45 papers
This paper introduces a large-scale, privacy-preserving dataset of ten years of user interactions on the Austrian newspaper DerStandard, comprising over 75 million comments and 400 million votes with anonymized identifiers and pre-computed vector embeddings to facilitate research on online discourse dynamics in the German language.
This paper introduces BOPIM, a Bayesian Optimization framework for influence maximization on temporal networks that utilizes novel kernel functions and a greedy acquisition strategy to achieve superior performance and significant speed improvements over existing methods while uniquely quantifying uncertainty in optimal seed sets.
This paper introduces PixelConfig, a framework for reverse-engineering Meta Pixel configurations, which reveals that default settings drive widespread adoption of activity and identity tracking features capable of capturing sensitive health data, while existing tracking restriction mechanisms offer limited practical protection.
The paper proposes VNOIP, a novel method leveraging variational neural Ordinary Differential Equations with bidirectional jump ODEs and attention mechanisms to explicitly model continuous-time popularity trend dynamics, thereby significantly improving the accuracy and efficiency of information popularity prediction in online social networks compared to existing state-of-the-art approaches.
This study audits Google's reverse image search and finds that it functions as an ineffective gatekeeper against visual misinformation, often prioritizing irrelevant content and repeated falsehoods over debunking information, particularly during the initial emergence of visual falsehoods.
This paper demonstrates that on Geometric Inhomogeneous Random Graphs, majority-vote opinion dynamics lead to the stabilization of localized opinion domains rather than global consensus, a phenomenon rigorously explained by a mean-field model showing a stationary interface profile that supports persistent opinion diversity.
This paper introduces the Mixture of Interacting Cascades (MIC), a joint user-topic interaction model based on marked multidimensional Hawkes processes that outperforms existing methods in modeling information spread and provides insightful visualizations of social network activity.
This study introduces an LLM-agent framework to simulate U.S. citizens' attitudes toward China from 2005 to 2025, demonstrating that while subjective news framing has a modest impact on negative attitudes, a "devil's advocate" agent is the most effective mechanism for debiasing opinions and producing more human-like cognitive outcomes.
This paper employs an equilibrium model to demonstrate that while news feed algorithms prioritizing viral stories can enhance information aggregation, they also risk creating self-perpetuating steady states where rational users collectively share and reinforce incorrect information, highlighting critical implications for platform design.
The paper proposes DS-DGA-GCN, an adaptive graph learning model that integrates diversity- and similarity-aware dynamic graph attention with a Network Feature Scoring system to effectively detect organized fake reviewer groups in dynamic networks, achieving state-of-the-art performance on real-world datasets.
This paper proposes a unified, domain-agnostic framework for computing the bus-factor by modeling projects as bipartite graphs, proving the NP-hardness of both redundancy and criticality formulations, and introducing a novel robustness-based measure with efficient linear-time approximations that outperforms existing methods in capturing project risk and fragmentation.
This paper presents an end-to-end framework analyzing the r/Brexit subreddit to demonstrate that political polarization on Reddit is driven by self-selection and echo chambers, where user opinions become entrenched rather than softened by cross-cutting exposure.
This paper proposes a framework integrating transition state theory with dynamic network modeling to characterize and predict discrete structural changes, such as faction realignment, often using cross-sectional data to forecast future network evolution.
This paper addresses the cold-start scenario in active correlation clustering, where no initial pairwise similarities are available, by proposing a coverage-aware method that encourages diversity to efficiently query similarities and achieve effective clustering.
This paper proposes a novel local search algorithm with a balanced optimization objective and provable linear convergence to efficiently identify polarized communities in signed networks, effectively addressing size imbalance and scaling to large datasets while outperforming state-of-the-art methods.
This paper demonstrates that optimizing for objectives like profit or crime reduction while maintaining a zero Average Treatment Effect (ATE) can mask significant unfairness driven by confounding, thereby arguing that fairness regulations must shift from evaluating aggregate decision-level outcomes to scrutinizing model-level causal mechanisms.
This paper introduces a novel operator framework based on two-path decompositions to compress ego-centered networks via safe quotient constructions, proving a transfer theorem that bounds the error in two-walk mass and characterizing conditions for exact preservation through wedge-equitable partitions.
This paper introduces a maximum-entropy framework for continuous-time temporal networks that factorizes interactions into global time processes and static edge probabilities, enabling closed-form analytical solutions and effective generative models that outperform standard Poisson processes.
This paper demonstrates that projecting users and entities into a joint social embedding space derived from large-scale Twitter data enables effective zero-shot cross-domain preference modeling and personalization, revealing that socio-demographic factors encoded in these embeddings correlate with user interests across diverse topics.
This study reveals that while Brasília's modernist urban form appears integrated at coarse scales, it actually fosters significant segregation at the block level where daily interactions occur, driven more by enclosed residential designs and barriers than by visit volumes, suggesting that true integration requires distributing shared destinations and porous connections rather than sequestering them.