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- W4281620912 abstract "Random walks can reveal communities or clusters in networks, because they are more likely to stay within a cluster than leave it. Thus, one family of community detection algorithms uses random walks to measure distance between pairs of nodes in various ways, and then applies K-Means or other generic clustering methods to these distances. Interestingly, information processing in the brain may suggest a simpler method of learning clusters directly from random walks. Drawing inspiration from the hippocampus, we describe a simple two-layer neural learning framework. Neurons in one layer are associated with graph nodes and simulate random walks. These simulations cause neurons in the second layer to become tuned to graph clusters through simple associative learning. We show that if these neuronal interactions are modelled a particular way, the system is essentially a variant of K-Means clustering applied directly in the walk-space, bypassing the usual step of computing node distances/similarities. The result is an efficient graph clustering method. Biological information processing systems are known for high efficiency and adaptability. In tests on benchmark graphs, our framework demonstrates this high data-efficiency, low memory use, low complexity, and real-time adaptation to graph changes, while still achieving clustering quality comparable to other algorithms." @default.
- W4281620912 created "2022-06-13" @default.
- W4281620912 creator A5047020510 @default.
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- W4281620912 date "2022-05-19" @default.
- W4281620912 modified "2023-09-26" @default.
- W4281620912 title "Hippocluster: an efficient, hippocampus-inspired algorithm for graph clustering" @default.
- W4281620912 doi "https://doi.org/10.48550/arxiv.2205.12338" @default.
- W4281620912 hasPublicationYear "2022" @default.
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