# Community detection with spiking neural networks for neuromorphic hardware

arXiv: https://arxiv.org/abs/1711.07361 · HTML: https://mlsanity.com/paper/1711.07361/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [SpikE: spike-based embeddings for multi-relational graph data](https://mlsanity.com/paper/2104.13398/) (2021)
- [Randomized Algorithms for Scientific Computing (RASC)](https://mlsanity.com/paper/2104.11079/) (2021)
- [Neuro-symbolic computing with spiking neural networks](https://mlsanity.com/paper/2208.02576/) (2022)

## Abstract (arXiv)

We present results related to the performance of an algorithm for community detection which incorporates event-driven computation. We define a mapping which takes a graph G to a system of spiking neurons. Using a fully connected spiking neuron system, with both inhibitory and excitatory synaptic connections, the firing patterns of neurons within the same community can be distinguished from firing patterns of neurons in different communities. On a random graph with 128 vertices and known community structure we show that by using binary decoding and a Hamming-distance based metric, individual communities can be identified from spike train similarities. Using bipolar decoding and finite rate thresholding, we verify that inhibitory connections prevent the spread of spiking patterns.