The document proposes a temporal latent space model to predict topical user communities in social networks. It models each user's interests over time as a latent vector in a topic space based on their past contributions. To predict future communities, it calculates the similarity between user vectors and clusters them. The model is evaluated on its ability to improve applications like news recommendation and user prediction, since there are no labeled ground truth communities available. Results show the model can better distribute users into meaningful communities.

1. TEMPORAL LATENT SPACE MODELING
for
COMMUNITY PREDICTION
Laboratory for Systems, Software, and Semantics (LS3)
ECIR2020

2. HOMOPHILY (McPherson et al. 2001)
“Similarity breeds connection.”
2
1. Introduction 2. Proposed Model 3. Evaluation

3. USER COMMUNITY DETECTION 3
Community
Detection
Temporal
Community
Detection
Community
Prediction
1. Introduction 2. Proposed Model 3. Evaluation

4. USER COMMUNITY PREDICTION 4
Community
Detection
Temporal
Community
Detection
Community
Prediction
Content
Topic
Links
Topology
1. Introduction 2. Proposed Model 3. Evaluation

5. TOPICAL USER COMMUNITY PREDICTION 5
Community
Detection
Temporal
Community
Detection
Community
Prediction
Content
Topic
Links
Topology
1. Introduction 2. Proposed Model 3. Evaluation

6. oLike-minded users do not know each other  no explicitly linked to each other
oExplicit link does not mean interest similarity  could be due to
• Sociological processes: conformity and/or sociability (Snijders Netw. Sci. 2019)
• Kinship (Diehl et al. AAAI’08)
oLinks are not accessible (Barbieri et al. TIST 2017)
oLinks are misleading or fraudulent due, e.g., link-farming (Labatut et al. ASONAM
2014)
oLink evolution happens at a much lower pace compared to content changes
(Myers et al. WWW’14)
• Links are often not removed when they become effectively ‘dead’.
TOPICAL vs. TOPOLAGICAL 6
1. Introduction 2. Proposed Model 3. Evaluation

7. Given a sequence of users’ contributions towards a set of topics Z
from time interval 1 to T, the goal is to predict topical user
communities in a future interval T+1.
TOPICAL USER COMMUNITY PREDICTION 7
@joe @john @mary
1. Introduction 2. Proposed Model 3. Evaluation

8. TEMPORAL LATENT SPACE MODELING 8@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation

9. TEMPORAL LATENT SPACE MODELING 9@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation

10. TEMPORAL LATENT SPACE MODELING 10@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation

11. TEMPORAL LATENT SPACE MODELING 11@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation

12. TEMPORAL LATENT SPACE MODELING 12@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation

13. TEMPORAL LATENT SPACE MODELING 13@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation

14. TEMPORAL LATENT SPACE MODELING 14@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation

15. TEMPORAL LATENT SPACE MODELING
Quadratic Loss
15@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation

16. TEMPORAL LATENT SPACE MODELING
Temporal Smoothness
16@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation

17. TEMPORAL LATENT SPACE MODELING 17
1. Introduction 2. Proposed Model 3. Evaluation

18. TEMPORAL LATENT SPACE MODELING
local Block Coordinate Gradient Descent (Zhu et al. TKDE
2016)
18@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation

19. TEMPORAL LATENT SPACE MODELING
local Block Coordinate Gradient Descent (Zhu et al. TKDE
2016)
19
1. Introduction 2. Proposed Model 3. Evaluation

20. USER COMMUNITY PREDICTION
Vector Cosine Similarity
20
T T + 1
1. Introduction 2. Proposed Model 3. Evaluation

21. T T + 1
USER COMMUNITY PREDICTION
Louvain Method (Blondel et al. JSTAT 2008)
21
1. Introduction 2. Proposed Model 3. Evaluation

22. EVALUATION
Extrinsic vs. Intrinsic
22
1. Introduction 2. Proposed Model 3. Evaluation
No labeled temporal & topical communities available!
Rand index, Jaccard index, or normalized mutual information (NMI) needs
golden communities
Instead evaluation in the context of applications  Better communities
improve the underlying application
o News recommendation
o User prediction

23. Assumption: a user tweets a news article (mention URL) iff she is
interested in the topics of the article
𝒢 = 𝑢, 𝑛, T + 1 𝑢 ∈ 𝒰, 𝑛 ∈ 𝑛𝑒𝑤𝑠}
oNews Recommendation: 𝑢, ? , T + 1
oUser Prediction: ? , 𝑛, T + 1
~3M ‘en’ tweets by 135,731 users Nov. 1- Dec. 31, 2010 (Abel et al.
UMAP’14)
GOLD STANDARD 23
1. Introduction 2. Proposed Model 3. Evaluation

24. RESULTS 24
User distribution in communities.
1. Introduction 2. Proposed Model 3. Evaluation

25. RESULTS 25
User distribution in communities.
1. Introduction 2. Proposed Model 3. Evaluation

26. FUTURE WORK
Bursty Behavior
26@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation

27. THANK YOU
QUESTIONS
Hossein Fani
Postdoctoral Research Fellow
Laboratory for Systems, Software, and Semantics (LS3)
Ryerson University
Canada
hfani@unb.ca, hossein.fani@ryerson.ca
Ebrahim Bagheri
Associate Professor
Laboratory for Systems, Software, and Semantics (LS3)
Ryerson University
Canada
bagheri@ryerson.ca
Weichang Du
Professor
University of New Brunswick
Canada
du@unb.ca