Interactive Discovery of Influential Friends from Social Networks By: Behzad Rezaie In the Name of...
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![Page 1: Interactive Discovery of Influential Friends from Social Networks By: Behzad Rezaie In the Name of God Professor: Dr. Mashayekhi May 11, 2014 brezaie@shahroodut.ac.ir.](https://reader035.fdocuments.in/reader035/viewer/2022070415/5697bfab1a28abf838c9ab9d/html5/thumbnails/1.jpg)
LOGO
Interactive Discovery of Influential Friends from Social
NetworksBy:Behzad Rezaie
In the Name of God
Professor:Dr. Mashayekhi
May 11, 2014
![Page 2: Interactive Discovery of Influential Friends from Social Networks By: Behzad Rezaie In the Name of God Professor: Dr. Mashayekhi May 11, 2014 brezaie@shahroodut.ac.ir.](https://reader035.fdocuments.in/reader035/viewer/2022070415/5697bfab1a28abf838c9ab9d/html5/thumbnails/2.jpg)
Experimental Results
Proposed Method
Problem Description
Introduction
State of the Art
Conclusion
Contents
![Page 3: Interactive Discovery of Influential Friends from Social Networks By: Behzad Rezaie In the Name of God Professor: Dr. Mashayekhi May 11, 2014 brezaie@shahroodut.ac.ir.](https://reader035.fdocuments.in/reader035/viewer/2022070415/5697bfab1a28abf838c9ab9d/html5/thumbnails/3.jpg)
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. 824–831.
Jiang F, Leung CKS, Tanbeer SK (2012) Finding popular friends in social networks. In: SCA 2012, pp. 501–508.
5 % Completed
![Page 4: Interactive Discovery of Influential Friends from Social Networks By: Behzad Rezaie In the Name of God Professor: Dr. Mashayekhi May 11, 2014 brezaie@shahroodut.ac.ir.](https://reader035.fdocuments.in/reader035/viewer/2022070415/5697bfab1a28abf838c9ab9d/html5/thumbnails/4.jpg)
Experimental Results
Proposed Method
Problem Description
Introduction
State of the Art
Conclusion
Contents
![Page 5: Interactive Discovery of Influential Friends from Social Networks By: Behzad Rezaie In the Name of God Professor: Dr. Mashayekhi May 11, 2014 brezaie@shahroodut.ac.ir.](https://reader035.fdocuments.in/reader035/viewer/2022070415/5697bfab1a28abf838c9ab9d/html5/thumbnails/5.jpg)
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
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Experimental Results
Proposed Method
Problem Description
Introduction
State of the Art
Conclusion
Contents
![Page 7: Interactive Discovery of Influential Friends from Social Networks By: Behzad Rezaie In the Name of God Professor: Dr. Mashayekhi May 11, 2014 brezaie@shahroodut.ac.ir.](https://reader035.fdocuments.in/reader035/viewer/2022070415/5697bfab1a28abf838c9ab9d/html5/thumbnails/7.jpg)
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.
15 % Completed
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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.
20 % Completed
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Experimental Results
Proposed Method
Problem Description
Introduction
State of the Art
Conclusion
Contents
![Page 10: Interactive Discovery of Influential Friends from Social Networks By: Behzad Rezaie In the Name of God Professor: Dr. Mashayekhi May 11, 2014 brezaie@shahroodut.ac.ir.](https://reader035.fdocuments.in/reader035/viewer/2022070415/5697bfab1a28abf838c9ab9d/html5/thumbnails/10.jpg)
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
25 % Completed
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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.
𝑃𝑟𝑜𝑚 ( 𝐴𝑛𝑎 ,𝐶𝑎𝑟𝑙𝑜𝑠 )=𝑃𝑟𝑜𝑚 ( 𝐴𝑛𝑎 )+𝑃𝑟𝑜𝑚(𝐶𝑎𝑟𝑙𝑜𝑠 )
2=0.5
𝑃𝑟𝑜𝑚 (𝐺 )=∑𝑖=1
𝑠𝑖𝑧𝑒 (𝐺 )
𝑃𝑟𝑜𝑚( 𝑓 𝑖)
𝑠𝑖𝑧𝑒(𝐺)
30 % Completed
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Proposed Method
𝐼𝑛𝑓 (𝐺 ,𝐿𝐶 )=𝑃𝑟𝑜𝑚 (𝐺 )∗𝐹𝑟𝑒𝑞(𝐺 ,𝐿𝐶)
Inf({Ana, Carlos}, LC) = Prom({Ana, Carlos}) * Freq({Ana, Carlos}, LC) = 0.5 * 4 = 2.0
35 % Completed
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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
40 % Completed
Solution
![Page 14: Interactive Discovery of Influential Friends from Social Networks By: Behzad Rezaie In the Name of God Professor: Dr. Mashayekhi May 11, 2014 brezaie@shahroodut.ac.ir.](https://reader035.fdocuments.in/reader035/viewer/2022070415/5697bfab1a28abf838c9ab9d/html5/thumbnails/14.jpg)
Example
minInf = 2.0
According to prominence value, we have: <Gil, Carlos, Eva, Beto, Fabio, Ana, Davi>
Proposed Method
45 % Completed
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}
![Page 15: Interactive Discovery of Influential Friends from Social Networks By: Behzad Rezaie In the Name of God Professor: Dr. Mashayekhi May 11, 2014 brezaie@shahroodut.ac.ir.](https://reader035.fdocuments.in/reader035/viewer/2022070415/5697bfab1a28abf838c9ab9d/html5/thumbnails/15.jpg)
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
50 % Completed
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Proposed MethodDIFSoN Mining Routine Using PromGMax
55 % Completed
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Proposed MethodEnhanced DIFSoN Mining Routine Using PromLMax
60 % Completed
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Proposed Method
65 % Completed
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Experimental Results
Proposed Method
Problem Description
Introduction
State of the Art
Conclusion
Contents
![Page 20: Interactive Discovery of Influential Friends from Social Networks By: Behzad Rezaie In the Name of God Professor: Dr. Mashayekhi May 11, 2014 brezaie@shahroodut.ac.ir.](https://reader035.fdocuments.in/reader035/viewer/2022070415/5697bfab1a28abf838c9ab9d/html5/thumbnails/20.jpg)
Experimental Results
WFIM vs. DIFSoN
WFIM is an FP-tree based weighted frequent pattern mining algorithm that requires two database scans.
Differences:• WFIM uses a secondary support threshold to calculate weighted frequent
patterns.
70 % Completed
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Experimental Results
Datasets• IBM synthetic datasets
T10I4D100K (http://www.almaden.ibm.com/cs/quest or http://www.cs.loyola.edu/*cgiannel/assoc_gen.html)
• Real datasets Mushroom (http://fimi.ua.ac.be/data) Pumsb (http://fimi.ua.ac.be/data) Kosarak (http://fimi.ua.ac.be/data)
75 % Completed
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Experimental Results
Runtime
80 % Completed
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Experimental Results
Compactness of the IF-tree
85 % Completed
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Experimental Results
Scalability of the DIFSoN
90 % Completed
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Experimental Results
Proposed Method
Problem Description
Introduction
State of the Art
Conclusion
Contents
![Page 26: Interactive Discovery of Influential Friends from Social Networks By: Behzad Rezaie In the Name of God Professor: Dr. Mashayekhi May 11, 2014 brezaie@shahroodut.ac.ir.](https://reader035.fdocuments.in/reader035/viewer/2022070415/5697bfab1a28abf838c9ab9d/html5/thumbnails/26.jpg)
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.
95 % Completed
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100 % Completed!!!
Conclusion
Results show that:• the IF-tree is compact and space efficient• the tree-based mining routine within the DIFSoN model
is fast and scalable for both sparse and dense data
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Any Questions?
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Thank You So much
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References
Cameron JJ, Leung CKS, Tanbeer SK (2011) Finding strong groups of friends among friends in social networks. In: SCA 2011, pp 824–831
Jiang F, Leung CKS, Tanbeer SK (2012) Finding popular friends in social networks. In: SCA 2012, pp 501–508
Leung CKS, Medina IJM, Tanbeer SK (2013) Analyzing socialnetworks to mine important friends. In: Social media mining andsocial network analysis: emerging research, pp 90–104