanghai Jiao Tong University
2McGill University
Email: [email protected]
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Outline
 Introduction of social networks
 Rumor blocking
 Proposed algorithms
 Performance Evaluation
 Conclusion and future work
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Social Networks
 Information sharing and diffusion
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Social Network
 Directed graph: G=(V,E)
 V - Set of vertices, representing users
 E - Set of edges, representing relationships (e.g. user 1
follows user 2) .
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Social Networks
 Innovation propagation
 Information sharing
 Rumor spreading
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Rumor Diffusion
 Viral spreading (large friends circles )
 Causing chaos in society (e.g. ISIS terrorism attack)
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How do we prevent the rumors from
further spreading?
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Outline
 Introduction of social networks
 Rumor blocking
 Proposed algorithms
 Performance Evaluation
 Conclusion and future work
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Rumor Propagation
 SI (Susceptible and Infected) model （with no
recovery）
 IC (Independent Cascade) model
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Pij denotes the probability of node j becoming infected by node i (i, j=1,2,…,7.)
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Rumor Propagation
 How to determine pij ?
 Global popularity
 Topic evolution tendency
Ising model in Physics
 Individual tendency
 Sending probability
 Acceptance probability
pglb (t;k) =
2
k
t2
(1- )
2 k-1
2
t e
k
G( )
2
pij = a pglb (t) + b pind (t),
a +b =1
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Rumor Blocking
 Two strategies:
 Blocking nodes --- Removing all the edges of the
selected node
 Blocking edges --- Removing selected edges
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Rumor Blocking
 Considering real world problem: will users accept being
blocked?
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Rumor Blocking
 User experience utility function
1 N Tth - Tb (u)
Ub = å
N u=1
Tth
 N nodes, each with blockage threshold Tth, which is
 A constant for homogeneous network
 A variable for heterogeneous network
 Tb(u) is the blockage time of node u
 Indicating the average blockage tolerance of whole social
network
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Outline
 Introduction of social networks
 Rumor blocking
 Proposed algorithms
 Performance Evaluation
 Conclusion and future work
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Problem Formulation
Goal
 Minimize the influence of rumor (the number of infected
nodes)
 Constraint of user experience utility
Traditional algorithms fail
 Time critical
 Stochastic topology
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Survival Analysis
Survival theory
 Probability of an event occurring within a time period t
 If the event occurs during t--- “death”; otherwise,
“survival”
0
“Death”
“Death”
t
“Survive”
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Survival Analysis
Survival function
()
(
S t = Pr T >t
)
The probability that a node “survives”
In our context
 User experience utility determines observation time t
 A node “survives” means not being infected
Our goal --- maximizing the likelihood of nodes
“surviving” during the observation time
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Proposed Algorithms
Hazard rate
 Instantaneous occurrence of an event
Pr(t £ T £ t + dt | T > t)
S ¢(t)
=dt®0
dt
S(t)
a v (t | s(t)) = lim
In our context
 Hazard rate: expectation of propagation probability from
precedent infected nodes
a v (t | s(t)) = a vT s(t) =
åa
u:tu <t
uv
puv (t)
 Coefficient matrix: Indicator matrix of the network
A := [a v ] λ N+ ´N
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Proposed Algorithms
 Greedy algorithm
 Each time finding the optimal node to block
 K iterations
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Proposed Algorithms
 Dynamic blocking algorithm
 “Incrementally” finding the optimal node to block
 Each time blocking 2- j ×K nodes
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Outline
 Introduction of social networks
 Rumor blocking
 Proposed algorithms
 Performance Evaluation
 Conclusion and future work
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Performance Evaluation
Datasets
Network extracted from the SinaWeibo, with 23，086
nodes, and 183，549 edges.
 Classic Greedy:
 Greedy algorithm based on descendant order of nodes degree and is used as the
baseline algorithm.
 Proposed Greedy:
 By blocking a node, we can generate a new propagation matrix and reach a new
maximum survival likelihood value.
 Dynamic Algorithm:
 Adjusts to each propagation status, and gradually includes new targeted nodes as
long as the cost is within the scope of tolerable user experience.
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Performance Evaluation
 Vertical dashed line indicates the starting point of blocking
 Left: 54 initial rumor seeds; Right: 32
 K=64, the total number of nodes to be blocked
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Performance Evaluation
 Different block durations vs. Infection ratio of network
 Infection ratio stop decreasing with block duration
 User experience improvement
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Outline
 Introduction of social networks
 Rumor diffusion model
 Rumor blocking algorithms
 Performance Evaluation
 Conclusion and future work
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Conclusion & Future work
Conclusion
 Rumor blocking --- a serious problem in social networks
 User experience --- a realistic issue in social networks
 Survival theory --- maximum likelihood solution
 Dynamic algorithm --- more reasonable and adaptable
Future work
 Network topology --- homogeneous & heterogeneous
 Experiments on more real world large scale datasets
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THANK YOU!
Q&A