Hippocluster: an efficient, hippocampus-inspired algorithm for graph clustering

TL;DR

Hippocluster is a biologically inspired, efficient graph clustering algorithm that directly learns clusters from random walk simulations, offering high efficiency, low memory use, and real-time adaptability.

Contribution

This paper introduces a novel hippocampus-inspired neural framework that performs graph clustering directly from random walks, bypassing traditional distance computations.

Findings

Achieves clustering quality comparable to existing algorithms.

Demonstrates high data-efficiency and low memory usage.

Provides real-time adaptation to dynamic graph changes.

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.