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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delay=50%
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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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