pwc-archive/papers-with-abstracts huggingface dataset:
How Cohesive Are Community Search Results on Online Social Networks?: An Experimental Evaluation
Authors: Yining Zhao, Sourav S Bhowmick, Nastassja L. Fischer, SH Annabel Chen
Date: 2025-04-28 00:00:00
Tasks: Attribute, Community Search

Recently, numerous community search methods for large graphs have been proposed, at the core of which is defining and measuring cohesion. This paper experimentally evaluates the effectiveness of these community search algorithms w.r.t. cohesiveness in the context of online social networks. Social communities are formed and developed under the influence of group cohesion theory, which has been extensively studied in social psychology. However, current generic methods typically measure cohesiveness using structural or attribute-based approaches and overlook domain-specific concepts such as group cohesion. We introduce five novel psychology-informed cohesiveness measures, based on the concept of group cohesion from social psychology, and propose a novel framework called CHASE for evaluating eight representative community search algorithms w.r.t. these measures on online social networks. Our analysis reveals that there is no clear correlation between structural and psychological cohesiveness, and no algorithm effectively identifies psychologically cohesive communities in online social networks. This study provides new insights that could guide the development of future community search methods.
D-Tracker: Modeling Interest Diffusion in Social Activity Tensor Data Streams
Authors: Shingo Higashiguchi, Yasuko Matsubara, Koki Kawabata, Taichi Murayama, Yasushi Sakurai
Date: 2025-05-01 00:00:00
Tasks: Tensor Decomposition

Large quantities of social activity data, such as weekly web search volumes and the number of new infections with infectious diseases, reflect peoples' interests and activities. It is important to discover temporal patterns from such data and to forecast future activities accurately. However, modeling and forecasting social activity data streams is difficult because they are high-dimensional and composed of multiple time-varying dynamics such as trends, seasonality, and interest diffusion. In this paper, we propose D-Tracker, a method for continuously capturing time-varying temporal patterns within social activity tensor data streams and forecasting future activities. Our proposed method has the following properties: (a) Interpretable: it incorporates the partial differential equation into a tensor decomposition framework and captures time-varying temporal patterns such as trends, seasonality, and interest diffusion between locations in an interpretable manner; (b) Automatic: it has no hyperparameters and continuously models tensor data streams fully automatically; (c) Scalable: the computation time of D-Tracker is independent of the time series length. Experiments using web search volume data obtained from GoogleTrends, and COVID-19 infection data obtained from COVID-19 Open Data Repository show that our method can achieve higher forecasting accuracy in less computation time than existing methods while extracting the interest diffusion between locations. Our source code and datasets are available at {https://github.com/Higashiguchi-Shingo/D-Tracker.
Scalable Meta-Learning via Mixed-Mode Differentiation
Authors: Iurii Kemaev, Dan A Calian, Luisa M Zintgraf, Gregory Farquhar, Hado van Hasselt
Date: 2025-05-01 00:00:00
Tasks: Meta-Learning

Gradient-based bilevel optimisation is a powerful technique with applications in hyperparameter optimisation, task adaptation, algorithm discovery, meta-learning more broadly, and beyond. It often requires differentiating through the gradient-based optimisation process itself, leading to "gradient-of-a-gradient" calculations with computationally expensive second-order and mixed derivatives. While modern automatic differentiation libraries provide a convenient way to write programs for calculating these derivatives, they oftentimes cannot fully exploit the specific structure of these problems out-of-the-box, leading to suboptimal performance. In this paper, we analyse such cases and propose Mixed-Flow Meta-Gradients, or MixFlow-MG -- a practical algorithm that uses mixed-mode differentiation to construct more efficient and scalable computational graphs yielding over 10x memory and up to 25% wall-clock time improvements over standard implementations in modern meta-learning setups.
Bayes-Optimal Fair Classification with Multiple Sensitive Features
Authors: Yi Yang, Yinghui Huang, Xiangyu Chang
Date: 2025-05-01 00:00:00
Tasks: Attribute, Classification, Fairness

Existing theoretical work on Bayes-optimal fair classifiers usually considers a single (binary) sensitive feature. In practice, individuals are often defined by multiple sensitive features. In this paper, we characterize the Bayes-optimal fair classifier for multiple sensitive features under general approximate fairness measures, including mean difference and mean ratio. We show that these approximate measures for existing group fairness notions, including Demographic Parity, Equal Opportunity, Predictive Equality, and Accuracy Parity, are linear transformations of selection rates for specific groups defined by both labels and sensitive features. We then characterize that Bayes-optimal fair classifiers for multiple sensitive features become instance-dependent thresholding rules that rely on a weighted sum of these group membership probabilities. Our framework applies to both attribute-aware and attribute-blind settings and can accommodate composite fairness notions like Equalized Odds. Building on this, we propose two practical algorithms for Bayes-optimal fair classification via in-processing and post-processing. We show empirically that our methods compare favorably to existing methods.
LLM-Assisted Automated Deductive Coding of Dialogue Data: Leveraging Dialogue-Specific Characteristics to Enhance Contextual Understanding
Authors: Ying Na, Shihui Feng
Date: 2025-04-28 00:00:00

Dialogue data has been a key source for understanding learning processes, offering critical insights into how students engage in collaborative discussions and how these interactions shape their knowledge construction. The advent of Large Language Models (LLMs) has introduced promising opportunities for advancing qualitative research, particularly in the automated coding of dialogue data. However, the inherent contextual complexity of dialogue presents unique challenges for these models, especially in understanding and interpreting complex contextual information. This study addresses these challenges by developing a novel LLM-assisted automated coding approach for dialogue data. The novelty of our proposed framework is threefold: 1) We predict the code for an utterance based on dialogue-specific characteristics -- communicative acts and communicative events -- using separate prompts following the role prompts and chain-of-thoughts methods; 2) We engaged multiple LLMs including GPT-4-turbo, GPT-4o, DeepSeek in collaborative code prediction; 3) We leveraged the interrelation between events and acts to implement consistency checking using GPT-4o. In particular, our contextual consistency checking provided a substantial accuracy improvement. We also found the accuracy of act predictions was consistently higher than that of event predictions. This study contributes a new methodological framework for enhancing the precision of automated coding of dialogue data as well as offers a scalable solution for addressing the contextual challenges inherent in dialogue analysis.
Hypothesis-free discovery from epidemiological data by automatic detection and local inference for tree-based nonlinearities and interactions
Authors: Giorgio Spadaccini, Marjolein Fokkema, Mark A. van de Wiel
Date: 2025-05-01 00:00:00

In epidemiological settings, Machine Learning (ML) is gaining popularity for hypothesis-free discovery of risk (or protective) factors. Although ML is strong at discovering non-linearities and interactions, this power is currently compromised by a lack of reliable inference. Although local measures of feature effect can be combined with tree ensembles, uncertainty quantifications for these measures remain only partially available and oftentimes unsatisfactory. We propose RuleSHAP, a framework for using rule-based, hypothesis-free discovery that combines sparse Bayesian regression, tree ensembles and Shapley values in a one-step procedure that both detects and tests complex patterns at the individual level. To ease computation, we derive a formula that computes marginal Shapley values more efficiently for our setting. We demonstrate the validity of our framework on simulated data. To illustrate, we apply our machinery to data from an epidemiological cohort to detect and infer several effects for high cholesterol and blood pressure, such as nonlinear interaction effects between features like age, sex, ethnicity, BMI and glucose level.
Observational Learning with a Budget
Authors: Shuo Wu, Pawan Poojary, Randall Berry
Date: 2025-04-28 00:00:00

We consider a model of Bayesian observational learning in which a sequence of agents receives a private signal about an underlying binary state of the world. Each agent makes a decision based on its own signal and its observations of previous agents. A central planner seeks to improve the accuracy of these signals by allocating a limited budget to enhance signal quality across agents. We formulate and analyze the budget allocation problem and propose two optimal allocation strategies. At least one of these strategies is shown to maximize the probability of achieving a correct information cascade.
Geometry-aware Active Learning of Spatiotemporal Dynamic Systems
Authors: Xizhuo Zhang, Bing Yao
Date: 2025-04-26 00:00:00
Tasks: 3D geometry, Active Learning, Prediction

Rapid developments in advanced sensing and imaging have significantly enhanced information visibility, opening opportunities for predictive modeling of complex dynamic systems. However, sensing signals acquired from such complex systems are often distributed across 3D geometries and rapidly evolving over time, posing significant challenges in spatiotemporal predictive modeling. This paper proposes a geometry-aware active learning framework for modeling spatiotemporal dynamic systems. Specifically, we propose a geometry-aware spatiotemporal Gaussian Process (G-ST-GP) to effectively integrate the temporal correlations and geometric manifold features for reliable prediction of high-dimensional dynamic behaviors. In addition, we develop an adaptive active learning strategy to strategically identify informative spatial locations for data collection and further maximize the prediction accuracy. This strategy achieves the adaptive trade-off between the prediction uncertainty in the G-ST-GP model and the space-filling design guided by the geodesic distance across the 3D geometry. We implement the proposed framework to model the spatiotemporal electrodynamics in a 3D heart geometry. Numerical experiments show that our framework outperforms traditional methods lacking the mechanism of geometric information incorporation or effective data collection.
Efficient Learning on Large Graphs using a Densifying Regularity Lemma
Authors: Jonathan Kouchly, Ben Finkelshtein, Michael Bronstein, Ron Levie
Date: 2025-04-25 00:00:00
Tasks: Graph Neural Network, Knowledge Graph Completion, LEMMA, Node Classification

Learning on large graphs presents significant challenges, with traditional Message Passing Neural Networks suffering from computational and memory costs scaling linearly with the number of edges. We introduce the Intersecting Block Graph (IBG), a low-rank factorization of large directed graphs based on combinations of intersecting bipartite components, each consisting of a pair of communities, for source and target nodes. By giving less weight to non-edges, we show how to efficiently approximate any graph, sparse or dense, by a dense IBG. Specifically, we prove a constructive version of the weak regularity lemma, showing that for any chosen accuracy, every graph, regardless of its size or sparsity, can be approximated by a dense IBG whose rank depends only on the accuracy. This dependence of the rank solely on the accuracy, and not on the sparsity level, is in contrast to previous forms of the weak regularity lemma. We present a graph neural network architecture operating on the IBG representation of the graph and demonstrating competitive performance on node classification, spatio-temporal graph analysis, and knowledge graph completion, while having memory and computational complexity linear in the number of nodes rather than edges.
Do global forecasting models require frequent retraining?
Authors: Marco Zanotti
Date: 2025-05-01 00:00:00
Tasks: Computational Efficiency

In an era of increasing computational capabilities and growing environmental consciousness, organizations face a critical challenge in balancing the accuracy of forecasting models with computational efficiency and sustainability. Global forecasting models, lowering the computational time, have gained significant attention over the years. However, the common practice of retraining these models with new observations raises important questions about the costs of forecasting. Using ten different machine learning and deep learning models, we analyzed various retraining scenarios, ranging from continuous updates to no retraining at all, across two large retail datasets. We showed that less frequent retraining strategies maintain the forecast accuracy while reducing the computational costs, providing a more sustainable approach to large-scale forecasting. We also found that machine learning models are a marginally better choice to reduce the costs of forecasting when coupled with less frequent model retraining strategies as the frequency of the data increases. Our findings challenge the conventional belief that frequent retraining is essential for maintaining forecasting accuracy. Instead, periodic retraining offers a good balance between predictive performance and efficiency, both in the case of point and probabilistic forecasting. These insights provide actionable guidelines for organizations seeking to optimize forecasting pipelines while reducing costs and energy consumption.
Revisiting Algorithmic Audits of TikTok: Poor Reproducibility and Short-term Validity of Findings
Authors: Matej Mosnar, Adam Skurla, Branislav Pecher, Matus Tibensky, Jan Jakubcik, Adrian Bindas, Peter Sakalik, Ivan Srba
Date: 2025-04-25 00:00:00
Tasks: Recommendation Systems

Social media platforms are constantly shifting towards algorithmically curated content based on implicit or explicit user feedback. Regulators, as well as researchers, are calling for systematic social media algorithmic audits as this shift leads to enclosing users in filter bubbles and leading them to more problematic content. An important aspect of such audits is the reproducibility and generalisability of their findings, as it allows to draw verifiable conclusions and audit potential changes in algorithms over time. In this work, we study the reproducibility of the existing sockpuppeting audits of TikTok recommender systems, and the generalizability of their findings. In our efforts to reproduce the previous works, we find multiple challenges stemming from social media platform changes and content evolution, but also the research works themselves. These drawbacks limit the audit reproducibility and require an extensive effort altogether with inevitable adjustments to the auditing methodology. Our experiments also reveal that these one-shot audit findings often hold only in the short term, implying that the reproducibility and generalizability of the audits heavily depend on the methodological choices and the state of algorithms and content on the platform. This highlights the importance of reproducible audits that allow us to determine how the situation changes in time.
Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction
Authors: Maximilian Schuessler, Erik Sverdrup, Robert Tibshirani
Date: 2025-05-01 00:00:00
Tasks: Prognosis

Robust estimation of heterogeneous treatment effects is a fundamental challenge for optimal decision-making in domains ranging from personalized medicine to educational policy. In recent years, predictive machine learning has emerged as a valuable toolbox for causal estimation, enabling more flexible effect estimation. However, accurately estimating conditional average treatment effects (CATE) remains a major challenge, particularly in the presence of many covariates. In this article, we propose pretraining strategies that leverage a phenomenon in real-world applications: factors that are prognostic of the outcome are frequently also predictive of treatment effect heterogeneity. In medicine, for example, components of the same biological signaling pathways frequently influence both baseline risk and treatment response. Specifically, we demonstrate our approach within the R-learner framework, which estimates the CATE by solving individual prediction problems based on a residualized loss. We use this structure to incorporate side information and develop models that can exploit synergies between risk prediction and causal effect estimation. In settings where these synergies are present, this cross-task learning enables more accurate signal detection, yields lower estimation error, reduced false discovery rates, and higher power for detecting heterogeneity.
Reinforcement Learning with Continuous Actions Under Unmeasured Confounding
Authors: Yuhan Li, Eugene Han, Yifan Hu, Wenzhuo Zhou, Zhengling Qi, Yifan Cui, Ruoqing Zhu
Date: 2025-05-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

This paper addresses the challenge of offline policy learning in reinforcement learning with continuous action spaces when unmeasured confounders are present. While most existing research focuses on policy evaluation within partially observable Markov decision processes (POMDPs) and assumes discrete action spaces, we advance this field by establishing a novel identification result to enable the nonparametric estimation of policy value for a given target policy under an infinite-horizon framework. Leveraging this identification, we develop a minimax estimator and introduce a policy-gradient-based algorithm to identify the in-class optimal policy that maximizes the estimated policy value. Furthermore, we provide theoretical results regarding the consistency, finite-sample error bound, and regret bound of the resulting optimal policy. Extensive simulations and a real-world application using the German Family Panel data demonstrate the effectiveness of our proposed methodology.
Leveraging Surplus Electricity: Profitability of Bitcoin Mining as a National Strategy in South Korea
Authors: Yoonseul Choi, Jaehong Jeong, Jungsoon Choi
Date: 2025-05-01 00:00:00

This study examines the feasibility and profitability of utilizing surplus electricity for Bitcoin mining. Surplus electricity refers to the remaining electricity after net metering, which can be repurposed for Bitcoin mining to improve Korea Electric Power Corporation's (KEPCO) energy resource efficiency and alleviate its debt challenges. Net metering (or net energy metering) is an electricity billing mechanism that allows consumers who generate some or all of their own electricity to use that electricity when they want, rather than when it is produced. Using the latest Bitcoin miner, the Antminer S21 XP Hyd, the study evaluates daily Bitcoin mining when operating at 30,565 and 45,439 units, incorporating Bitcoin network hash rates to assess profitability. To examine profitability, the Random Forest Regressor and Long Short-Term Memory models were used to predict the Bitcoin price. The analysis shows that the use of excess electricity for Bitcoin mining not only generates economic revenue, but also minimizes energy loss, reduces debt, and resolves unsettled payment issues for KEPCO. This study empirically investigates and analyzes the integration of electricity surplus in South Korea with bitcoin mining for the first time. The findings highlight the potential to strengthen the financial stability of KEPCO and demonstrate the feasibility of Bitcoin mining. In addition, this research serves as a foundational resource for future advancements in the Bitcoin mining industry and the efficient use of energy resources.
GeoRDF2Vec Learning Location-Aware Entity Representations in Knowledge Graphs
Authors: Martin Boeckling, Heiko Paulheim, Sarah Detzler
Date: 2025-04-23 00:00:00
Tasks: Knowledge Graphs

Many knowledge graphs contain a substantial number of spatial entities, such as cities, buildings, and natural landmarks. For many of these entities, exact geometries are stored within the knowledge graphs. However, most existing approaches for learning entity representations do not take these geometries into account. In this paper, we introduce a variant of RDF2Vec that incorporates geometric information to learn location-aware embeddings of entities. Our approach expands different nodes by flooding the graph from geographic nodes, ensuring that each reachable node is considered. Based on the resulting flooded graph, we apply a modified version of RDF2Vec that biases graph walks using spatial weights. Through evaluations on multiple benchmark datasets, we demonstrate that our approach outperforms both non-location-aware RDF2Vec and GeoTransE.
Inference for max-linear Bayesian networks with noise
Authors: Mark Adams, Kamillo Ferry, Ruriko Yoshida
Date: 2025-05-01 00:00:00
Tasks: Causal Inference

Max-Linear Bayesian Networks (MLBNs) provide a powerful framework for causal inference in extreme-value settings; we consider MLBNs with noise parameters with a given topology in terms of the max-plus algebra by taking its logarithm. Then, we show that an estimator of a parameter for each edge in a directed acyclic graph (DAG) is distributed normally. We end this paper with computational experiments with the expectation and maximization (EM) algorithm and quadratic optimization.
Creating and Evaluating Code-Mixed Nepali-English and Telugu-English Datasets for Abusive Language Detection Using Traditional and Deep Learning Models
Authors: Manish Pandey, Nageshwar Prasad Yadav, Mokshada Adduru, Sawan Rai
Date: 2025-04-23 00:00:00
Tasks: Abuse Detection, Abusive Language

With the growing presence of multilingual users on social media, detecting abusive language in code-mixed text has become increasingly challenging. Code-mixed communication, where users seamlessly switch between English and their native languages, poses difficulties for traditional abuse detection models, as offensive content may be context-dependent or obscured by linguistic blending. While abusive language detection has been extensively explored for high-resource languages like English and Hindi, low-resource languages such as Telugu and Nepali remain underrepresented, leaving gaps in effective moderation. In this study, we introduce a novel, manually annotated dataset of 2 thousand Telugu-English and 5 Nepali-English code-mixed comments, categorized as abusive and non-abusive, collected from various social media platforms. The dataset undergoes rigorous preprocessing before being evaluated across multiple Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs). We experimented with models including Logistic Regression, Random Forest, Support Vector Machines (SVM), Neural Networks (NN), LSTM, CNN, and LLMs, optimizing their performance through hyperparameter tuning, and evaluate it using 10-fold cross-validation and statistical significance testing (t-test). Our findings provide key insights into the challenges of detecting abusive language in code-mixed settings and offer a comparative analysis of computational approaches. This study contributes to advancing NLP for low-resource languages by establishing benchmarks for abusive language detection in Telugu-English and Nepali-English code-mixed text. The dataset and insights can aid in the development of more robust moderation strategies for multilingual social media environments.