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TEMPORAL LATENT SPACE MODELING
for
COMMUNITY PREDICTION
Laboratory for Systems, Software, and Semantics (LS3)
ECIR2020
HOMOPHILY (McPherson et al. 2001)
“Similarity breeds connection.”
2
1. Introduction 2. Proposed Model 3. Evaluation
USER COMMUNITY DETECTION 3
Community
Detection
Temporal
Community
Detection
Community
Prediction
1. Introduction 2. Proposed Model 3. Evaluation
USER COMMUNITY PREDICTION 4
Community
Detection
Temporal
Community
Detection
Community
Prediction
Content
Topic
Links
Topology
1. Introduction 2. Proposed Model 3. Evaluation
TOPICAL USER COMMUNITY PREDICTION 5
Community
Detection
Temporal
Community
Detection
Community
Prediction
Content
Topic
Links
Topology
1. Introduction 2. Proposed Model 3. Evaluation
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
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
TEMPORAL LATENT SPACE MODELING 8@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation
TEMPORAL LATENT SPACE MODELING 9@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation
TEMPORAL LATENT SPACE MODELING 10@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation
TEMPORAL LATENT SPACE MODELING 11@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation
TEMPORAL LATENT SPACE MODELING 12@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation
TEMPORAL LATENT SPACE MODELING 13@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation
TEMPORAL LATENT SPACE MODELING 14@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation
TEMPORAL LATENT SPACE MODELING
Quadratic Loss
15@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation
TEMPORAL LATENT SPACE MODELING
Temporal Smoothness
16@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation
TEMPORAL LATENT SPACE MODELING 17
1. Introduction 2. Proposed Model 3. Evaluation
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
TEMPORAL LATENT SPACE MODELING
local Block Coordinate Gradient Descent (Zhu et al. TKDE
2016)
19
1. Introduction 2. Proposed Model 3. Evaluation
USER COMMUNITY PREDICTION
Vector Cosine Similarity
20
T T + 1
1. Introduction 2. Proposed Model 3. Evaluation
T T + 1
USER COMMUNITY PREDICTION
Louvain Method (Blondel et al. JSTAT 2008)
21
1. Introduction 2. Proposed Model 3. Evaluation
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
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
RESULTS 24
User distribution in communities.
1. Introduction 2. Proposed Model 3. Evaluation
RESULTS 25
User distribution in communities.
1. Introduction 2. Proposed Model 3. Evaluation
FUTURE WORK
Bursty Behavior
26@mary@john@joe
1. Introduction 2. Proposed Model 3. Evaluation
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

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ECIR20: Temporal Latent Space Modeling for Community Prediction

  • 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

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