SWITCHER-RANDOM-WALKS: A COGNITIVE-INSPIRED MECHANISM FOR NETWORK EXPLORATION

Semantic memory is the subsystem of human memory that stores knowledge of concepts or meanings, as opposed to life specific experiences. The organization of concepts within semantic memory can be understood as a semantic network, where the concepts (nodes) are associated (linked) to others depending on perceptions, similarities, etc. Lexical access is the complemen-tary part of this system and allows the retrieval of such organized knowledge. While conceptual information is stored under certain underlying organization (and thus gives rise to a specific topology), it is crucial to have an accurate access to any of the information units, e.g. the con-cepts, for efficiently retrieving semantic information for real-time need. An example of an infor-mation retrieval process occurs in verbal fluency tasks, and it is known to involve two different mechanisms: “clustering”, or generating words within a subcategory, and, when a subcategory is exhausted, “switching ” to a new subcategory. We extended this approach to random-walking on a network (clustering) in combination to jumping (switching) to any node with certain proba-bility and derived its analytical expression based on Markov chains. Results show that this dual mechanism contributes to optimize the exploration of different network models in terms of the mean first passage time. Additionally, this cognitive inspired dual mechanism opens a new frame