Transcript Document
Science & Research Breanch
Islamic Azad University
Limiting the Spread of Misinformation
in Social Networks
Providers : A.Shekari
E.Ehsani
E.Golzardi
Dear Professor : Mr.Sheykh Esmaili
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Table of Contents
Introduction
Related Work
Diffusion OF Misinformation
Eventual Influence Limitation
Evaluation
EIL With Incomplete Data
Conclusion
Strong & Week Points
Refrence
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Introduction
Online social networks have many benefits
social networks can be very beneficial
It can also have disruptive effects
social networks are the main source of news for many people today
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Table of Contents
Introduction
Related Work
Diffusion OF Misinformation
Eventual Influence Limitation
Evaluation
EIL With Incomplete Data
Conclusion
Strong & Week Points
Refrence
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Related Work
The experimental results show that :
the greedy approach performs better than the heuristics
the best strategy for the first player is to choose high degree nodes
the first player, the first to decide, is not always advantageous
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Table of Contents
Introduction
Related Work
Diffusion OF Misinformation
Eventual Influence Limitation
Evaluation
EIL With Incomplete Data
Conclusion
Strong & Week Points
Refrence
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Diffusion OF Misinformation
A social network can bemodeled as a directed graph G = (N,E)
A node w is a neighbor of a node v if and only if there is ev,w Ɛ
E, an edge from v to w in G.
pv,w is assigned to each edge ev,w
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Diffusion Modle
Independent cascade model (ICM)
Multi-Campaign Independent Cascade Model (MCICM)
Campaign-Oblivious Independent Cascade Model (COICM)
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Table of Contents
Introduction
Related Work
Diffusion OF Misinformation
Eventual Influence Limitation
Evaluation
EIL With Incomplete Data
Conclusion
Strong & Week Points
Refrence
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Eventual Influence Limitation
Given a network and the MCIC Model, campaign C spreading
bad information is detected with delay r
budget k, select AL as seeds for initial activation with the
limiting campaign L
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Table of Contents
Introduction
Related Work
Diffusion OF Misinformation
Eventual Influence Limitation
Evaluation
EIL With Incomplete Data
Conclusion
Strong & Week Points
Refrence
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Problem Definition
Problem Definition
One strategy to deal with a misinformation campaign is to limit the
number of users
we will assume that the spread of influence for campaign C starts from
one node n and at that point campaign L is initiated
we will focus on minimizing the number of nodes
We refer to this problem as the eventual influence limitation problem
(EIL)
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General Purpose
limiting the influence of a misinformation campaign
Submodularity Of EIL
f(S U V) – f(S) >= f(T U V)-f(T)
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General Purpose
for all elements v and all pairs of sets S ⊂ T
as high as the marginal gain from adding the same element to
a superset of S
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General Purpose
The greedy hill-climbing
starting with the empty set, and repeatedly adding an
element that gives the maximum marginal gain
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Evaluation
in our specific problem each simulation involves the expensive
computation of shortest paths which is crucial to EIL and this
makes EIL even more computationally
intense then the influence maximization problems
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Evaluation
In this Figure we present our evaluation of the 4 methods on
MCICM
delay=20%
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delay=50%
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Evaluation
Figure 4: Evaluation for COICM
delay=20%
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Table of Contents
Introduction
Related Work
Diffusion OF Misinformation
Eventual Influence Limitation
Evaluation
EIL With Incomplete Data
Conclusion
Strong & Week Points
Refrence
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EIL With Incomplete Data
sets of active, inactive and newly activated nodes for
campaign C be denoted Agiven , Igiven & Ngiven respectively
Apred , Ipred & Npred
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Prediction Algorithms
1. Identifying A and I
Three reasons : 1) for identifying newly activated nodes
2,3) storage , Incorrectly identified
A good heuristic should : 1) nodes in Ap should form a connected component
2) have as many arborescences as possible
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Prediction Algorithms(cont.)
1: {Given (Agiven, Igiven,G,Ca) where G= (N,E) is the network graph,
Agiven, Igiven are the incomplete sets of active and inactive nodes
and Ca is an approximate value of |A| }
2: Apred = Agiven
3: Create a refined graph G ′ that consists of nodes in N − I given
4: Select a node ni at random from A
5: Tstein = min Steiner tree rooted at ni in G ′ covering Agiven
6: Nstein = nodes in Tstein
7: Apred = Apred [ Nstein
8: while |Apred| < Ca do
9: Nchoose = ni ∈ N − Igiven − Apred
10: Apred = Apred [ {argmaxn ∈ Nchoose {deg(n) Apred}}
11: Output Apred
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Ipred = N - Apred
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Prediction Algorithms(cont.)
2. Identifying N
1) In set Apred
Shortest average path bfs the leaves
2) Random spanning tree on the Gpred the leaves
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Predictive Hill Climbing Approach (PHCA)
Agiven , Igiven & Ngiven
Identify ALP , the set of k nodes to influence by campaign L in
graph G
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Evaluation of PHCA
Accuracy, precision and recall statistics
Accuracy refers to the ratio of the nodes whose true states are correctly
identified
Precision refers to the ratio of nodes that are active
recall refers to the ratio of nodes identified as active to the total number
of active nodes
with decreasing Pknown = Greater amount of missing information
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Evaluation of PHCA(cont.)
Select the nodes that are unknown to be infected
(a) Delay = 30%
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(b) delay = 70%
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Table of Contents
Introduction
Related Work
Diffusion OF Misinformation
Eventual Influence Limitation
Evaluation
EIL With Incomplete Data
Conclusion
Strong & Week Points
Refrence
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Conclusion
Introduced PHCA algoritm
Predicts the all the nodes of the network
Then uses the PHCA to choose the set of influentials using the
predicted data
PHCA provides good performance, within 96-90% that would
be achieved with no missing information
For large delays the performance degrades to 75%
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Table of Contents
Introduction
Related Work
Diffusion OF Misinformation
Eventual Influence Limitation
Evaluation
EIL With Incomplete Data
Conclusion
Strong & Week Points
Refrence
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Strong Points
Choose influentials largely achieved with no missing information
Inactive nodes correct result
Provider Two Model : MCICM & COCIM
Paper presented with proof
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Weak Points
We have identified more with the possibilitie
Results are not correct
Work on a synthetic graph
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Table of Contents
Introduction
Related Work
Diffusion OF Misinformation
Eventual Influence Limitation
Evaluation
EIL With Incomplete Data
Conclusion
Strong & Week Points
Refrence
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Refrence
[1] C. Budak, D. Agrawal, A. El Abbadi,” Limiting the Spread of Misinformation in Social
Networks”,Department of Computer Science University of California, Santa
Barbara, Santa Barbara CA 93106-5110, USA
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