46 papers
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.
This paper presents a mathematical model using replicator dynamics to demonstrate how social feedback mechanisms—specifically positive versus negative feedback—govern whether a society undergoes a discontinuous or continuous phase transition between widespread compliance and rule-breaking, thereby explaining the fragility of social order under weak institutions.
This paper introduces a spatially explicit longitudinal sequence analysis of African political violence from 1997 to 2024, identifying six distinct conflict trajectories and their spatial interdependencies to advance conflict research beyond discrete transitions toward a holistic understanding of violence life cycles.
This paper proposes a structurally integrated framework for longitudinal science mapping that unifies thematic detection and lineage reconstruction within a single weighted relational architecture, replacing inconsistent set-theoretic overlap methods with a cohesive model of thematic evolution based on graded document affiliation and centrality-weighted structural relevance.
This paper introduces an actor-oriented expansion of the SAGES Framework to analyze how adversarial and constructive actors strategically shape narratives across five stages of evolution, demonstrating its utility through case studies of the 2021 Myanmar coup and the 2022 Russia-Ukraine war to inform countermeasures in contested information ecosystems.
This paper proposes a unified categorical framework that models knowledge graphs as free categories and employs Grothendieck topologies to construct a topos of sheaves, thereby enabling local-to-global semantic reasoning for context-dependent relational data.
This paper addresses the online minimization of polarization and disagreement in the Friedkin-Johnsen opinion dynamics model under incomplete information by proposing a two-stage low-rank matrix bandit algorithm that achieves a cumulative regret of through subspace estimation and linear bandit optimization.
The paper introduces STEP, a continuous-time stochastic framework that models temporal link prediction as a sequential forecasting problem via Poisson-driven motif transitions, achieving significant precision gains and lower runtime compared to state-of-the-art baselines while remaining compatible with existing graph neural networks.
Based on a controlled study of 737 participants, this paper demonstrates that while AI can optimize specific tasks, human guidance remains essential for effective collaborative "vibe coding," as human-led instruction significantly outperforms AI-led approaches and yields the best results when humans direct the process while AI handles evaluation.
This paper presents an algorithmic audit of TikTok revealing that while the platform technically complies with the Digital Service Act's ban on profiled advertising to minors, it effectively circumvents this protection by delivering highly personalized, often undisclosed influencer marketing content to adolescents, thereby highlighting the urgent need to expand the regulatory definition of "advertisement" to cover such commercial practices.
This paper introduces a method that transforms multivariate time series into symbolic sequences to identify statistically significant higher-order dependencies via a Bayesian approach, modeling these motifs as hyperedges in a hypergraph to reveal meaningful interactions in complex systems like neural and social networks.
This paper demonstrates that macroeconomic fluctuations and tail risk arise from the interference of overlapping "productivity waves" propagating through a production network, where the dominant transient eigenvalue of the network's dynamics, rather than the static degree distribution, determines the magnitude of aggregate volatility and the significance of granular shocks.
This article proposes a unified definition of cognitive warfare and an OODA loop-based interaction framework to address current definitional inconsistencies, enabling joint force leaders and analysts to effectively assess, compare, and evaluate cognitive campaigns through measurable attributes of superiority.
Threadle is a high-performance, memory-efficient C# engine designed to store and query massive, multilayer, mixed-mode networks by utilizing a pseudo-projection approach that avoids the prohibitive memory costs of materializing two-mode data projections.
This paper introduces "segmentedness," a statistically principled metric defined as the fraction of disallowed node-pair communications, and provides a normalized estimator with a 95% confidence interval that requires only 97 uniformly sampled node pairs to accurately measure network segmentation across various models and real-world datasets.
This paper proposes a scalable extension of CopulaGNN for link sign prediction that overcomes computational intractability by representing edge correlations via a Gramian of edge embeddings and reformulating conditional probabilities, thereby achieving linear convergence and competitive performance on signed graphs.
This paper introduces GroupMultiNeSS, a novel latent space model that simultaneously extracts shared, group-specific, and individual latent structures from grouped multiplex networks, offering improved modeling accuracy and biological insights compared to existing approaches.
This paper provides a comprehensive analysis of the dynamical behaviors, classification, and various forms of stability for all 39 possible two-node McCulloch-Pitts networks with self-loops and ternary weights, highlighting how model variants, value encodings, and regulatory structures fundamentally influence system dynamics and robustness.
This paper introduces HIT-Leiden, a novel algorithm that efficiently maintains high-quality Leiden communities in large dynamic graphs by bounding the scope of updates through hierarchical structures, achieving speedups of up to five orders of magnitude over existing methods while meeting stringent production latency requirements.
This study analyzes socialbot activity in modern Japanese social media, revealing that their influence exceeds that observed during the 2016 U.S. presidential election and that human-retweeted socialbots are difficult to distinguish from genuine human accounts.