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Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Social Network Clustering Kyle Luh, Peter Elliott, and Raymond Ahn University of California Los Angeles August 2, 2011 Kyle Luh,

  1. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Social Network Clustering Kyle Luh, Peter Elliott, and Raymond Ahn University of California Los Angeles August 2, 2011 Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  2. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Outline Preliminaries 1 Attempted Solutions and Results 2 Recommended Solution and Results 3 Artificial Data 4 Future Work 5 Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  3. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Hollenbeck Gang Activity Hollenbeck has an area of approximately 15.2 miles. In this area, 31 violent gangs reside. Hollenbeck is one of the top three most violent LA policing regions. Gang violence in this region has existed since before WWII. Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  4. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Hollenbeck Gang Activity The LAPD has provided an Excel database of non-criminal stops they have made in the Hollenbeck area. The data includes: time of stop location (gang territory and coordinates) gang affiliation sex age ethnicity Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  5. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Goals Use clustering techniques to predict unknown gang affiliations. Detect other social structures that may not be captured by gang affiliation. Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  6. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Graph Models Convert individuals into nodes. Edge weights indicate similarity. Unfortunately, data is sparse. Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  7. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Choosing a Measure of Similarity A function of Euclidean distance Dot product of feature vector Gang territory Individuals and their gang associations Individual to individual interactions Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  8. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Results Actual Gang Clusters Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  9. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Outline Preliminaries 1 Attempted Solutions and Results 2 Recommended Solution and Results 3 Artificial Data 4 Future Work 5 Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  10. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work K-means algorithm Algorithm Choose number of partitions. Compute centroids. Shift centers to the centroid of their affiliated points. Repeat until equilibrium is achieved. Cons The K-means algorithm only accounts for location. We hope to utilize more of the data. Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  11. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Results: K-means Approach 6.47 6.48 6.49 6.5 6.51 6.52 6.53 Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  12. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work A Metric for Cluster Evaluation We define purity to be purity (Ω , C ) = 1 � max j | ω k ∩ c j | N k where Ω = { ω 1 , · · · , ω K } are the clusters and C = { c 1 , · · · , c j } are the actual classes. Another measure we used was Adjusted Mutual Information which may be more appropriate since our gangs vary significantly in size. Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  13. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Results: K-means Approach Purity ≈ 0 . 4 and AMI ≈ 0 . 4 6.47 6.48 6.49 6.5 6.51 6.52 6.53 Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  14. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Modularity Maximization Modularity compares the number of edges within a cluster to the number expected Maximize modularity. We can calculate the change in modularity at each step and stop when the change is not positive [M.J. Newman, 2006] Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  15. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Results: Modularity Maximization Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  16. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Convergence of Iterated Correlations (CONCOR) Compute correlations of entries to the mean of rows/columns Continue to calculate the correlations of the correlation matrix until we are left with +1 and − 1. The method is repeated on each cluster to achieve a finer partition. [Wasserman, 1994] Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  17. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Results: CONCOR Purity ≈ 0 . 5 and AMI ≈ 0 . 46. Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  18. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Outline Preliminaries 1 Attempted Solutions and Results 2 Recommended Solution and Results 3 Artificial Data 4 Future Work 5 Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  19. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Spectral Clustering Create a matrix of eigenvectors of the Adjacency matrix. The eigenvectors capture the axes which contain the most variation in the data. Run k-means algorithm on new space. Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering [Ng et al, 2001]

  20. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Eigenvalues Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  21. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Eigenvector Plots: Distance Only Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  22. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Eigenvector Plots: Social Information Only Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  23. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Where to go from here? The geographic data provides no insights. The social data is so sparse that its eigenvectors are completely useless alone. We decided to combine the two adjacency matrices, α A + (1 − α ) B , where α is a weighting parameter. Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  24. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Eigenvector Plots: Combined Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  25. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Clustering Results Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  26. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Clustering Results Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  27. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Clustering Results Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  28. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Results: Spectral Approach Purity ≈ . 7 and AMI ≈ . 65 Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  29. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Outline Preliminaries 1 Attempted Solutions and Results 2 Recommended Solution and Results 3 Artificial Data 4 Future Work 5 Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  30. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Artificial Data Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

  31. Preliminaries Attempted Solutions and Results Recommended Solution and Results Artificial Data Future Work Artificial Data Inputs: Number of people Number of communities Gang multiplier Threshold G ( x , y ) = ( η x + η y ) σ dist ( x , y ) (1 + M δ ij ) [N Masuda, 2005] Kyle Luh, Peter Elliott, and Raymond Ahn Social Clustering

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