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- w: Spreading activation
- Spreading activation is a method for searching associative networks, neural networks, or semantic networks. The search process is initiated by labeling a set of source nodes (e.g. concepts in a semantic network) with weights or "activation" and then iteratively propagating or "spreading" that activation out to other nodes linked to the source nodes. Most often these "weights" are real values that decay as activation propagates through the network. When the weights are discrete this process is often referred to as marker passing. Activation may originate from alternate paths, identified by distinct markers, and terminate when two alternate paths reach the same node.
- Spreading activation models are used in cognitive psychology to model the fan out effect.
- Spreading activation can also be applied in information retrieval,  by means of a network of nodes representing documents and terms contained in those documents.
- ↑ John R. Anderson (1983). "A spreading activation theory of memory." Journal of Verbal Learning and Verbal Behavior.
- ↑ S. Preece (1981). A spreading activation network model for information retrieval. PhD thesis, University of Illinois, Urbana-Champaign.
- ↑ Fabio Crestani (1997). "Application of Spreading Activation Techniques in Information Retrieval". Artificial Intelligence Review.