Papers

Studies of careers in science, film, music, and books report a common pattern. When a person's most successful work arrives is close to a random draw over the works they produce. How large their successes tend to be, in contrast, follows a stable, person-specific factor. We test whether this pattern holds for open-source software careers and whether it changed when generative AI coding tools arrived. From the complete public record of GitHub push events (2015-2025), we reconstruct 102.2M career works by 6.15M contributors, and for the 908k contributors whose repositories publish packages, we measure each work's impact by how many downstream packages come to depend on it. First, we find that the timing of a career's biggest hit is close to a lottery over their works, as in science and the arts, with a small, replicable lean toward early career that grows as careers get longer. Second, some coders reliably produce higher-impact work than others, but this lasting personal factor accounts for only part of why impact persists (about a fifth in our primary specification); the rest behaves like momentum, success feeding on itself for a period of time. Third, within the same contributors, this structure did not change after ChatGPT's release. The stable factor's weight grew by about as much as it grew for an earlier cohort that simply aged, and subtracting the effect of aging from the effect of generative AI puts the shift at +0.03 (95% CI [-0.22, +0.23]), indistinguishable from zero. The success pattern documented in science and the arts therefore describes open-source careers too, and it shows no detectable break across the arrival of generative AI. These results have implications for how track records on open platforms should be read and on what to expect from generative AI for the careers built on them.

We reassess a published fusion method under a fair protocol. Local similarity indices are cheap but score every node pair without common neighbors zero, and weighting them by a node centrality product, as proposed for PageRank, is not robust once the protocol is made comparable across indices; on our eight-network panel the reported improvement does not survive that protocol for six of the seven local indices. We study these issues in a unified piecewise framework that modulates the local score multiplicatively by a centrality product when local information suffices and uses a small-dose centrality product as a completion term when it does not. A scale analysis and a reassessment on eight real-world networks (five-fold cross-validation, exhaustive negative samples) trace this to the O(n^-2) scale of the PageRank product, whose modulation factor is then a near-identity map, and to its rank correlation with degree exceeding 0.89; control arms show scale calibration is necessary but not sufficient. The min-max normalized DomiRank product instead has an O(1) range and substantial non-degree content, and a grid search yields unified parameters (omega=5, c=0.1) under which DR-JC/HPI/SO significantly outperform their local baselines already at the dataset level (p=0.027 and 0.035), the remaining four at the fold level only; DR-RA attains an average AUC of 0.9257, above nine of ten comparison methods, whereas degree-only preferential attachment reaches only 0.7736. Mechanism decomposition shows the two mechanisms contribute differently across index families and should be dosed separately, yielding a safety boundary 0 < c <= 0.3, a plateau omega in [0.5, 12] and a near-critical plateau sigma in [0.7, 0.95]. Two negative results bound the claim: under a degree-corrected benchmark the gains over local baselines vanish and reverse in sign, and the AUC gains do not translate into top-L hit rate.

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.

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.

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.

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.

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.

Large language models (LLMs) are rapidly emerging as a new paradigm for modeling social networks by representing users and their relationships and interactions through natural language. Unlike classical network models or deep learning approaches, LLMs can simulate context-aware social behavior and language-driven interactions, enabling more realistic modeling of network formation and dynamic social processes. However, existing studies are scattered across different research communities and lack a unified perspective. This survey presents the first comprehensive review of LLMs for social network modeling by organizing the literature into two broad categories: network generative models and dynamic process models. Network generative models are further classified into selection-based and interaction-based approaches, while dynamic process models are categorized into opinion dynamics, information diffusion, and rumor propagation, each with their underlying modeling mechanisms. LLMs enable rich textual social interactions and decision-making, but they also exhibit many limitations, including inherent social biases and prompt sensitivity. We outline these open research challenges and discuss future directions in LLM-based social network modeling.

Otome games, a romance simulation genre primarily targeting female, have emerged as a major force in the global gaming market, attracting hundreds of millions of players and billions in revenue. Despite their popularity, otome game communities face pervasive online toxicity, which has been largely unexplored. In this work, we present the first large-scale measurement of toxicity in otome game communities across social platforms. We introduce OtomeSCAN, a framework for collecting, evaluating, and analyzing 620,045 posts from Weibo and Reddit spanning 18 months. To support robust analysis, we manually annotated a ground-truth dataset of 4,308 posts, identifying eight target groups such as players and game developers. We evaluate seven toxicity detectors on the dataset, including general-purpose models and our proposed LLM-based detectors, with our best model achieving F1-scores of 0.82 (Weibo) and 0.78 (Reddit). Our analysis reveals significant platform-based differences in toxicity: 22.20% of otome-related posts on Weibo are toxic, compared to 3.71% on Reddit. Besides, real-world events like in-community conflicts can rapidly escalate toxicity, with toxicity ratios increasing to 37.09% in just 72 hours during an external attack on Weibo. We also flag 191 potential-coordination clusters in otome game communities, 64.40% of which target game developers, with several accounts participating repeatedly across multiple clusters. We hope our work inspires further research on community-specific toxicity and contributes to building healthier online spaces for marginalized gaming communities.

As geopolitical tensions, energy crises, and energy-intensive AI infrastructure intensify concerns about demand, affordability, and resilience, communities increasingly encounter these challenges through everyday energy practices, particularly winter heating. Against this background, the doctoral exposé, "Designing Human-HVAC Interaction for Healthy, Affordable, and Sustainable Heating in Smart Homes", is structured around four chapters. First, a multidisciplinary literature review defines and positions Human-HVAC Interaction, focusing on heating in smart homes. Second, longitudinal living lab studies with design probes examine everyday heating practices, thermal comfort, and indoor environmental quality, with attention to thermally vulnerable groups such as older adults, pregnant or menopausal women, parents with infants, and people affected by allergies or airborne pollutants. Third, a VR-based smart home demonstrator explores how heating and IEQ scenarios can be prototyped and evaluated as a virtual living lab, while critically examining the limits of representing bodily indoor climate conditions through VR. Fourth, follow-up design studies examine how VR-based insights can be translated into physical-digital prototypes that combine digital fabrication, distributed environmental sensing, and diverse interface forms for critical heating and IEQ contexts. The thesis aims to contribute a design-oriented understanding of Human-HVAC Interaction by building from a multidisciplinary literature review to empirical living lab and co-design studies, VR-based prototyping, and physical system development, examining how smart home users make sense of, negotiate, and respond to smart HVAC system.

Higher-order structures offer a natural representation of complex systems that involve interactions between groups of different sizes. A widespread feature of their higher-order structure is nestedness, whereby interactions involving smaller groups are contained within larger ones. Yet, why interactions of different orders organise into nested structures remains largely unexplained. Here, we introduce an analytically tractable geometric model of higher-order networks in which a single latent geometric space couples interactions across orders, leading to the spontaneous emergence of nestedness. We show analytically and numerically that nestedness undergoes a transition between a nested geometric regime, where it remains finite in the thermodynamic limit, and a regime where it vanishes with system size. In this regime, we uncover a weakly geometric range characterized by an anomalously slow finite-size decay, allowing substantial nestedness to persist in finite systems even when its asymptotic value vanishes. Finally, with a single geometric coupling parameter, the model reproduces the nestedness profiles observed in real-world hypergraphs across different domains. Our results reveal latent geometry as a simple organising principle underlying the nested organisation of higher-order interactions.

The global structure of P2P networks underlying modern cryptocurrencies is hidden by design: each node only knows its neighbors and maintains a local peer table of IP addresses. The peer table of a node can be seen as the node's “local view” of the set of nodes currently in the network: it is constantly updated with information collected from the neighbors and it is used by the node to establish new connections when needed. The maintenance rules of the nodes' peer tables determine the global structure of the network and its evolution. Even though the global structure is unknown to any node of the network as well as to any external observer, the rules for the exchange of information between neighbors are compatible with the design and use of network crawlers that can query the nodes and extract some information about their peer tables. In this paper we first analyze the data we crawled from nodes of the Bitcoin and Dogecoin P2P networks, to collect information about the distribution of IP addresses in the peer tables and to estimate the evolution of network size and churn rate; we then use the estimates to set up the parameters for a simulation of the Bitcoin network and we analyze the evolution of the network structure that we get from the simulation. Overall, our results show that Bitcoin's peer-table maintenance rules induce non-uniform, heavy-tailed visibility patterns while nevertheless generating a well-connected and structurally robust network.

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.

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.

We develop an algorithmic framework for studying the evolution of communities in social networks. We begin with the theoretical foundation, from which we conclude that an evolution is at most as strong as its weakest link. This allows us to formulate an efficient algorithm to identify all evolutionary sequences in a dynamic social network. We use this algorithm to empirically study community evolution in several large social networks, to identify those features of the early stages of a community that indicate whether a community is going to be shortlived or not. Our results show that it is possible to correlate the lifespan of a community to structural parameters of its early evolution; these conclusions are robust across all the social networks we have investigated.

In a mechanistic model of the dawn chorus, Kaye showed that heterogeneous activation thresholds and a shared feedback signal determined by the population's active fraction can produce abrupt collective activation and hysteresis. We extend this mechanism to a network of interacting agents. Each node has a continuous activation level, and a nonnegative row-stochastic matrix determines how node activities contribute to individual feedback. We prove that sufficiently weak feedback yields a unique globally attracting equilibrium. For all feedback strengths, the homogeneous dynamics exactly reproduce Kaye's scalar equation; consequently, network topology does not alter the folds or cusp of the homogeneous branch, and no heterogeneous mode becomes unstable before the homogeneous mode. For equitable partitions, the network admits an exact quotient system in which nodes within a block receive the same aggregate input from every block. When blocks are uncoupled, the quotient reduces to independent copies of Kaye's scalar equation. We show that every assignment of stable scalar equilibria to blocks persists under sufficiently weak interblock coupling, producing a combinatorial family of stable quotient equilibria that lift to stable full-network equilibria and remain under small perturbations that break exact equitability. For two symmetrically coupled blocks, branches with unequal block activities terminate at a pair of symmetry-related cusp bifurcations. Near the onset of bistability, we derive scaling laws for the interblock coupling at which these bifurcations occur, the activity difference between the blocks at bifurcation, and the corresponding shift of the external stimulus from the scalar cusp. Numerical continuation confirms the scaling laws for gamma, logistic, and normal threshold distributions.

Prediction-market probabilities increasingly appear in news coverage, yet little is known about which market movements become news or how much trading money sits behind the numbers journalists quote. Unlike a poll, a market price can be moved by anyone willing to trade, so the cost of manufacturing a number that circulates as news bears directly on the information environment. We link 173.7 million signed Polymarket trades to news coverage from 2024-2025. From 6,990 articles mentioning prediction-market venues, an LLM-based, human-validated matcher extracts 1,582 sentences quoting market odds and attributes 918 to the specific market whose price they cite. We then detect 44,976 price dislocations, movements of at least five percentage points backed by concentrated one-sided trading, and ask whether a market is cited more often afterward. In the days after a dislocation, a market's citation rate is about 33% higher than its matched baseline (log citation-rate ratio $\tau_{\mathrm{cite}}=0.283$, permutation $p=0.001$), robust to binary and Poisson count outcomes. Yet move size is not the strongest predictor of citation: prominence dominates (standardized $\beta=0.610$ vs. $\beta=0.159$ for move size). Finally, we combine the dollar flow behind a given price change with observed citation rates into a metric we call epistemic leverage, the dollars needed to move a market five points and have the move cited. It stays near \$0.7-1.0 million across prominence quintiles, because cheaper-to-move markets are proportionally less likely to be cited. The implied threat model centers not on the long tail of cheaply moved markets but on the few prominent markets newsrooms treat as informational infrastructure, where a seven-figure price of influence sits within the budgets of actors with a large stake in the quoted number. We release aggregate event-study data and validation materials.

Link sign prediction (LSP) aims to infer the positive or negative polarity of unobserved links in signed networks. Signed Graph Neural Networks (SGNNs) usually rely on signed-graph structural priors, including structural balance and homophily-like similarity, to guide message passing and prediction. These priors describe population-level tendencies, not guarantees for individual target edges. Their failures are especially costly under severe sign imbalance, where errors on minority and locally conflicting relations are harder to detect and correct. We propose Polarity-Asymmetric Structural Calibration (PASC), a target-edge structural-prior calibration framework for signed link prediction. PASC constructs a structure-only prior representation, estimates a target-edge structural prior score, and compares this score with a local signed-context cue to derive a conflict residual. The residual calibrates signed attention aggregation, target-edge gated fusion, and regime-adaptive optimization. Experiments on five real-world signed network datasets show that PASC consistently achieves the best Macro-F1 among representative baselines, with competitive AUC, Binary-F1, and Micro-F1. Structural-shift experiments further suggest reduced dependence on dense-neighborhood and local-closure shortcuts. Source code is available at https://github.com/iqqGGGGGGG/PASC-for-LSP.

We consider a fully connected gossip network of $n$ nodes that track a binary continuous-time Markov source through a strategic sender transmitting updates under a communication budget at a rate that depends on the source state. The receivers exchange packets through gossip and decide whether to follow the sender. We model this interaction as a Stackelberg game and analyze it through a stochastic hybrid systems (SHS) framework. We prove that the sender's budget constraint binds at every interior optimum, reducing its problem to a one-dimensional search on the budget line. When the sender pushes its preferred state at the higher rate, the receivers gossip at the highest available rate. Gossip has no direction of its own and works against the asymmetry in the sender's policy rather than reinforcing it. We prove that an optimistic Stackelberg equilibrium exists, and that it is unique and explicitly characterized whenever a policy on the strategic half of that line is feasible at the gossip cap. Monte Carlo simulations agree with the analytical recursion.

Platform moderation can disrupt online communities, but the responses that follow do not necessarily take the form of direct migration. Communities may relocate, reorganize, discuss moderation decisions, express censorship concerns, or continue activity on alternative platforms without clear evidence of user-level movement. This study examines Reddit community bans and quarantines and their relationship with activity and migration-related discourse on Voat. Using data from the Multi-Platform Aggregated Dataset of Online Communities (MADOC), we analyze interventions affecting FatPeopleHate, GreatAwakening, MillionDollarExtreme, and CringeAnarchy. We combine computational discourse screening with a stratified independent human-validation procedure and population-adjusted estimates across 90-day pre- and post-intervention windows. The results reveal heterogeneous cross-platform responses. FatPeopleHate experienced a substantial increase in overall Voat activity while the relative prevalence of policy-response and broader relevant discourse declined. CringeAnarchy showed declines in direct migration and policy-response discourse following its permanent ban, although some estimates were sensitive to uncertain human labels. GreatAwakening instead exhibited a modest increase in policy-response discourse without clear evidence of increased direct migration. These findings suggest that platform interventions do not produce a uniform migration process. Overall destination-platform activity and migration-related discourse may move in different directions, highlighting the importance of distinguishing behavioral migration from broader discussion of moderation, censorship, governance, and community rebuilding.

Safety evaluations typically focus on individual agents, but interacting agents can spread harmful information and influence the environment in which later decisions are made. We study how collective failure changes with harmful-agent fraction and society size in a controlled financial agent society, where agents communicate over a social network and trade in a shared market. In the primary financial scenario, collective failure requires broad harmful diffusion together with severe price dislocation or liquidity stress. Across all tested society sizes, failure remains rare at low harmful fractions but rises sharply over a narrow range. As society size grows from N=100 to N=2000, the harmful fraction associated with a 50% failure probability decreases from 4.7% to 2.2%, while the corresponding number of harmful agents increases from approximately 5 to 44. In contrast, when the number of harmful agents is held fixed, their impact becomes weaker as the society grows. Controlled interventions further show that broader network reach shifts the collapse boundary toward lower harmful fractions, whereas stronger conformity alone has little effect. To characterize these effects, we introduce Agent Society Dynamics, a finite-size framework for relating harmful-agent fraction, society size, and interaction structure to collective failure. Overall, our results reveal a nonlinear, size-dependent collapse transition in financial agent societies, showing that collective failure depends not only on the prevalence of harmful agents but also on the size and interaction structure of the surrounding society. Code is available at https://github.com/SAIL-Research-Lab/WolfSociety.

Blocking is a widely used tool that helps people manage unwanted interactions on social platforms. We study the problem of predicting block events on Bluesky: whether a given user will block a particular account, given recent interaction, network, activity, and content signals. Using more than three million block events and over 260 million user interactions, we examine several formulations of the directed block prediction problem, differing in which possible targets are considered and how negative examples are selected. We observe that only about 5% of user blocks are preceded by a recent direct interaction. Expanding the set of possible targets to include accounts connected through a common neighbor raises the share to about 22%, once high-degree accounts are excluded as intermediaries. Conditional on candidate inclusion, Hits@1 ranges from 61.3% to 72.4%, compared with a random baseline of 16.7%. However, predictive performance and the signals used by the models depend strongly on how candidates and comparison examples are constructed. Although our goal is to empirically analyze user behavior rather than to propose a deployable system, these findings are relevant to user-facing moderation tools that might help users identify accounts they may wish to avoid or block. In such tools, candidate generation would be a substantive design and evaluation choice rather than merely a preprocessing step.

Graphs are a standard representation for data in the social sciences, cybersecurity, computer infrastructure, bioinformatics, and more. Typical real-world graphs are sparse, meaning the average degree is small (in the tens, while the number of vertices is more than millions). When graph data is collected from a source, a major task is to perform data exploration. Thus, any region of “density" is of interest, since it indicates special structure. An important goal is to cover a significant portion of the graph using dense subgraphs. Existing algorithms that find many dense subgraphs do not have overlapping output, and hence provide limited coverage. Other methods that produce overlapping clusters do not generate dense subgraphs. The main goal of this paper is to develop provable and practical methods that can provide overlapping dense subgraphs that can cover large portions of real-world networks. Our contribution is an algorithm CohortRecovery that achieves this goal. We first develop a mathematical framework of triadic cohorts that captures the notion of “detectable dense subgraphs” that potentially overlap. We prove that CohortRecovery can output a set of dense subgraphs, such that each triadic cohort is almost completely contained in some dense subgraph. We give a practical implementation of CohortRecovery and demonstrate it on a variety of datasets. It typically runs in under ten minutes on a commodity machine even on graphs with tens of millions of edges. For numerous datasets, CohortRecovery is able to cover more than 25% of the vertices in non-trivial subgraphs of density more than 0.8, and is significantly better than a wide variety of scalable graph clustering/community detection algorithms. Moreover, we demonstrate that output of CohortRecovery captures ground truth clusters obtained by manual curation.

Pass receiver selection is a fundamental task in football analytics, aiming to predict the intended receiver under a given game state. This task is challenging with event-centered freeze-frame observations, a broadcast-like setting that provides only partial and variable player visibility without complete trajectories or stable player identities. The model must therefore reason over anonymous visible candidates, opponent pressure, and recent context under partial observation. To address this setting, we propose a Hierarchical Possession-aware Graph Pointer Network (HPGPN), which formulates pass receiver selection as variable-size candidate prediction over visible teammates. HPGPN jointly models current player interactions, local event context, and possession-level temporal dynamics. It represents the current pass situation with a graph, incorporates fixed event context, and uses dynamic possession history to capture how the attacking sequence evolves. Candidate representations are refined hierarchically by integrating spatial, contextual, and historical evidence, and a glimpse pointer head scores the receiver candidates. Experiments on public football event and freeze-frame data show that HPGPN improves pass receiver selection performance. Ablation studies demonstrate the effectiveness of graph-based interaction modeling, fixed event context, and dual-branch dynamic possession-history modeling.

Graph representation learning (GRL) serves as a canonical paradigm for modeling complex networks. However, real-world AI systems inherently manifest as evolving heterogeneous entities with complex interactions, posing significant challenges to static or homogeneous modeling. To address these complexities, representation learning for Dynamic Heterogeneous Graphs (DHGs) has emerged as a vital approach for learning low-dimensional representations that simultaneously preserve structural semantics and temporal dynamics. This survey presents the first systematic review of DHG representation learning methods. We first introduce a unified formal definition that encompasses both discrete-time and continuous-time DHGs from the perspective of temporal granularity. Building upon this formulation, we propose a novel algorithm-centric taxonomy that categorizes existing literature, including early embedding-based approaches, graph neural network (GNN)-based models, and relatively recent Transformer-based DHG methods, while explicitly highlighting their intrinsic modeling biases with respect to dynamic granularity. Furthermore, we summarize representative applications of DHG representation learning, along with commonly used datasets and benchmarks. Finally, we discuss promising research directions that guide future advances in this rapidly evolving field.

Multimodal Graph Neural Networks have become standard for recommendation by augmenting sparse interaction data with content features. Yet current architectures face two bottlenecks: structural rigidity, from a reliance on static precomputed similarity graphs that cannot adapt to evolving preferences; and semantic fragility, where noisy modality signals are indiscriminately fused, distorting the collaborative signal. We propose MURAL (Multimodal Uncertainty-aware Recommendation via Adaptive edge Learning), a unified framework that shifts multimodal recommendation from fixed structural augmentation to dynamic topology discovery. To address structural rigidity, an Adaptive Edge Learner combines a differentiable retrieval-augmented strategy with an approximate nearest neighbor search to discover latent item-item correlations that are both semantically adaptive and computationally scalable (O(NlogN)). To address semantic fragility, an Uncertainty-Aware Fusion module models the aleatoric uncertainty of heterogeneous modalities, dynamically down-weighting unreliable features while prioritizing high-confidence signals as a defense against cross-modal noise. We further employ a contrastive teacher-student alignment that anchors modality-specific representations to stable behavioral signals, ensuring optimization stability without gradient leakage. Experiments on large-scale benchmarks including TikTok and Amazon show that MURAL significantly surpasses both structural and generative state-of-the-art baselines, achieving superior accuracy while offering interpretability through domain-specific modality dominance and robustness under extreme data corruption.

Centrality measures are widely used to rank nodes in networked data, but fairness interventions for graph centrality typically target global score mass or modify the centrality operator rather than controlling who appears in the displayed top-k ranking. We study this top-k setting for Katz centrality. Given a target group proportion, an admissible set of directed edge additions, and a fairness tolerance, the goal is to find the smallest edit set whose resulting Katz top-k ranking satisfies the target representation constraint. We formalize this problem as Fair Top-k Katz Centrality Design and show that the minimum-edit objective is strongly inapproximable, ruling out worst-case polynomial-time approximation guarantees unless P = NP. We then derive closed-form Katz sensitivity expressions showing that useful edits are boundary-driven: they must help promotable nodes outside the top-k set overtake opposing nodes inside it. Based on this structure, we develop BLADE, a scalable boundary-link algorithm that avoids dense Katz-kernel maintenance by using score-based direct-target batches and warm-started Katz updates. Experiments on synthetic and real-world networks show that BLADE reaches the desired top-k representation using far fewer edits than natural baselines, while scaling to large real-world graphs and preserving the original ranking structure.

Graphs are a fundamental data structure underlying many problems in the natural and social sciences. Over the past decade, Graph Neural Networks (GNNs) have dominated graph machine learning, supported by solid theoretical foundations. Yet scientists often understand graph structure through vision: chemists read molecular diagrams and social scientists inspect network visualizations. Despite decades of work on graph visualization, most graph learning pipelines still treat graphs purely as symbolic structures, rarely leveraging the visual form of graphs. We argue that this gap deserves renewed attention in the era of powerful vision and vision-language models. This survey provides a first systematic overview of the emerging area we term vision meets graphs, which treats visual depictions of graphs as first-class inputs for reasoning and learning. We organize existing work into three threads. Vision for Graph Reasoning studies how models can use visual depictions of graphs to understand structure and carry out multi-step reasoning. Vision for Graph Learning explores how visual features can complement or augment graph encoders beyond known limitations of message passing. Scientific Graphs examines domains where standardized depiction conventions support both reasoning and learning. Our goal is to clarify what current methods can and cannot do, and to outline a path toward foundation models that perceive and reason about graphs as scientists do.

A new type of decision networks is suggested and its operation is analyzed. The network nodes are represented by intelligent agents who can denote either some biological beings, like humans, or neurons of the brain, or the nodes of artificial intelligence. The specifics of the network are in the following: It is probabilistic in the sense that the choice, accomplished by each agent, is characterized by the related probability. It is dynamic, with the probabilities varying in time due to the exchange of information between the agents. It is affective, because the agents choose between alternatives by taking account of utility as well as of biases and emotions. In general, it is heterogeneous, being composed of the groups of agents with different properties, for instance having long-term memory and short-term memory. The network dynamics, caused by the information exchange, results in decision error decrease. The network operation is illustrated by the example starting with the Allais paradox, its resolution, and the decision error diminution in the process of decision dynamics with information exchange. Resorting to machine-learning techniques it is possible to regulate the behavior of the network agents forcing them to choose particular alternatives.

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/.