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(Abridged) Scientific publishing is undergoing major change, driven by a shift toward open access (OA), the rise of artificial intelligence (AI), and growing demands for transparency, reproducibility, and equity. At the same time, rapid growth in article output strains editors and reviewers and means that metrics and speed can eclipse quality and rigor. To better understand how the community is responding, Astronomy \& Astrophysics (A\&A) commissioned the {A\&A Survey on Trends and Challenges in Scientific Publishing}, which documents community opinion on journal choice, peer review, OA, research evaluation, and the role of AI, with the goal of informing future editorial policies and the wider conversation on sustainable, ethical, and equitable scientific communication. Distributed online in May 2025 to \SI{28787} A\&A authors and co-authors, the survey drew \SI{2944} responses from 69 countries by its closing date. The responses were clear. Journal quality and reputation are the most decisive factors in deciding where to publish, followed by cost. The principal worry about peer review is reviewer expertise and fairness rather than speed. Citation counts are still a

Today's evolving labor markets rely increasingly on recommender systems for hiring, talent management, and workforce analytics, with natural language processing (NLP) capabilities at the core. Yet, research in this area remains highly fragmented. Studies employ divergent ontologies (ESCO, O*NET, national taxonomies), heterogeneous task formulations, and diverse model families, making cross-study comparison and reproducibility exceedingly difficult. General-purpose benchmarks lack coverage of work-specific tasks, and the inherent sensitivity of employment data further limits open evaluation. We present \textbf{WorkRB} (Work Research Benchmark), the first open-source, community-driven benchmark tailored to work-domain AI. WorkRB organizes 13 diverse tasks from 7 task groups as unified recommendation and NLP tasks, including job/skill recommendation, candidate recommendation, similar item recommendation, and skill extraction and normalization. WorkRB enables both monolingual and cross-lingual evaluation settings through dynamic loading of multilingual ontologies. Developed within a multi-stakeholder ecosystem of academia, industry, and public institutions, WorkRB has a modular design

Community currencies (CCs) have been adopting innovative systems to overcome implementational hurdles from issuing paper currencies. Using a qualitative approach, this paper examined this digital transition of Sarafu Network in Kenya and its predecessor CCs as a case study. From the original vouchers launched in 2010, the foundation Grassroots Economics introduced a digital interface in 2016 that operates on a feature phone, and then integrated blockchain technology starting in 2018, undergoing several migrations before becoming settling on its current iteration called Community Asset Vouchers on the Celo blockchain since 2023. Using affordances from human-computer interaction, the research shows that digitalization and blockchain improved the facilitation of economic activities of the local communities, both their typical market transactions as well as traditional reciprocal labor exchanges, by offering more functionalities compared to the analog version of Sarafu. The unique contributions of blockchain include enabling automation of holding tax calculations and linking the vouchers to the mainstream monetary system via stablecoins facilitated by a series of smart contracts also k

Over the last 15 years, the Atacama Large Millimeter/submillimeter Array (ALMA) has revolutionized astrophysics by providing unprecedented resolution and sensitivity in observing the cold universe, including the formation of stars, planets, and galaxies. With groundbreaking discoveries ranging from the first detailed images of protoplanetary disks to the kinematics of galaxies in the Epoch of Reionization, ALMA has showcased the vast discovery potential of the (sub-)mm wavelength regime. However, in another 15 years from now--in the 2040s--the science landscape will have changed dramatically as new major observational facilities will have started their operations or have come towards advanced maturity in their scientific outcome (e.g., JWST, Rubin Observatory, ELT, Euclid, Gaia, Plato, Ariel, Roman Space Telescope, SPHEREx, LiteBIRD, LISA, SKA and others). At the same time, ALMA's current Wideband Sensitivity Upgrade will have been in place for ~10 years, and ALMA itself will have been operational for 30 years. To fully exploit this era, the community needs a next-generation facility operating at (sub-)mm wavelengths with capabilities far beyond those possible within ALMA's current

Social recommendation, which seeks to leverage social ties among users to alleviate the sparsity issue of user-item interactions, has emerged as a popular technique for elevating personalized services in recommender systems. Despite being effective, existing social recommendation models are mainly devised for recommending regular items such as blogs, images, and products, and largely fail for community recommendations due to overlooking the unique characteristics of communities. Distinctly, communities are constituted by individuals, who present high dynamicity and relate to rich structural patterns in social networks. To our knowledge, limited research has been devoted to comprehensively exploiting this information for recommending communities. To bridge this gap, this paper presents CASO, a novel and effective model specially designed for social community recommendation. Under the hood, CASO harnesses three carefully-crafted encoders for user embedding, wherein two of them extract community-related global and local structures from the social network via social modularity maximization and social closeness aggregation, while the third one captures user preferences using collaborati

Articulation agreements provide more transparency about how community college courses will transfer and fulfill university requirements. However, the literature displays conflicting results on whether articulation agreements improve transfer-related outcomes; perhaps one contributor to these conflicting research results is the subpar user experience of articulation agreement reports and the websites that host them. Accordingly, we surveyed and interviewed California community college transfer students to gather their suggestions for new academic-advising-related software features for the ASSIST website. ASSIST is California's official centralized repository of articulation agreement reports between public California community colleges and universities. We analyzed the open-ended survey and interview data using structural coding and thematic analysis. We identified four themes around students' software feature suggestions for ASSIST: (a) features that automate laborious academic advising tasks, (b) features to reduce ambiguity with articulation agreements, (c) features to mitigate mistakes in term-by-term course planning, and (d) features to facilitate online advising from advisors

In this article, we aim to analyse the nature and epistemic consequences of what figures in network science as patterns of nodes and edges called 'communities'. Tracing these patterns as multi-faceted and ambivalent, we propose to describe the concept of community as a 'vague operator', a variant of Susan Leigh Star's notion of the boundary object, and propose that the ability to construct different modes of description that are both vague in some registers and hyper-precise in others, is core both to digital politics and the analysis of 'communities'. Engaging with these formations in terms drawn from mathematics and software studies enables a wider mapping of their formation. Disentangling different lineages in network science then allows us to contextualise the founding account of 'community' popularised by Michelle Girvan and Mark Newman in 2002. After studying one particular community detection algorithm, the widely-used 'Louvain algorithm', we comment on controversies arising with some of their more ambiguous applications. We argue that 'community' can act as a real abstraction with the power to reshape social relations such as producing echo chambers in social networking sit

It is becoming increasingly important that physics educators equip their students with the skills to work with data effectively. However, many educators may lack the necessary training and expertise in data science to teach these skills. To address this gap, we created the Data Science Education Community of Practice (DSECOP), bringing together graduate students and physics educators from different institutions and backgrounds to share best practices and lessons learned from integrating data science into undergraduate physics education. In this article we present insights and experiences from this community of practice, highlighting key strategies and challenges in incorporating data science into the introductory physics curriculum. Our goal is to provide guidance and inspiration to educators who seek to integrate data science into their teaching, helping to prepare the next generation of physicists for a data-driven world.

The German Information Retrieval community is located in two different sub-fields: Information and computer science. There are no current studies that investigate these communities on a scientometric level. Available studies only focus on the information scientific part of the community. We generated a data set of 401 recent IR-related publications extracted from six core IR conferences from a mainly computer scientific background. We analyze this data set at the institutional and researcher level. The data set is publicly released, and we also demonstrate a mapping use case.

The plague of false information, popularly called fake news has affected lives of news consumers ever since the prevalence of social media. Thus understanding the spread of false information in social networks has gained a lot of attention in the literature. While most proposed models do content analysis of the information, no much work has been done by exploring the community structures that also play an important role in determining how people get exposed to it. In this paper we base our idea on Computational Trust in social networks to propose a novel Community Health Assessment model against fake news. Based on the concepts of neighbor, boundary and core nodes of a community, we propose novel evaluation metrics to quantify the vulnerability of nodes (individual-level) and communities (group-level) to spreading false information. Our model hypothesizes that if the boundary nodes trust the neighbor nodes of a community who are spreaders, the densely-connected core nodes of the community are highly likely to become spreaders. We test our model with communities generated using three popular community detection algorithms based on two new datasets of information spreading networks c

Social network analysis is a popular discipline among the social and behavioural sciences, in which the relationships between different social entities are modelled as a network. One of the most popular problems in social network analysis is finding communities in its network structure. Usually, a community in a social network is a functional sub-partition of the graph. However, as the definition of community is somewhat imprecise, many algorithms have been proposed to solve this task, each of them focusing on different social characteristics of the actors and the communities. In this work we propose to use novel combinations of affinity functions, which are designed to capture different social mechanics in the network interactions. We use them to extend already existing community detection algorithms in order to combine the capacity of the affinity functions to model different social interactions than those exploited by the original algorithms.

Community structure in networks is often a consequence of homophily, or assortative mixing, based on some attribute of the vertices. For example, researchers may be grouped into communities corresponding to their research topic. This is possible if vertex attributes have discrete values, but many networks exhibit assortative mixing by some continuous-valued attribute, such as age or geographical location. In such cases, no discrete communities can be identified. We consider how the notion of community structure can be generalized to networks that are based on continuous-valued attributes: in general, a network may contain discrete communities which are ordered according to their attribute values. We propose a method of generating synthetic ordered networks and investigate the effect of ordered community structure on the spread of infectious diseases. We also show that community detection algorithms fail to recover community structure in ordered networks, and evaluate an alternative method using a layout algorithm to recover the ordering.

A precise definition of what constitutes a community in networks has remained elusive. Consequently, network scientists have compared community detection algorithms on benchmark networks with a particular form of community structure and classified them based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different reasons for why we would want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different approaches to community detection also delineates the many lines of research and points out open directions and avenues for future research.

The stochastic block model (SBM) is one of the most widely used generative models for network data. Many continuous-time dynamic network models are built upon the same assumption as the SBM: edges or events between all pairs of nodes are conditionally independent given the block or community memberships, which prevents them from reproducing higher-order motifs such as triangles that are commonly observed in real networks. We propose the multivariate community Hawkes (MULCH) model, an extremely flexible community-based model for continuous-time networks that introduces dependence between node pairs using structured multivariate Hawkes processes. We fit the model using a spectral clustering and likelihood-based local refinement procedure. We find that our proposed MULCH model is far more accurate than existing models both for predictive and generative tasks.

The process by which new communities emerge is a central research issue in the social sciences. While a growing body of research analyzes the formation of a single community by examining social networks between individuals, we introduce a novel community-centered perspective. We highlight the fact that the context in which a new community emerges contains numerous existing communities. We reveal the emerging process of communities by tracing their early members' previous community memberships. Our testbed is Reddit, a website that consists of tens of thousands of user-created communities. We analyze a dataset that spans over a decade and includes the posting history of users on Reddit from its inception to April 2017. We first propose a computational framework for building genealogy graphs between communities. We present the first large-scale characterization of such genealogy graphs. Surprisingly, basic graph properties, such as the number of parents and max parent weight, converge quickly despite the fact that the number of communities increases rapidly over time. Furthermore, we investigate the connection between a community's origin and its future growth. Our results show that

Over the past decade, community detection in overlapping un-weighted networks, where nodes can belong to multiple communities, has been one of the most popular topics in modern network science. However, community detection in overlapping weighted networks, where edge weights can be any real value, remains challenging. In this article, we propose a generative model called the weighted degree-corrected mixed membership (WDCMM) model to model such weighted networks. This model adopts the same factorization for the expectation of the adjacency matrix as the previous degree-corrected mixed membership (DCMM) model. Our WDCMM extends the DCMM from un-weighted networks to weighted networks by allowing the elements of the adjacency matrix to be generated from distributions beyond Bernoulli. We first address the community membership estimation of the model by applying a spectral algorithm and establishing a theoretical guarantee of consistency. Then, we propose overlapping weighted modularity to measure the quality of overlapping community detection for both assortative and dis-assortative weighted networks. To determine the number of communities, we incorporate the algorithm into the propos

The problem of community detection is important as it helps in understanding the spread of information in a social network. All real complex networks have an inbuilt structure which captures and characterizes the network dynamics between its nodes. Linkages are more likely to form between similar nodes, leading to the formation of some community structure which characterizes the network dynamic. The more friends they have in common, the more the influence that each person can exercise on the other. We propose a disjoint community detection algorithm, $\textit{NashDisjoint}$ that detects disjoint communities in any given network. We evaluate the algorithm $\textit{NashDisjoint}$ on the standard LFR benchmarks, and we find that our algorithm works at least as good as that of the state of the art algorithms for the mixing factors less than 0.55 in all the cases. We propose an overlapping community detection algorithm $\textit{NashOverlap}$ to detect the overlapping communities in any given network. We evaluate the algorithm $\textit{NashOverlap}$ on the standard LFR benchmarks and we find that our algorithm works far better than the state of the art algorithms in around 152 different

We have investigated community structure in the co-inventor network of a given cohort of patents and related this structure to the dynamics of how these patents acquire their first citation. A statistically significant difference in the time lag until first citation is linked to whether or not this citation comes from a patent whose listed inventors share membership in the same communities as the inventors of the cited patent. Although the inventor-community structures identified by different community-detection algorithms differ in several aspects, including the community-size distribution, the magnitude of the difference in time to first citation is robustly exhibited. Our work is able to quantify the expected acceleration of knowledge flow within inventor communities and thereby further establishes the utility of network-analysis tools for studying innovation dynamics.

This is the summary report of the Community Engagement Frontier for the Snowmass 2021 study of the future of particle physics. The report discusses a number of general issues of importance to the particle physics community, including (1) the relation of universities, national laboratories, and industry, (2) career paths for scientists engaged in particle physics, (3) diversity, equity, and inclusion, (4) physics education, (5) public education and outreach, (6) engagement with the government and public policy, and (7) the environmental and social impacts of particle physics.