0.25
0.5
0.75
1.25
1.5
1.75
2
Classification and Clustering in Large Complex Networks
Published on Jun 14, 20118152 Views
Data represented as graphs with complex structures are ubiquitous nowadays. Examples include technological networks (e.g., the Internet), informational networks (e.g., the World-Wide Web), social n
Related categories
Chapter list
Classification and Clustering in Large Complex Networks00:00
Wordle TM says ...00:07
Complex Networks are Ubiquitous00:59
Problems / Applications (1)02:11
Outline (1)04:26
Relational Classifiers04:39
Within-Network Classification05:44
Background: Previous Work on Within-Network Classification06:45
Our Work Addresses Two Challenges for Within-Network Classification07:43
Limitations of Existing Approaches for Within-Network Classification08:12
Our Solution: Ghost Edge Classifiers Have Consistently High Performance10:46
Challenge 1: Label Sparsity - The Standard Approach11:26
Challenge 1: Label Sparsity - The Ghost Edge Approach12:12
Challenge 1: Label Sparsity - Weighting Ghost Edges (1)13:26
Challenge 1: Label Sparsity - Weighting Ghost Edges (2)13:50
Challenge 2: Non-homphily - How to handle degrees of homophily? (1)14:32
Challenge 2: Non-homphily - How to handle degrees of homophily? (2)14:39
The GhostEdge Classifiers16:54
Summary of Data Sets & Prediction Tasks19:50
Various Ways of Measuring Homophily on a Network21:20
Dyadicity and Heterophilicity22:34
Log Odds of Dyadicity & Heterophilicity24:18
Experimental Methodology for Evaluating Within-Network Classifiers25:39
We Ran Seven Individual Classifiers26:33
GhostEdgeNL is Top Performer Among Relational Neighbor Classifiers27:59
GhostEdgeL is Top Performer Among Link-Based Classifiers28:49
Summary: Ghost Edges29:22
Outline (2)31:18
Community Discovery31:47
Measures of Effectiveness33:39
Background35:19
Modularity (FM)38:22
Cross-Associations (XA)38:42
Latent Dirichlet Allocation for Graphs (LDA-G)39:00
LDA-G, FM, and XA are not Consistent w.r.t. Link Prediction40:15
Hybrid Community Detection Framework (HCDF)41:02
More Whitespace, Higher Average AUC on Link Prediction42:16
Hybrid Community Detection Framework (HCDF)43:24
HCDF with Attribute Coalescing Strategy44:32
Plate Model for HCDF with Attribute Coalescing Strategy45:18
HCD Algorithm45:35
Summary of Real-World Graphs Used in Experiments (1)45:50
Summary of Real-World Graphs Used in Experiments (2)46:15
Summary of Real-World Graphs Used in Experiments (3)46:35
Measuring Effectiveness Quantitatively: Link Prediction (1)47:25
Measuring Effectiveness Quantitatively: Link Prediction (2)48:28
Experimental Methodology48:53
Link Prediction Performance Across Various Graphs48:59
Worst-Case Link Prediction Performance Across All Graphs50:32
Measuring Effectiveness Quantitatively: Value of Information51:17
Robustness Measured across Various Real-World Graphs52:11
What’s Going on Here?53:13
What About a Super-Hybrid?54:06
Summary: Community Discovery55:07
Conclusions56:02
Thank You56:45