Editing Community Detection (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Community Detection''' — A set of techniques in social network analysis for finding groups of nodes that are densely connected internally.
* '''Modularity (Q)''' — A mathematical measure of "How good" a community split is; it compares the connections inside a group to what you would expect in a "Random" network.
* '''Clustering''' — The general process of "Grouping" similar things together.
* '''Edge Betweenness''' (Girvan-Newman) — Finding communities by "Cutting the bridges" (the edges with the highest betweenness) until the network "Falls apart" into groups.
* '''The Louvain Method''' — A very fast, popular algorithm that builds communities from the "Bottom up" (starting with single nodes).
* '''Overlapping Communities''' — The reality that one person can belong to "Many" groups at once (e.g., your "Family" group and your "Python Developer" group).
* '''Hierarchical Clustering''' — A "Tree-like" structure that shows how small groups (Families) join into medium groups (Neighborhoods) and then large groups (Cities).
* '''Clique''' — The "Tightest" possible community: a group where "Everyone knows Everyone."
* '''Assortative Mixing''' — The "Birds of a Feather" effect: the tendency of people to connect with people who are "Like them" (Homophily).
* '''Core-Periphery Structure''' — A common pattern where a "Dense Core" of people are connected to a "Loose Periphery" of hangers-on.
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