Interactive Discovery of Influential Friends from Social Networks By: Behzad Rezaie In the Name of God Professor: Dr. Mashayekhi May 11, 2014 [email protected].

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Interactive Discovery of Influential Friends from Social

NetworksBy:Behzad Rezaie

In the Name of God

Professor:Dr. Mashayekhi

May 11, 2014

[email protected]

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. 824–831.

Jiang F, Leung CKS, Tanbeer SK (2012) Finding popular friends in social networks. In: SCA 2012, pp. 501–508.

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

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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.

𝑃𝑟𝑜𝑚 ( 𝐴𝑛𝑎 ,𝐶𝑎𝑟𝑙𝑜𝑠 )=𝑃𝑟𝑜𝑚 ( 𝐴𝑛𝑎 )+𝑃𝑟𝑜𝑚(𝐶𝑎𝑟𝑙𝑜𝑠 )

2=0.5

𝑃𝑟𝑜𝑚 (𝐺 )=∑𝑖=1

𝑠𝑖𝑧𝑒 (𝐺 )

𝑃𝑟𝑜𝑚( 𝑓 𝑖)

𝑠𝑖𝑧𝑒(𝐺)

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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

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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:• WFIM uses a secondary support threshold to calculate weighted frequent

patterns.

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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)

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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

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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:• 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

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 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