1. Luca Lugini Publication by Yingze Wang, Guang Xiang, and Shi-Kuo Chang
Sparse Multi-task Learning for Detecting Influential Nodes in an Implicit Diffusion Network
Luca Lugini
Publication by Yingze Wang, Guang Xiang, and Shi-Kuo Chang

2. Overview Introduction Linear Influence Model Proposed Method: MSLIM
Experiments and Results
Conclusions

3. Introduction Context: social network analysis
Goal of the paper: find the most influential nodes
Why is this important?

4. Linear Influence Model
Vk: Volume of topic k at time t
Mk: Influence indicator matrix (set of nodes propagating topic k at time t)
Iu: Influence function for node u

5. Linear Influence Model
Influence function:
Solution:

6. Limitations of LIM Each topic is independent of each other
topic k
Each topic is independent of each other
Single influence function for each node

7. Proposed Method: Multi-task Sparse Linear Influence Model (MSLIM)
Extend LIM by introducing topic-sensitive influence functions Iu t
Iu1
Iu2
Iuk
. . .

8. Proposed Method: Multi-task Sparse Linear Influence Model (MSLIM)

9. Proposed Method: Multi-task Sparse Linear Influence Model (MSLIM)
Introducing regularization penalties
topic-sensitive
volume prediction
penalty encouraging Iu to be zero
penalty encouraging Iuk to be zero
influential nodes for topic k

10. Experiments and Results
Twitter dataset:
1000 users
3 years of tweets
over 2.6 million tweets
50 topics
Results:

11. Conclusions
Location distribution of influential nodes for topics “Apple” and “Samsung”
Location distribution of influential nodes for topics “Startup” and “Finance”

12. Questions? References:
Wang, Yingze, Guang Xiang, and Shi-Kuo Chang. "Sparse Multi-Task Learning for Detecting Influential Nodes in an Implicit Diffusion Network." AAAI
“Learning with Sparsity for Detecting Influential Nodes in Implicit Information Diffusion Networks”, Yingze Wang, 2014.