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Tweets and Twitter's "following" mechanism link people in a variety of ways, ranging from short (but often meaningful) conversational dialogues to interest graphs that connect people and the things that they care about.
It is the asymmetric following model that casts Twitter as more of an interest graph than a social network, and the APIs that provide just enough of a framework for struc‐ ture and self-organizing behavior to emerge from the chaos.
Think of an interest graph as a way of modeling connections between people and their arbitrary interests.
Interest graphs provide a profound number of possibilities in the data mining realm that primarily involve measuring correlations between things for the objective of making intelligent recommendations and other applications in machine learning.
For example, you could use an interest graph to measure correlations and make recommendations ranging from whom to follow on Twitter to what to purchase online to whom you should date.
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