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1. Optimizing state change detection in functional temporal networks through dynamic community detection

   https://doi.org/10.1093/comnet/cny030  

   Vaiana, Michael ; Goldberg, Ethan M. ; Muldoon, Sarah F. ; Porter, ed., Mason ( December 2018 , Journal of Complex Networks)

   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.

    

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2. Cancer Biomarker Discovery from Gene Co-expression Networks Using Community Detection Methods

   https://doi.org/10.1109/BIBM47256.2019.8982960  

   Tanvir, Raihanul Bari ; Mondal, Ananda Mohan ( November 2019 , 2019 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM))

   Finding the network biomarkers of cancers and the analysis of cancer driving genes that are involved in these biomarkers are essential for understanding the dynamics of cancer. Clusters of genes in co-expression networks are commonly known as functional units. This work is based on the hypothesis that the dense clusters or communities in the gene co-expression networks of cancer patients may represent functional units regarding cancer initiation and progression. In this study, RNA-seq gene expression data of three cancers - Breast Invasive Carcinoma (BRCA), Colorectal Adenocarcinoma (COAD) and Glioblastoma Multiforme (GBM) - from The Cancer Genome Atlas (TCGA) are used to construct gene co-expression networks using Pearson Correlation. Six well-known community detection algorithms are applied on these networks to identify communities with five or more genes. A permutation test is performed to further mine the communities that are conserved in other cancers, thus calling them conserved communities. Then survival analysis is performed on clinical data of three cancers using the conserved community genes as prognostic co-variates. The communities that could distinguish the cancer patients between high- and low-risk groups are considered as cancer biomarkers. In the present study, 16 such network biomarkers are discovered. 

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3. Community detection in complex networks: From statistical foundations to data science applications

   https://doi.org/10.1002/wics.1566  

   Dey, Asim K. ; Tian, Yahui ; Gel, Yulia R. ( January 2021 , WIREs Computational Statistics)

   null (Ed.)
4. Archaeological networks, community detection, and critical scales of interaction in the U.S. Southwest/Mexican Northwest

   https://doi.org/10.1016/j.jaa.2023.101511  

   Peeples, Matthew A. ; J. Bischoff, Robert ( June 2023 , Journal of Anthropological Archaeology)

   Archaeologists have long recognized that spatial relationships are an important influence on and driver of all manner of social processes at scales from the local to the continental. Recent research in the realm of complex networks focused on community detection in human and animal networks suggests that there may be certain critical scales at which spatial interactions can be partitioned, allowing researchers to draw potential boundaries for interaction that provide insights into a variety of social phenomena. Thus far, this research has been focused on short time scales and has not explored the legacies of historic relationships on the evolution of network communities and boundaries over the long-term. In this study, we examine networks based on material cultural similarity drawing on a large settlement and material culture database from the U.S. Southwest/Mexican Northwest (ca. 1000–1450 CE) divided into a series of short temporal intervals. With these temporally sequenced networks we: 1) demonstrate the utility of network community detection for partitioning interactions in geographic space, 2) identify key transitions in the geographic scales of network communities, and 3) illustrate the role of previous network configurations in the evolution of network communities and their spatial boundaries through time. 

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5. Fusing data depth with complex networks: Community detection with prior information

   https://doi.org/10.1016/j.csda.2019.01.007  

   Tian, Yahui ; Gel, Yulia R. ( November 2019 , Computational Statistics & Data Analysis)