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1. Community detection in the human connectome: Method types, differences and their impact on inference

   https://doi.org/10.1002/hbm.26669  

   Brooks, Skylar J.; Jones, Victoria O.; Wang, Haotian; Deng, Chengyuan; Golding, Staunton G. H.; Lim, Jethro; Gao, Jie; Daoutidis, Prodromos; Stamoulis, Catherine (March 2024, Human Brain Mapping)

   Abstract Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method‐dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting‐state fMRI from = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), and = 5338 synthetic networks with heterogeneous, data‐inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization‐based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi‐optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method‐specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method‐dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods. 

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2. Inferring functional communities from partially observed biological networks exploiting geometric topology and side information

   https://doi.org/10.1038/s41598-022-14631-x  

   Sia, Jayson; Zhang, Wei; Jonckheere, Edmond; Cook, David; Bogdan, Paul (June 2022, Scientific Reports)

   Abstract Cellular biological networks represent the molecular interactions that shape function of living cells. Uncovering the organization of a biological network requires efficient and accurate algorithms to determine the components, termed communities, underlying specific processes. Detecting functional communities is challenging because reconstructed biological networks are always incomplete due to technical bias and biological complexity, and the evaluation of putative communities is further complicated by a lack of known ground truth. To address these challenges, we developed a geometric-based detection framework based on Ollivier-Ricci curvature to exploit information about network topology to perform community detection from partially observed biological networks. We further improved this approach by integrating knowledge of gene function, termed side information, into the Ollivier-Ricci curvature algorithm to aid in community detection. This approach identified essential conserved and varied biological communities from partially observedArabidopsisprotein interaction datasets better than the previously used methods. We show that Ollivier-Ricci curvature with side information identified an expanded auxin community to include an important protein stability complex, the Cop9 signalosome, consistent with previous reported links to auxin response and root development. The results show that community detection based on Ollivier-Ricci curvature with side information can uncover novel components and novel communities in biological networks, providing novel insight into the organization and function of complex networks. 

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3. Communication about sensors and communication through sensors: localizing the Internet of Things in rural communities

   https://doi.org/10.1093/jcmc/zmad005  

   Butkowski, Chelsea; Chan, Ngai_Keung; Berniker, Talia; Rodriguez, Alfredo; Schlather, Kenneth; Max_Zhang, K.; Humphreys, Lee; Özkul, ed., Didem (August 2023, Journal of Computer-Mediated Communication)

   Abstract Internet of Things (IoT) sensor networks are an emerging technology at the center of the datafication and optimization of far-reaching environmental infrastructures—from “smart cities” to workplace efficiencies. However, this low-power, low-cost technology is also well suited to local deployments in rural communities, which are often overlooked by digital development initiatives. Therefore, we used a social construction of technology approach to study how various U.S.-based IoT stakeholders—including designers and advocates as well as citizen stakeholders—understand and value sensor network technologies. Through observational methods, in-depth interviews, and participatory design research in a rural Upstate New York municipality, we worked to design sensor networks with rural community members to generate data about and for community members to further local knowledge. We found that designing rural sensor networks requires stakeholders to navigate obstacles of communication about sensors and communication through sensors to facilitate secure, ethical, and localized sensing in rural communities. 

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4. ClassicalGSG : Prediction of log P using classical molecular force fields and geometric scattering for graphs

   https://doi.org/10.1002/jcc.26519  

   Donyapour, Nazanin; Hirn, Matthew; Dickson, Alex (March 2021, Journal of Computational Chemistry)

   Abstract This work examines methods for predicting the partition coefficient (logP) for a dataset of small molecules. Here, we use atomic attributes such as radius and partial charge, which are typically used as force field parameters in classical molecular dynamics simulations. These atomic attributes are transformed into index‐invariant molecular features using a recently developed method called geometric scattering for graphs (GSG). We call this approach “ClassicalGSG” and examine its performance under a broad range of conditions and hyperparameters. We train ClassicalGSG logPpredictors with neural networks using 10,722 molecules from the OpenChem dataset and apply them to predict the logPvalues from four independent test sets. The ClassicalGSG method's performance is compared to a baseline model that employs graph convolutional networks. Our results show that the best prediction accuracies are obtained using atomic attributes generated with the CHARMM generalized force field and 2D molecular structures. 

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5. Constant community identification in million scale networks using image thresholding algorithms

   https://doi.org/10.1145/3487351.3488350  

   Chowdhury, Anjan; Srinivasan, Sriram; Bhowmick, Sanjukta; Mukherjee, Animesh; Ghosh, Kuntal (November 2021, ASONAM '21: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining)

   Constant communities, i.e., groups of vertices that are always clustered together, independent of the community detection algorithm used, are necessary for reducing the inherent stochasticity of community detection results. Current methods for identifying constant communities require multiple runs of community detection algorithm(s). This process is extremely time consuming and not scalable to large networks. We propose a novel approach for finding the constant communities, by transforming the problem to a binary classification of edges. We apply the Otsu method from image thresholding to classify edges based on whether they are always within a community or not. Our algorithm does not require any explicit detection of communities and can thus scale to very large networks of the order of millions of vertices. Our results on real-world graphs show that our method is significantly faster and the constant communities produced have higher accuracy (as per F1 and NMI scores) than state-of-the-art baseline methods. 

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