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At least 19 recordsLinked to original sources

arXiv2026

Filling holes in science draws collective attention, but most higher-order holes remain unexplored

Most scientific discoveries justify their significance by filling holes between existing ideas and arguments. Representing knowledge as high-dimensional concept embeddings, we use persistent homology to detect scientific holes of increasing order, from lower-order gaps between disconnected ideas to higher-order cavities within unevenly connected ones; then we identify the research that fills them. Researchers who close holes draw outsized collective attention, staging novelty that the citing literature rewards, most strongly in discovery-oriented fields and least in design-oriented fields. Closing a hole is followed by persistent work on the same combinations of concepts at many times the rate of comparable holes left open. Yet as knowledge grows, higher-order holes explode while the fraction of research that fills them collapses, leaving most higher-order combinations unexplored. These results call for a richer science of holes and mark a frontier where contemporary AI might help fill the gaps human science opens.

cs.CY↗

arXiv2026

A Network Science Perspective on Evaluating Deep Graph Generative Models

Traditional network models from network science, such as the Erdos-Renyi and configuration models, generate random networks that reproduce few selected topological properties observed in real-world networks. Deep graph generative models emerge as a data-driven approach, leveraging deep neural network architectures to learn complex structural distributions directly from real-world networks to generate more realistic synthetic networks. Because real social contact networks cannot be shared due to privacy risks, synthetic networks serve as an alternative for developing and evaluating epidemic mitigation strategies. In this work, we evaluate deep graph generative models as well as the configuration from a network science perspective by assessing both the topological similarity between generated and real-world networks and their utility in identifying effective node immunization strategies to sup- press epidemic/misinformation spreading. It is found that two deep graph generative models produce synthetic networks that closely resemble the structural properties of real-world networks, enabling them to identify effective immunization strategies.

cs.SI↗

arXiv2026

Inflation and the Distortion of Relative Prices

This paper presents a production network model of inflationary dynamics in which inflation can have near-zero correlation with the size of price change yet generate significant distortions in relative prices. New money enters unevenly across firms and percolates through buyer--seller links, thereby displacing relative prices even when every price is free to adjust and every market clears. Under state-dependent price stickiness, firms absorb higher inflation through more frequent rather than larger price changes, leaving the distortion intact. The distortion depends critically on the spectral gap of the production network, which sets the rate at which the economy converges to equilibrium. We quantify the mechanism on a reconstructed production network with the universe of firms in the United States. At a moderate rate of inflation, the typical firm's relative-price distortion is of the same magnitude as one quarter's inflation. The size of price change remains essentially uncorrelated with the magnitude of relative-price distortion.

econ.TH↗

arXiv2026

Looking for Bidding Teammates: A Game-Theoretic Model of Stranger Collusion in Peer Review

Paper bidding is the entry point to reviewer assignment at large CS conferences: reviewers declare interest, combined by an optimizer with automated affinity scores. Reviewers who have never met recruit each other online, exchange identifiers, bid on each other's papers, and reciprocate with inflated scores. Existing collusion models assume already-trusted colleagues; open recruitment removes that assumption and the mechanism that made such arrangements work. We give the first game-theoretic model of collusion \emph{formation} in peer review, the \emph{Mutual Bidding Dilemma}: a four-stage game covering recruitment, exchange of identifiers under risk of being reported, unverifiable bidding, and reciprocal reviewing. The model predicts the arrangement cannot form: once assigned a partner's paper, writing the inflated review is pure cost, since the benefit depends on the partner's own decision. Reciprocation is never individually rational, for any payoffs, and the arrangement unwinds. What closes the gap is enforcement, not incentives: authors see their own reviews, deadlines recur every few months, and the group remembers who reciprocated. We derive the condition under which inflation is sustainable, the detection rate above which no partnership survives, and show effort enters both. On a calibrated end-to-end conference simulation, no detector we test exceeds $F_1 = 0.322$ against an attacker who camouflages bids, spreads them around a ring, and manipulates affinity; a two-person arrangement is worth $3.2$ points of acceptance probability; and the harm is distributional, not aggregate: $70$ honest papers are displaced while mean quality moves by only $0.002$, so no summary statistic reveals it. Randomized assignment is the one defense reaching enforcement itself, making a partner who never bid indistinguishable from one who bid and lost.

cs.GT↗

arXiv2026

Higher-order rich clubs and configuration models on general directed hypergraphs

Detecting structure in complex networks, especially those arising from physical systems, is a central problem across the sciences. One approach is via rich club analysis, which identifies important vertices using a centrality metric and measures whether those vertices are more tightly interconnected than expected by chance. While informative, this approach captures only pairwise interactions, missing out on higher-order ones known to shape the structure and function of many complex systems. We propose a hyper-rich club pipeline that asks whether central vertices are more tightly interconnected than expected by chance through hyperedges encoding higher-order interactions, which also enables the inclusion of important, often omitted, directional information. We work in a broad class of hypergraphs, which we call general directed hypergraphs, that includes as special cases undirected hypergraphs, head-and-tail directed hypergraphs, and totally ordered hypergraphs (a hypergraph related to directed simplicial complexes from topological data analysis). This unifies several non-equivalent notions of directed hypergraph under one definition. On these hypergraphs we define a hyper-rich club framework whose concrete construction depends on explicit choices the domain scientist fixes according to their research goals. Particular choices recover the existing rich club notions for graphs and undirected hypergraphs, and yield the first such notion for each version of directed hypergraphs. We demonstrate that the pipeline recovers meaningful structure in data by studying networks of very different origins: connectomes, temporal networks of infectious spread, networks of poems, and the XGI hypergraph database, in each case detecting structure the standard graph rich club misses.

cs.SI↗

arXiv2026

Past-aware game-theoretic centrality: a framework for cardinality-constrained set-function maximization on networks

We consider cardinality-constrained optimization of set functions over the nodes of a graph. The standard greedy algorithm selects each node according to its immediate marginal contribution, a local criterion that may fail to anticipate the synergies within the final set. We introduce past-aware game-theoretic centrality (PAGTC), which evaluates a candidate node through its expected marginal contribution over the possible completions of the current partial solution to the prescribed target size. This yields a sequential selection strategy that explicitly accounts for the final budget. For nonnegative monotone submodular objectives and a budget $r$, we prove an approximation guarantee of $r/(2r-1)$ and derive computable a posteriori bounds. Since direct PAGTC evaluation involves averaging over a large number of coalitions, we extend an exact computation framework for game-theoretic centrality and derive efficiently computable expressions for two classes of graph-optimization problems, namely facility location and influence in complex contagion, covering both submodular and non-submodular cases. The numerical results show that the benefits depend on the objective and are most pronounced for complex contagion, where submodularity does not hold.

cs.SI↗

arXiv2026

Mapping the Reddit Bot Ecosystem: Taxonomy and Evolution

Automated agents increasingly participate in online communities, yet their population structure and roles remain poorly understood. Using a dataset of 3,389 identified bots and their full activity histories, we construct a taxonomy of bot "species" on the news aggregation and social media platform Reddit based on temporal, community, linguistic, and semantic features. Clustering analysis reveals 18 distinct bot types spanning content-specialized, behavior-driven, and infrastructural roles such as moderation and utility support. In addition, temporal analysis shows that bot numbers and activity expanded rapidly before peaking around the COVID-19 period, then started declining even before Reddit's 2023 API policy changes. However, the overall diversity of bot species has remained remarkably stable. These findings suggest that online bot populations form evolving digital ecosystems.

cs.SI↗

arXiv2026

Topology-induced Operators Reveal Complementary Graph Representations without Training

Graph representation learning has largely focused on designing increasingly sophisticated models to transform graph topology into vector representations, or embeddings. However, the extent to which embedding quality depends on model learning, rather than on the underlying topological transformations, remains unclear. Here, we show that informative embeddings can be derived without complicated model design and gradient-based training. Propagating random features through implicit hierarchical structures induced by random walks and anonymous walks yields embeddings that capture node proximity and structural role, respectively. These two training-free embeddings preserve complementary aspects of graph organization and perform competitively with classic and recent methods across various node-, edge-, and graph-level tasks. They often require substantially less computation, resulting in a favorable quality-efficiency trade-off. Combining the two types of embeddings further improves inference quality of some tasks compared with using either embedding type alone. Our results suggest that informative graph embeddings can arise from carefully chosen topological transformations before any learning operation is applied.

cs.LG↗

arXiv2026

The conservative turn in science: The changing character of knowledge recombination

This study examines how the dominant mode of cross-disciplinary knowledge combination has changed over time. It addresses a limitation of existing studies that document declining disruptiveness at the level of individual works but do not reveal whether the mechanisms of cross-disciplinary knowledge flow have themselves shifted. Drawing on 56 million publications and 816 million citations from OpenAlex (1960-2025), the study classifies cross-disciplinary citations into five types based on the set relationship between the subfield portfolios of citing and cited publications and introduces a semantic-distance measure derived from SPECTER-v2 embeddings of publication abstracts. It subsequently demonstrates that cross-disciplinary citations have shifted markedly away from configurations with no shared disciplinary ground toward types built on partially overlapping knowledge bases, a reorientation further reflected in a steady decline in average semantic distance between citing and cited fields. This shift co-occurs with a diffusion of brokerage positions across the network and a greater reliance on older literature, consistent with a system that has become broader and more decentralized in its interdisciplinary reach. Finally, this study discusses the implications of these findings for science policy and for the design of cross-disciplinary funding mechanisms.

cs.SI↗

arXiv2026

Endorsement Without New Evidence: How Sequential Voting Inflates Mandates in Online Community Governance

Online communities often treat large support margins in public elections as strong mandates. We argue that such margins can overstate the independent scrutiny behind a decision. Using 198,275 free-text rationales from Wikipedia admin elections, we introduce vote-text divergence, a measure that flags a decisive vote paired with a thin, deferential rationale. Divergence rises as voters arrive later, even after controlling for voter and election fixed effects. The pattern is consistent with information saturation: once prior text is accounted for, arrival order no longer predicts divergence, while accumulated prior evidence does. The effect is strongest among peripheral voters in the co-voting network. Yet divergence does not predict worse post-promotion outcomes, such as administrative activity or survival. Public tallies can therefore weaken the scrutiny signal even while selecting capable administrators: a margin may appear to reflect more consensus and support than it actually contains.

cs.SI↗

arXiv2026

Ephemeral Feeds and Enduring Rituals: RushTok and the Formation of Event-Based Algorithmic Communities

Each August, TikTok's For You page turns the University of Alabama's sorority recruitment into RushTok. We examine RushTok as an event-based algorithmic community: a collective assembled around a bounded offline ritual and sustained by recommendation. Using a mixed-methods survey (n=71) and a reflexive account of creator outreach, we ask who participates, how, and with what stakes. Findings show an ambiguous and entertainment based throughline; many called it a community (51/71) but few claimed membership (11/71). Affiliation centered on creators rather than shared practices, with parasocial attention clustering around a small set of potential new members (PNMs) and returning figures. Higher content exposure tracked with self-identification as a community member; those members commented, followed creators, and engaged across videos. Attempts to interview creators were met with silence or refusals, reflecting community boundary-work despite viral visibility. We outline implications for platform governance, including time-bounded context, graduated visibility, and aftercare.

cs.HC↗

arXiv2026

Democracy Needs Reach: Political Equality, Online Speech, and Algorithmic Recommendation

Within democracies, the capacity to influence political outcomes through speech depends not only on the right to express oneself, but also on the opportunity to reach relevant audiences. In this paper, I argue that the unequal distribution of algorithmic reach on social media platforms undermines equality of opportunity for political influence (EOPI), which is a central democratic ideal. Drawing on Niko Kolodny's work, I contend that current recommendation algorithms create and perpetuate informal inequalities by concentrating attention among a small minority of already-amplified speakers while systematically marginalizing others. To address this problem, I propose recommendation floors as a mechanism for equalizing political speech. To help users achieve meaningful participation, each verified account would receive guaranteed minimum recommendation for up to a limited number of political posts per week. Although this measure represents one component of the structural reforms needed to move the digital public sphere closer to democratic ideals, it offers a feasible pathway to reducing informal inequalities in political influence online.

cs.CY↗

arXiv2026

Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework

In online advertising, marketing interventions such as coupons introduce significant confounding bias into Click-Through Rate (CTR) prediction. Observed clicks reflect a mixture of users' intrinsic preferences and the uplift induced by these interventions. This causes conventional models to miscalibrate base CTRs, which distorts downstream ranking and billing decisions. Furthermore, marketing interventions often operate as multi-valued treatments with varying magnitudes, introducing additional complexity to CTR prediction. To address these issues, we propose the Unified Multi-Valued Treatment Network (UniMVT). Specifically, UniMVT disentangles confounding factors from treatment-sensitive representations, enabling a full-space counterfactual inference module to jointly reconstruct the debiased base CTR and intensity-response curves. To handle the complexity of multi-valued treatments, UniMVT employs an auxiliary intensity estimation task to capture treatment propensities and devise a unit uplift objective that normalizes the intervention effect. This ensures comparable estimation across the continuous coupon-value spectrum. UniMVT simultaneously achieves debiased CTR prediction for accurate system calibration and precise uplift estimation for incentive allocation. Extensive experiments on synthetic and industrial datasets demonstrate UniMVT's superiority in both predictive accuracy and calibration. Furthermore, real-world A/B tests confirm that UniMVT significantly improves business metrics through more effective coupon distribution.

cs.SI↗

arXiv2026

Emergence of polarization in networks of large language model agents

Rapid advances in large language models (LLMs) have not only empowered autonomous agents to generate social networks, communicate, and form shared and diverging opinions on political issues, but have also begun to play a growing role in shaping human political deliberation. Our understanding of their collective behaviours and underlying mechanisms remains incomplete, however, posing unexpected risks to human society. In this paper, we simulate networked systems involving thousands of LLM agents across different backbone models (GPT-3.5, GPT-4o, ChatGLM, Llama-3, and DeepSeek-V3), in which agents interact through LLM-guided conversations and update their opinions over time, resulting in the emergence of opinion polarization. We discover that these agents spontaneously develop their own social network with properties characteristic of human social networks, including homophilic clustering. The collective opinions of these LLM agents evolve in ways that exhibit behavioural patterns consistent with social phenomena and mechanisms widely discussed in studies of human behaviour. Overall, these behaviours, patterns, and emergent phenomena produced by LLM agents are consistent with real-world observations and established opinion-dynamics models. This consistency suggests that LLM agents can serve as a valuable synthetic testbed for exploring hypothetical intervention strategies in networked LLM-agent systems.

cs.SI↗

arXiv2026

Co-evolution of the global research collaboration network and the performance of nations in science and technology

Researchers have long suspected that international research collaboration (IRC) and scientific and technological (S&T) performance are subject to reciprocal causality, yet the endogenous co-evolution of these twin phenomena has yet to be tested by large-scale empirical analysis. This study tests these effects simultaneously using a longitudinal co-evolution model on three decades of global network and national performance data. Stochastic actor-oriented models (SAOM) are used to analyze data on 172 countries from 1993 to 2022. Yearly IRC networks are constructed from Web of Science's XML database, and performance data are gathered from Elsevier's fractional field-weighted citation impact (FWCI). The models also account for geographic, economic, demographic, and political factors, as well as endogenous network processes. The results provide support for co-evolution. Distance and shared language moderate this relationship in contrasting ways. The selection effect of performance on tie formation is amplified across distance and dampened by a shared language, whereas the influence effect of performance on centrality is attenuated by remoteness and strengthened by linguistic reach. This pattern suggests that information asymmetry shapes partner selection, while communication and coordination shape the returns to collaboration, pointing to a signaling role for citation-based performance metrics in collaborator selection.

cs.SI↗

arXiv2026

Less Is Moral: A CHARMing Framework for Moral Foundations Detection in Endorsement Behaviour

Moral language plays a central role in shaping online endorsement and the diffusion of information, yet existing moral foundation detection systems often suffer from poor cross-domain generalization, weak rationale grounding, and reliance on costly prompting-based large language models (LLMs). We introduce CHARM, a MAC- and Hate-speech-Aware Rationalealigned Moral foundation detection framework built on a lightweight fine-tuned LLM, which integrates complementary moral grounding, rationale alignment, and polarity-aware hate speech signals to support more robust and faithful moral prediction. Unlike prior dictionary-, fine-tune-, or prompt-based detectors, which decouple computation from psychological theory, CHARM is built so that each component -- MAC cross-attention, rationale alignment, and hate-speech modulation -- operationalizes a distinct psychological construct. Using a 30\% subsample of the MFTC, MFRC, and News training pools together with the richer supervision in MFTCXplain, CHARM improves AUC by up to 15.3\% in-domain, surpasses the supervised baselines on every out-of-domain dataset in both AUC and F1, and offers a scalable, low-cost alternative to prompting-based LLM detectors. We further apply CHARM to large-scale COVID-19 discourse on Twitter and show that moral value alignment is strongly associated with online endorsement behavior. By making moral framing measurable at scale, CHARM offers a practical tool for studying the spread of morally charged misinformation. Code and additional materials: https://github.com/HuixiangF/CHARM/.

cs.CL↗

arXiv2026

How Well Do LLMs Simulate Survey Responses Following a Breast Cancer Screening Intervention?

Collecting survey data is laborious and limited by privacy constraints. Large language models (LLMs) have shown promise as predictive social simulations. It is unclear whether they can replicate population-level response distributions before and after a healthcare intervention. Using information derived from 4125 women aged 35-59 years, we evaluate whether agents informed solely by pre-intervention profile information can reproduce post-intervention response distributions. Groups of LLM agents (n=50) were created with Gemma 4 E4B and Qwen3.5 9B; conditions ranged from zero-shot prompting to agent profiles enriched with aggregate or individual-level demographic characteristics and pre-intervention questionnaire responses. We compared predicted and observed response distributions with Total Variation Distance (TVD) and Normalized Wasserstein Distance (NWD). Across both LLMs, profile-based agents improved distributional accuracy relative to zero-shot and random baselines. Nevertheless, direct sampling of 50 real participants remained more accurate. Prediction errors were also higher among participants aged 55-59 years and those living in private property. Errors also varied by question theme and LLM model, with the highest errors observed for cancer fatalism and post intervention attitudes toward genetics. Sensitivity analyses showed that performance was influenced by prompt template changes and temperature hyperparameter. Our results show the potential of LLM-based agents to model behavioral responses to interventions in silico. However, profiles containing additional information beyond demographics did not consistently outperform simpler ones. Certain cultural constructs and population groups also remain inadequately represented by the LLM models evaluated. Future work may include building behaviorally grounded and locally validated virtual populations.

cs.SI↗

arXiv2026

Ex Ante Estimation of Payable Relief and Compensation Timing for Supplier Selection

Supplier selection affects not only operating performance but also the payable network entered by a new obligation. This paper develops the Compensability Capacity Assessment (CCA), an ex ante buyer-supplier measure of expected gross payable relief and its likely timing. CPM provides the bounded structural kernel; concave CCA variants add bilateral invoice capacity. The measure is tested on 749,952 analytical invoices issued during 2012-2023, using frozen nine-month histories and future weekly, monthly, quarterly, semester, and annual windows. Cycle-restricted and path-enabled clearing are independent outcome-generating environments used to validate the measure, not technologies compared by this study. Across seven fully observed quarters in 2022-2023, log-CCA has a median Spearman correlation of 0.638 with future integrated relief; persistent relations carry 93.5% of relief, and the highest-scoring relation captures 92.5% of buyer-specific best relief. Predictability remains positive from week to year, with quarterly recalibration providing the best operating balance between signal, coverage, and timeliness. A timing analysis shows that, across the two validation environments, 89-91% of attributed relief occurs within seven days of invoice issue and 94-96% within thirty days, on average about sixteen days before contractual maturity. Log-CCA correlates 0.564 with thirty-day relief and 0.566 with relief-days. Its highest quartile has a 96.5% median probability of thirty-day compensation, compared with 57.6% in the lowest quartile. Conditional waiting-time prediction is weaker, so CCA should rank timely compensation opportunity rather than forecast an exact payment date. The findings link supplier placement, financial circularity, and working-capital exposure while motivating deployment, causal testing, and quarterly drift monitoring.

cs.SI↗