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1. Planting Seeds for Community

   Eischen, Ellen; Hsu, Catherine (November 2022, Notices of the American Mathematical Society)
2. Planting Seeds for Community

   Eischen, Ellen; Hsu, Catherine (November 2022, Notices of the American Mathematical Society)

   An article that gives tips on how to build community in mathematics communities. 

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3. Group Testing for Community Infections

   https://doi.org/10.1109/MBITS.2021.3126244  

   Nikolopoulos, Pavlos; Srinivasavaradhan, Sundara Rajan; Fragouli, Christina; Diggavi, Suhas N. (September 2021, IEEE BITS the Information Theory Magazine)
4. Beyond the Individual: A Community-Engaged Framework for Ethical Online Community Research

   https://doi.org/10.1145/3757560  

   Zent, Matthew; Yong, Seraphina; Bala, Dhruv; Chancellor, Stevie; Konstan, Joseph A; Terveen, Loren; Yarosh, Svetlana (October 2025, Proceedings of the ACM on Human-Computer Interaction)

   Online community research routinely poses minimal risk to individuals, but does the same hold true for online communities? In response to high-profile breaches of online community trust and increased debate in the social computing research community on the ethics of online community research, this paper investigates community-level harms and benefits of research. Through 9 participatory-inspired workshops with four critical online communities (Wikipedia, InTheRooms, CaringBridge, and r/AskHistorians), we found researchers should engage more directly with communities' primary purpose by rationalizing their methods and contributions in the context of community goals to equalize the beneficiaries of community research. To facilitate deeper alignment of these expectations, we present the FACTORS (Functions for Action with Communities: Teaching, Overseeing, Reciprocating, and Sustaining) framework for ethical online community research. Finally, we reflect on our findings by providing implications for researchers and online communities to identify and implement functions for navigating community-level harms and benefits. 

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5. Exploring temporal community evolution: algorithmic approaches and parallel optimization for dynamic community detection

   https://doi.org/10.1007/s41109-023-00592-1  

   Sattar, Naw Safrin; Buluc, Aydin; Ibrahim, Khaled Z.; Arifuzzaman, Shaikh (December 2023, Applied Network Science)

   Abstract Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical complex systems including social contacts, business transactions, and computer communications. Community discovery is an extensively used graph analysis kernel with rich literature for static graphs. However, community discovery in a dynamic setting is challenging for two specific reasons. Firstly, the notion of temporal community lacks a widely accepted formalization, and only limited work exists on understanding how communities emerge over time. Secondly, the added temporal dimension along with the sheer size of modern graph data necessitates new scalable algorithms. In this paper, we investigate how communities evolve over time based on several graph metrics under a temporal formalization. We compare six different algorithmic approaches for dynamic community detection for their quality and runtime. We identify that a vertex-centric (local) optimization method works as efficiently as the classical modularity-based methods. To its advantage, such local computation allows for the efficient design of parallel algorithms without incurring a significant parallel overhead. Based on this insight, we design a shared-memory parallel algorithmDyComPar, which demonstrates between 4 and 18 fold speed-up on a multi-core machine with 20 threads, for several real-world and synthetic graphs from different domains. 

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