Dynamic community detection (DCD) in temporal networks is a complicated task that involves the selection of a method and its associated hyperparameters. How to choose the most appropriate method generally depends on the type of network being analysed and the specific properties of the data that define the network. In functional temporal networks derived from neuronal spike train data, communities are expected to be transient, and it is common for the network to contain multiple singleton communities. Here, we compare the performance of different DCD methods on functional temporal networks built from synthetic neuronal time series data with known community structure. We find that, for these networks, DCD methods that utilize interlayer links to perform community carry over between layers outperform other methods. However, we also observe that DCD performance is highly dependent on the topology of interlayer links, especially in the presence of singleton and transient communities. We therefore define a novel way of defining interlayer links in temporal networks called skeleton coupling that is specifically designed to enhance the linkage of communities in the network throughout time based on the topological properties of the community history. We show that integrating skeleton coupling with current DCD methods improves the method’s performance in synthetic data with planted singleton and transient communities. The use of skeleton coupling to perform DCD will therefore allow for more accurate and interpretable results of community evolution in real-world neuronal data or in other systems with transient structure and singleton communities.
Dynamic community detection provides a coherent description of network clusters over time, allowing one to track the growth and death of communities as the network evolves. However, modularity maximization, a popular method for performing multilayer community detection, requires the specification of an appropriate null network as well as resolution and interlayer coupling parameters. Importantly, the ability of the algorithm to accurately detect community evolution is dependent on the choice of these parameters. In functional temporal networks, where evolving communities reflect changing functional relationships between network nodes, it is especially important that the detected communities reflect any state changes of the system. Here, we present analytical work suggesting that a uniform null network provides improved sensitivity to the detection of small evolving communities in temporal networks with positive edge weights bounded above by 1, such as certain types of correlation networks. We then propose a method for increasing the sensitivity of modularity maximization to state changes in nodal dynamics by modelling self-identity links between layers based on the self-similarity of the network nodes between layers. This method is more appropriate for functional temporal networks from both a modelling and mathematical perspective, as it incorporates the dynamic nature of network nodes. We motivate our method based on applications in neuroscience where network nodes represent neurons and functional edges represent similarity of firing patterns in time. We show that in simulated data sets of neuronal spike trains, updating interlayer links based on the firing properties of the neurons provides superior community detection of evolving network structure when groups of neurons change their firing properties over time. Finally, we apply our method to experimental calcium imaging data that monitors the spiking activity of hundreds of neurons to track the evolution of neuronal communities during a state change from the awake to anaesthetized state.
more » « less- Award ID(s):
- 1734795
- NSF-PAR ID:
- 10114612
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Journal of Complex Networks
- Volume:
- 7
- Issue:
- 4
- ISSN:
- 2051-1329
- Page Range / eLocation ID:
- p. 529-553
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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