Relay-Linking Models for Prominence and Obsolescence in Evolving Networks

The rate at which nodes in evolving social networks acquire links (friends, citations) shows complex temporal dynamics. Preferential attachment and link copying models, while enabling elegant analysis, only capture rich-gets-richer effects, not aging and decline. Recent aging models are complex and heavily parameterized; most involve estimating 1-3 parameters per node. These parameters are intrinsic: they explain decline in terms of events in the past of the same node, and do not explain, using the network, where the linking attention might go instead. We argue that traditional characterization of linking dynamics are insufficient to judge the faithfulness of models. We propose a new temporal sketch of an evolving graph, and introduce several new characterizations of a network's temporal dynamics. Then we propose a new family of frugal aging models with no per-node parameters and only two global parameters. Our model is based on a surprising inversion or undoing of triangle completion, where an old node relays a citation to a younger follower in its immediate vicinity. Despite very few parameters, the new family of models shows remarkably better fit with real data. Before concluding, we analyze temporal signatures for various research communities yielding further insights into their comparative dynamics. To facilitate reproducible research, we shall soon make all the codes and the processed dataset available in the public domain

Understanding the Impact of Early Citers on Long-Term Scientific Impact

This paper explores an interesting new dimension to the challenging problem of predicting long-term scientific impact (LTSI) usually measured by the number of citations accumulated by a paper in the long-term. It is well known that early citations (within 1-2 years after publication) acquired by a paper positively affects its LTSI. However, there is no work that investigates if the set of authors who bring in these early citations to a paper also affect its LTSI. In this paper, we demonstrate for the first time, the impact of these authors whom we call early citers (EC) on the LTSI of a paper. Note that this study of the complex dynamics of EC introduces a brand new paradigm in citation behavior analysis. Using a massive computer science bibliographic dataset we identify two distinct categories of EC - we call those authors who have high overall publication/citation count in the dataset as influential and the rest of the authors as non-influential. We investigate three characteristic properties of EC and present an extensive analysis of how each category correlates with LTSI in terms of these properties. In contrast to popular perception, we find that influential EC negatively affects LTSI possibly owing to attention stealing. To motivate this, we present several representative examples from the dataset. A closer inspection of the collaboration network reveals that this stealing effect is more profound if an EC is nearer to the authors of the paper being investigated. As an intuitive use case, we show that incorporating EC properties in the state-of-the-art supervised citation prediction models leads to high performance margins. At the closing, we present an online portal to visualize EC statistics along with the prediction results for a given query paper

Citation sentence reuse behavior of scientists: A case study on massive bibliographic text dataset of computer science

Our current knowledge of scholarly plagiarism is largely based on the similarity between full text research articles. In this paper, we propose an innovative and novel conceptualization of scholarly plagiarism in the form of reuse of explicit citation sentences in scientific research articles. Note that while full-text plagiarism is an indicator of a gross-level behavior, copying of citation sentences is a more nuanced micro-scale phenomenon observed even for well-known researchers. The current work poses several interesting questions and attempts to answer them by empirically investigating a large bibliographic text dataset from computer science containing millions of lines of citation sentences. In particular, we report evidences of massive copying behavior. We also present several striking real examples throughout the paper to showcase widespread adoption of this undesirable practice. In contrast to the popular perception, we find that copying tendency increases as an author matures. The copying behavior is reported to exist in all fields of computer science; however, the theoretical fields indicate more copying than the applied fields

Highly photo-stable Perovskite nanocubes: towards integrated single photon sources based on tapered nanofibers

The interest in perovskite nanocrystals (NCs) such as CsPbBr$_3$ for quantum applications is rapidly raising, as it has been demonstrated that they can behave as very efficient single photon emitters. The main problem to tackle in this context is their photo-stability under optical excitation. In this article, we present a full analysis of the optical and quantum properties of highly efficient perovskite nanocubes synthesized with an established method, which is used for the first time to produce quantum emitters, and is shown to ensure an increased photostability. These emitters exhibit reduced blinking together with a strong photon antibunching. Remarkably these features are hardly affected by the increase of the excitation intensity well above the emission saturation levels. Finally, we achieve for the first time the coupling of a single perovskite nanocube with a tapered optical nanofiber in order to aim for a compact integrated single photon source for future applications

Modeling interdisciplinary interactions among Physics, Mathematics & Computer Science

Interdisciplinarity has over the recent years have gained tremendous importance and has become one of the key ways of doing cutting edge research. In this paper we attempt to model the citation flow across three different fields -- Physics (PHY), Mathematics (MA) and Computer Science (CS). For instance, is there a specific pattern in which these fields cite one another? We carry out experiments on a dataset comprising more than 1.2 million articles taken from these three fields. We quantify the citation interactions among these three fields through temporal bucket signatures. We present numerical models based on variants of the recently proposed relay-linking framework to explain the citation dynamics across the three disciplines. These models make a modest attempt to unfold the underlying principles of how citation links could have been formed across the three fields over time.Comment: Accepted at Journal of Physics: Complexit

CASPR: Customer Activity Sequence-based Prediction and Representation

Tasks critical to enterprise profitability, such as customer churn prediction, fraudulent account detection or customer lifetime value estimation, are often tackled by models trained on features engineered from customer data in tabular format. Application-specific feature engineering adds development, operationalization and maintenance costs over time. Recent advances in representation learning present an opportunity to simplify and generalize feature engineering across applications. When applying these advancements to tabular data researchers deal with data heterogeneity, variations in customer engagement history or the sheer volume of enterprise datasets. In this paper, we propose a novel approach to encode tabular data containing customer transactions, purchase history and other interactions into a generic representation of a customer's association with the business. We then evaluate these embeddings as features to train multiple models spanning a variety of applications. CASPR, Customer Activity Sequence-based Prediction and Representation, applies Transformer architecture to encode activity sequences to improve model performance and avoid bespoke feature engineering across applications. Our experiments at scale validate CASPR for both small and large enterprise applications.Comment: Presented at the Table Representation Learning Workshop, NeurIPS 2022, New Orleans. Authors listed in random orde

Interactions between a marine heatwave and tropical cyclone Amphan in the Bay of Bengal in 2020

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Rathore, S., Goyal, R., Jangir, B., Ummenhofer, C., Feng, M., & Mishra, M. Interactions between a marine heatwave and tropical cyclone Amphan in the Bay of Bengal in 2020. Frontiers in Climate, 4, (2022): 861477, https://doi.org/10.3389/fclim.2022.861477.Interactions are diagnosed between a marine heatwave (MHW) event and tropical super cyclone Amphan in the Bay of Bengal. In May 2020, an MHW developed in the Bay of Bengal driven by coupled ocean-atmosphere processes which included shoaling of the mixed layer depth due to reduced wind speed, increased net surface shortwave radiation flux into the ocean, increased upper ocean stratification, and increased sub-surface warming. Ocean temperature, rather than salinity, dominated the stratification that contributed to the MHW development and the subsurface ocean warming that also increased tropical cyclone heat potential. The presence of this strong MHW with sea surface temperature anomalies >2.5°C in the western Bay of Bengal coincided with the cyclone track and facilitated the rapid intensification of tropical cyclone Amphan to a super cyclone in just 24 h. This rapid intensification of a short-lived tropical cyclone, with a lifespan of 5 days over the ocean, is unprecedented in the Bay of Bengal during the pre-monsoon period (March-May). As the cyclone approached landfall in northern India, the wind-induced mixing deepened the mixed layer, cooled the ocean's surface, and reduced sub-surface warming in the bay, resulting in the demise of the MHW. This study provides new perspectives on the interactions between MHWs and tropical cyclones that could aid in improving the current understanding of compound extreme events that have severe socio-economic consequences in affected countries.CU acknowledges support from the James E. and Barbara V. Moltz Fellowship for Climate-Related Research and the Independent Research & Development Program at WHOI. MF was supported by the Centre for Southern Hemisphere Oceans Research (CSHOR), which is a joint initiative between the Qingdao National Laboratory for Marine Science and Technology (QNLM), CSIRO, University of New South Wales, and the University of Tasmania