1,720,972 research outputs found
MOTIF-Driven Contrastive Learning of Graph Representations
No full textWe propose a MOTIF-driven contrastive framework to pretrain a graph neural network in a self-supervised manner so that it can automatically mine motifs from large graph datasets. Our framework achieves state-of-the-art results on various graph-level downstream tasks with few labels, like molecular property prediction
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On the Contextual Unfairness of Modern Machine Learning: Graph Neural Networks to Large Language Models
Full text linkGraph neural networks (GNNs) and large language models (LLMs) have emerged as popular machine learning (ML) models for powering applications such as social recommendation on social media platforms and chat-based assistants, respectively. GNNs and LLMs are both contextual models: GNNs operate on social context in social networks, while LLMs process syntactic and semantic context in language. In conjunction with the proliferation of GNNs and LLMs, there is decreasing trust in the fairness of ML. Unfair ML models cause real-world harm, such as the reinforcement of stereotypes and discrimination in hiring. The unfairness of GNNs is exacerbated by social context (e.g., graph structure, message passing). However, this aspect is not explored in research on the fairness of traditional ML models and requires a deeper principled understanding. Moreover, the open-ended nature of LLM generations can make automatic evaluations of syntactic and semantic context-dependent unfairness difficult.This dissertation tackles technical challenges in addressing the unfairness of GNNs and LLMs. In the first part, we theoretically and empirically investigate different forms of GNN unfairness (i.e., imputation bias, preferential attachment bias, degree bias), and how they are affected by graph structure and the choice of graph filter. We further propose principled metrics and methods to alleviate GNN unfairness. In the second part of this dissertation, we assess the measurement validity of evaluations of LLM misgendering. In the final part, we return to the relatively simple setting of feedforward neural networks, and even in this setting, we identify and tackle major challenges in obtaining a precise analytical theory of how model design choices and data properties contribute to unfairness. Such a theory for GNNs and LLMs could aid in interpreting model outputs and designing stronger evaluation and mitigation methods for unfairness. Overall, this dissertation develops a principled understanding of and addresses the unfairness of modern ML models, towards preventing the further entrenchment of social inequalities and promoting justice
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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