Tagging systems can be modeled as tripartite networks consisting of users, resources, and tags. Analysis of tag co-occurrence networks reveals scale-free properties and non-trivial clustering that may encode semantic relationships between tags. A simple hierarchical model of user tagging is able to partially reproduce properties of real-world tag co-occurrence networks, providing evidence that users generally apply tags according to an underlying conceptual hierarchy. Further modeling semantic relationships could improve systems by facilitating categorization and spam detection.

3. TAGS

8. Sub-linear vocabulary growth internal time # of tags

9. del.icio.us > x 0.8

10. Tag frequency distribution

11. Preferential attachment

12. Few tags per resource

19. Distribution of strength

20. Distribution of strength ?

21. Nontrivial clustering & spam detection Clustering coefficient C(k) Average density of triangles around nodes with degree k

22. Nontrivial clustering & spam detection

23. Nontrivial clustering & spam detection k = 502

24. Looking for a k = 502 page...

26. SPAM

27. Nontrivial clustering & spam detection spam k = 502

33. Users' strategies Do users tag resources according to tag conceptual hierarchy?

34. Semantics and hierarchy For example ” Emergence of scaling in random networks” by A.-L. Barabasi and R. Albert

35. Semantics and hierarchy For example ” Emergence of scaling in random networks” by A.-L. Barabasi and R. Albert scale-free networks

36. Semantics and hierarchy For example ” Emergence of scaling in random networks” by A.-L. Barabasi and R. Albert scale-free networks networks HIERARCHICAL

37. Semantics and hierarchy For example ” Emergence of scaling in random networks” by A.-L. Barabasi and R. Albert scale-free networks WWW NON HIERARCHICAL

43. Results: strength distribution

44. Results: clustering