LOGO
Interactive Discovery of Influential Friends from Social
NetworksBy:Behzad Rezaie
In the Name of God
Professor:Dr. Mashayekhi
May 11, 2014
Experimental Results
Proposed Method
Problem Description
Introduction
State of the Art
Conclusion
Contents
State of the Art
Cameron JJ, Leung CKS, Tanbeer SK (2011) Finding strong groups of friends among friends in social networks. In: SCA 2011, pp. 824โ831.
Jiang F, Leung CKS, Tanbeer SK (2012) Finding popular friends in social networks. In: SCA 2012, pp. 501โ508.
5 % Completed
Experimental Results
Proposed Method
Problem Description
Introduction
State of the Art
Conclusion
Contents
Introduction
Social networks have become popular to facilitate collaboration and knowledge sharing among users
Interactions or interdependencies among users are deeply important in social networks
Such interactions or interdependencies can be dependent on or influenced by user characteristics such as connectivity, centrality, weight, importance, and activity in the networks
10 % Completed
Experimental Results
Proposed Method
Problem Description
Introduction
State of the Art
Conclusion
Contents
Problem Description
A Facebook user may want to identify those prominent friends who have high impact (e.g., in terms of knowledge or expertise about a subject matter) in the social network.
A LinkedIn user may want to get introduced to those second-degree connections who have rich experience in some profession.
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Problem Description
Finding influential friends from social networks may also help corporations and business organizations in making important business decisions.
A Twitter use may also be interested in following (and subscribing to a Twitter feed from) those who are highly influential in the whole network.
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Experimental Results
Proposed Method
Problem Description
Introduction
State of the Art
Conclusion
Contents
Proposed Method
: list of friends}: a group of k friendsLC: a list collection of friends in GFreq(G, LC): frequency of G in LC
G = {Ana, Carlos} LC = {L1, L2, L5, L7} Freq(G, LC) = 4
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Proposed Method
The prominence, which is represented by a non-negative number, indicates the status (such as importance, weight, value, reputation, belief, position, or significance) of a friend in a social network.
๐๐๐๐ ( ๐ด๐๐ ,๐ถ๐๐๐๐๐ )=๐๐๐๐ ( ๐ด๐๐ )+๐๐๐๐(๐ถ๐๐๐๐๐ )
2=0.5
๐๐๐๐ (๐บ )=โ๐=1
๐ ๐๐ง๐ (๐บ )
๐๐๐๐( ๐ ๐)
๐ ๐๐ง๐(๐บ)
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Proposed Method
๐ผ๐๐ (๐บ ,๐ฟ๐ถ )=๐๐๐๐ (๐บ )โ๐น๐๐๐(๐บ ,๐ฟ๐ถ)
Inf({Ana, Carlos}, LC) = Prom({Ana, Carlos}) * Freq({Ana, Carlos}, LC) = 0.5 * 4 = 2.0
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Proposed Method
When mining frequent patterns, the frequency measure satisfies the downward closure property: if a pattern is frequent, then all its subsets are also frequent. Equivalently, if a pattern is infrequent, then all its supersets are also infrequent.
Influence does not satisfy the downward closure property. minInf = 2.0Inf({Carlos}) = 4 * 0.4 = 1.6Inf({Ana, Carlos}) = 4 * 0.5 = 2.0
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Solution
Example
minInf = 2.0
According to prominence value, we have: <Gil, Carlos, Eva, Beto, Fabio, Ana, Davi>
Proposed Method
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L1 = {Carlos, Eva, Beto, Ana}L2 = {Carlos, Beto, Ana}L3 = {Eva, Beto, Fabio}L4 = {Beto, Ana, Davi}L5 = {Carlos, Eva, Beto, Ana}L6 = {Eva, Beto, Fabio}L7 = {Carlos, Eva, Beto, Ana}
Proposed Method L1 = {C, E, B, A}
L2 = {C, B A}L3 = {E, B, F}L4 = {B, A, D}L5 = {C, E, B, A}L6 = {E, B, F}L7 = {C, E, B, A}
IF-tree construction
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Proposed MethodDIFSoN Mining Routine Using PromGMax
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Proposed MethodEnhanced DIFSoN Mining Routine Using PromLMax
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Proposed Method
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Experimental Results
Proposed Method
Problem Description
Introduction
State of the Art
Conclusion
Contents
Experimental Results
WFIM vs. DIFSoN
WFIM is an FP-tree based weighted frequent pattern mining algorithm that requires two database scans.
Differences:โข WFIM uses a secondary support threshold to calculate weighted frequent
patterns.
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Experimental Results
Datasetsโข IBM synthetic datasets
T10I4D100K (http://www.almaden.ibm.com/cs/quest or http://www.cs.loyola.edu/*cgiannel/assoc_gen.html)
โข Real datasets Mushroom (http://fimi.ua.ac.be/data) Pumsb (http://fimi.ua.ac.be/data) Kosarak (http://fimi.ua.ac.be/data)
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Experimental Results
Runtime
80 % Completed
Experimental Results
Compactness of the IF-tree
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Experimental Results
Scalability of the DIFSoN
90 % Completed
Experimental Results
Proposed Method
Problem Description
Introduction
State of the Art
Conclusion
Contents
Conclusion
DIFSoN comprises the IF-tree and a mining routine.
Although the notion of influential friends does not satisfy the downward closure property, we addressed this issue using the global maximum prominence values of users.
To enhance the model, we proposed to use the local maximum prominence values.
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100 % Completed!!!
Conclusion
Results show that:โข the IF-tree is compact and space efficientโข the tree-based mining routine within the DIFSoN model
is fast and scalable for both sparse and dense data
Any Questions?
Thank You So much
References
Cameron JJ, Leung CKS, Tanbeer SK (2011) Finding strong groups of friends among friends in social networks. In: SCA 2011, pp 824โ831
Jiang F, Leung CKS, Tanbeer SK (2012) Finding popular friends in social networks. In: SCA 2012, pp 501โ508
Leung CKS, Medina IJM, Tanbeer SK (2013) Analyzing socialnetworks to mine important friends. In: Social media mining andsocial network analysis: emerging research, pp 90โ104