Time-varying nodal measures with temporal community structure: A cautionary note to avoid misinterpretation.

William Hedley Thompson,Russell A Poldrack,Jessey Wright,James M Shine,Karolina Finc,Granit Kastrati

doi:10.1002/hbm.24950

Abstract

In network neuroscience, temporal network models have gained popularity. In these models, network properties have been related to cognition and behavior. Here, we demonstrate that calculating nodal properties that are dependent on temporal community structure (such as the participation coefficient [PC]) in time‐varying contexts can potentially lead to misleading results. Specifically, with regards to the participation coefficient, increases in integration can be inferred when the opposite is occurring. Further, we present a temporal extension to the PC measure (temporal PC) that circumnavigates this problem by jointly considering all community partitions assigned to a node through time. The proposed method allows us to track a node's integration through time while adjusting for the possible changes in the community structure of the overall network.

Highlights

Quantifying the time-varying properties of a network often utilizes a multilayer network approach of temporally ordered “snapshots” consisting of connectivity matrices through time
The participation coefficient (PC) is an example of a static network measure used in time-varying contexts that is applied to multiple temporal snapshots
We found that when difference in PC is large, either PC through time with temporal communities (PCT) or point with static communities” (PCS) will have high participation (Figure 2d–g)

Summary

| INTRODUCTION

Quantifying the time-varying properties of a network often utilizes a multilayer network approach of temporally ordered “snapshots” consisting of connectivity matrices through time (i.e., temporal network theory; Holme & Saramäki, 2012; Kivelä et al, 2014) This approach answers questions about how nodes, edges, and communities in a network fluctuate over time. Others apply static measures to each temporal snapshot (e.g., Bola & Sabel, 2015 found changes in rich club coefficients applied to multiple time points). The participation coefficient (PC) is an example of a static network measure used in time-varying contexts that is applied to multiple temporal snapshots. We propose a new method—the temporal PC (TPC)—that takes into account various community partitions calculated over time

| METHODS

| RESULTS

Findings

| DISCUSSION