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Cliques and Sub-groups

Cliques and Sub-groups. 王一 棉 1201211788. Contents. Top-down approaches Components Blocks and cut-points Lambda sets and bridges Bottom-up approaches K -cliques K-clans K- plexes K-cores Application & Thinking. Review. SNA (Social Network Analysis) Individual level

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Cliques and Sub-groups

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  1. Cliques and Sub-groups 王一棉 1201211788

  2. Contents • Top-down approaches • Components • Blocks and cut-points • Lambda sets and bridges • Bottom-up approaches • K-cliques • K-clans • K-plexes • K-cores • Application & Thinking

  3. Review • SNA (Social Network Analysis) • Individual level • Centrality (Degree/closeness/betweenness) • Today: Group level • Clique & sub-groups level • Top-down approaches • Bottom-up approaches

  4. 今天我们聊聊帮派…… • 物以类聚，人以群分 • 拉帮结派，结党营私

  5. 江湖太乱……

  6. Top-down Components • Components of a graph are sub-graphs that are connected within, but disconnected between sub-graphs. Weak Components • Connected, regardless of the direction of ties. Strong Components • Directed path from A to B in order for the two to be in the same component.

  7. Top-down: Blocks and cut-points The UCINET algorithm Network>Regions>Bi-Component locates and identifies blocks and cutpoints.

  8. Top-down: Lambda sets and bridges • Network>Subgroups>Lambda Setlocates the vulnerable "bridges" between pairs of actors.

  9. Bottom-up: K-Cliques • precise than the general notion of a high local density. • Cliques and K-Cliques • Formally, a clique is the maximum number of actors who have all possible ties present among themselves. • K-Cliques: "a friend of a friend."

  10. Bottom-up: K-clans • A restricted version of K-cliques A B C E D F

  11. Bottom-up: K-plexes • Vulnerability in K-cliques • K-plexes • Actors may be members of a clique even if they have ties to all but k other members. • Which also means, each actor’s degree is not less than n-k 1 2 4 A 3 F C B 3-cliques, but not a 3-plexes E D

  12. Comparison • Rather than the large and "stringy" groupings sometimes produced by n-clique analysis, k-plex analysis tends to find relatively large numbers of smaller groupings. • Why? Rule (indirect or direct connection)

  13. Summary • Component • Weak: new information • Strong: information effectiveness • K-clique • In-group homogeneity • Similarity, acceptability • K-plexes • Small group • Interaction, information effectiveness

  14. Application in marketing • Combine both individual level and group level • For virus marketing, find the cut-point or bridge • For WOM, initiate it inside a small clique or strong components

  15. THANK YOU!

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