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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. 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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5. Community Code Engagements: Summer of Code & Hackathons for Community Building in Scientific Software

   https://doi.org/10.1145/2660398.2660420  

   Trainer, Erik H.; Chaihirunkarn, Chalalai; Kalyanasundaram, Arun; Herbsleb, James D. (January 2014, International Conference on Supporting Group Work)

   Community code engagements -- short-term, intensive software development events -- are used by some scientific communities to create new software features and promote community building. But there is as yet little empirical support for their effectiveness. This paper presents a qualitative study of two types of community code engagements: Google Summer of Code (GSoC) and hackathons. We investigated the range of outcomes these engagements produce and the underlying practices that lead to these outcomes. In GSoC, the vision and experience of core members of the community influence project selection, and the intensive mentoring process facilitates creation of strong ties. Most GSoC projects result in stable features. The agenda setting phase of hackathons reveals high priority issues perceived by the community. Social events among the relatively large numbers of participants over brief engagements tend to create weak ties. Most hackathons result in prototypes rather than finished tools. We discuss themes and tradeoffs that suggest directions for future empirical work around designing community code engagements. 

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