Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems
Alexander Loth, Martin Kappes, Marc-Oliver Pahl
AI 速览：该文从2024年综述转向实践对策，介绍JudgeGPT与RogueGPT构成的实验管线，用于研究人类对AI生成新闻的感知与检测。
Generative AI and misinformation research have evolved since our 2024 survey. This paper presents an updated perspective, transitioning from literature review to practical countermeasures. We report on changes in the threat landscape, including improved AI-generated content through Large Language Models (LLMs) and multimodal systems - addressing the adverse impacts of AI on information quality. This work focuses on our practical contributions: JudgeGPT, a platform to evaluate human perception of AI-generated news, and RogueGPT, a controlled stimulus generation engine for research. Together, these tools form an experimental pipeline for studying how humans perceive and detect AI-generated misinformation. Our findings show that detection capabilities have improved, but competition between generation and detection continues. We discuss mitigation strategies such as LLM-based detection, inoculation approaches, and the dual-use nature of generative AI.
Crowds Believe in You: Online Prediction Markets Overestimate How Often People Achieve Their Own Goals
Zihan Chen, Lei Nico Zheng, Di Zhu
AI 速览：基于 Manifold 上 6,150 个自我目标预测市场的订单重建，作者报告自我市场比外部事件市场更常被高估，且纠偏信息主要掌握在当事人手中。
People increasingly share personal goals online and invite others to forecast them. Research on the planning fallacy finds that observers predict a person's behavior less optimistically than the person does, and prediction markets aggregate such judgments, harnessing the wisdom of crowds. We study 6,150 prediction markets on Manifold in which 1,660 users asked about their own futures ("Will I ...?"), reconstruct every order and execution, and compare these self-markets with markets about external events. Self-markets resolved YES 4.4 percentage points less often than their final prices implied, whereas external markets were nearly calibrated (0.9 points); the difference was 2.2 points at the same price level and 1.9 points within creators who made both kinds of market. Absent when markets opened, the overstatement emerged within a day of trading, even where the person never traded, and peaked in the week before close. In a market's first day, observers moved external-market prices down toward the base rate, but moved self-market prices 3.6 points further toward YES, except in markets naming an outcome the person would not want, which were not overpriced. The correction came late and largely from the person, whose orders supplied 38% of the eventual movement toward the outcome in self-markets, against 9% in external markets. Because self-market prices mainly failed to separate successes from failures, a recalibration learned from earlier markets improved later forecasts by only 1.5%. Crowds believe in the futures people pose for themselves, and the information that would correct them lies with the person.
Reversing Polarization through Tolerance-Aware Recommendation and Adaptive Diversification
Zhanzhan Zhao, Shuqi Zhao, Zichen Fan, Dana Randall
AI 速览：该研究基于意见动力学模型与LLM智能体模拟，探讨容忍度感知推荐结合自适应多样化能否在已极化人群中逆转而非仅减缓极化。
Recommender systems face a tension between broadening exposure to diverse viewpoints and avoiding interactions that reinforce extreme positions. This tension is especially consequential in already polarized populations. Here we investigate whether recommendation strategies can reverse polarization rather than merely slow its development. Building on the Attraction--Repulsion Model of opinion dynamics, we introduce two complementary design principles: Tolerance-aware recommendation, which seeks to learn each individual's tolerance for disagreement and reduces exposure to viewpoints beyond that tolerance, and adaptive diversification, which broadens exposure for agents with more extreme opinions while retaining stronger personalization for moderates. Agent-based experiments show that highly personalized recommendations intensify polarization under polarized initial conditions. Tolerance awareness alone primarily slows polarization, whereas combining it with adaptive diversification can reduce polarization below its initial level and, under suitable conditions, restore consensus. Additional experiments incorporating persistent extreme influencers and a spiral-of-silence mechanism show that the combined strategy can mitigate polarization under social pressure and selective participation. To examine whether these findings extend beyond predefined opinion-update rules, we repeat the experiments on a simulated social platform populated by 100 LLM-driven agents prompted to move toward opinions similar to their own and away from those that differ substantially. Across three runs, the combined strategy is again the only recommendation policy tested that reverses polarization in every run. These findings distinguish polarization prevention from reversal and identify tolerance-aware, adaptive exposure as a promising recommendation design principle for reconnecting divided populations.
The growth of Generative Artificial Intelligence (GenAI) has shifted disinformation production from manual fabrication to automated, large-scale manipulation. This article presents findings from the first wave of a longitudinal expert perception survey (N=21) involving AI researchers, policymakers, and disinformation specialists. It examines the perceived severity of multimodal threats -- text, image, audio, and video -- and evaluates current mitigation strategies. Results indicate that while deepfake video presents immediate "shock" value, large-scale text generation poses a systemic risk of "epistemic fragmentation" and "synthetic consensus," particularly in the political domain. The survey reveals skepticism about technical detection tools, with experts favoring provenance standards and regulatory frameworks despite implementation barriers. GenAI disinformation research requires reproducible methods. The current challenge is measurement: without standardized benchmarks and reproducibility checklists, tracking or countering synthetic media remains difficult. We propose treating information integrity as an infrastructure with rigor in data provenance and methodological reproducibility.
As Large Language Model (LLM) agents increasingly participate in persistent social environments, understanding their long-term social interaction capabilities becomes increasingly important. While existing research has advanced long-term memory and conversational context retrieval, less attention has been paid to whether agents can autonomously re-engage with previously inactive conversations. In particular, remembering a past interaction does not necessarily mean that an agent can identify when to revisit it, decide to return, or reconstruct the context needed for a coherent continuation. To investigate this gap, we introduce agent social re-entry as a multi- dimensional evaluation problem, distinguishing three complementary capabilities: Finding, Returning, and Reconstructing. We develop a shared-snapshot evaluation framework that measures these dimensions independently, allowing us to examine how conversational resurfacing and persistent memory relate to different aspects of social re-entry. Evaluations across six LLM configurations show that resurfacing substantially improves target conversation identification, while persistent memory is associated with higher rates of autonomous target-directed replying. However, neither improvement is accompanied by consistent gains in contextual reconstruction. These results highlight the distinction between accessing previous conversations, choosing to re-engage, and maintaining conversational continuity. Our framework provides a way to evaluate these capabilities separately rather than relying on memory retrieval or conversational response quality alone.
Population displacement is a housing-related involuntary residential dislocation. It has become increasingly widespread in many cities, particularly in neighbourhoods undergoing rapid economic and demographic change, and measuring it is essential to assess the social consequences of urban transformation and housing market pressures. Despite its relevance, quantifying displacement presents difficulties due to limited replicability across cities and time periods and the need to analyse long time spans: displacement is a gradual process, impossible to capture in one data snapshot. We introduce a novel tool to overcome these difficulties. Using publicly available address change data, we construct four cubical complexes simultaneously incorporating geographical and temporal information of people moving, and analyse using Topological Data Analysis tools. Finally, we demonstrate this method through a 20-year case study in Madrid, Spain. The results demonstrate its ability to reveal patterns consistent with population displacement and identify the neighbourhoods and years potentially affected--patterns not observable from yearly maps of the raw address change data.
We study coordinated groups of wallets on a bonding-curve launchpad (pump.fun on Solana), using a complete record of 200\,443 token launches, 15\,854\,121 curve trades and 51\,025\,958 SOL transfers over 88.3 hours. A prospective, multi-channel discovery procedure (co-buying, shared funding, joint signing, transfers, consolidation) yields 23\,710 wallet groups. Without ground-truth labels we validate them by out-of-sample link prediction, independent non-trading links and a comparison with naive co-buy clustering at equal numbers of flagged tokens: raw clustering collapses into one giant component, yet it ranks tokens at least as well as the groups, so the value of the structure is descriptive. Group activity is highly concentrated and splits into serial creators and long-lived rings of early buyers. Known members buy earlier than other buyers of the same token and extract more cash than an activity-matched placebo set, while late buyers' net cash flow is negative; tokens with members show a pump-like price path, but the contrast vanishes when controls are restricted to comparable tokens, so we report it as an association. A follower entering at the earliest group signal loses money out of sample in all 32 configurations and does no better than random entry. A few pools of tens to over a hundred fast wallets are associated with a two- to four-fold higher chance of reaching 20 SOL, also within creators, with a smaller excess at 60 SOL and none for graduation; on a final hold-out the team rules keep a precision of about one half to three quarters, yet many signals arrive when the curve already holds 20 SOL, one team supplies most of them, and they add no information beyond early curve state. We also document evaluation errors that turn the same data into apparent profit.
Ming Xie, Shibo He, Qihao Huang, Yuzhen Fan, Jiming Chen
Spreading in real-world networks, including modern communication and networked systems, often unfolds through group interactions rather than isolated pairwise contacts. Higher-order network representations such as simplicial complexes, hypergraphs, and their temporal, multilayer, and directed extensions provide a principled way to encode group interactions. Unfortunately, existing studies on spreading over these substrates remain fragmented and largely model-centric, offering limited insight into how higher-order representations and contagion mechanisms jointly shape emergent dynamics. This review addresses this gap by introducing a structure-mechanism-behavior three-level perspective that provides a systematic mapping from higher-order network structures and spreading mechanisms to collective behaviors. Within this framework, we compare how different higher-order network representations encode group interactions, introduce a taxonomy of contagion mechanisms, and analyze their structural carrying capacity, defined as the ability of a given representation to support specific contagion mechanisms and the associated dynamical behaviors. We further map specific structure-mechanism combinations to three hallmark emergent behaviors, thereby offering a unified cross-level perspective beyond existing model-centric surveys. These insights are particularly relevant for communication systems involving group interactions, such as social information spreading, cooperative protocols, and distributed networked processes. Lastly, leveraging this three-level perspective, we offer a roadmap for guiding and controlling dynamics on higher-order networks, while discussing future directions in representation choice, mechanism design, and data-driven inference.
Instantaneous Quantum Polynomial-time (IQP) circuits are promising candidates for near-term quantum computing due to the conjectured classical hardness of their sampling task and their shallow, highly parallelizable structure. Despite extensive studies of their complexity-theoretic properties, the role of circuit connectivity in their trainability as variational ansatze remains poorly understood. In this work, we present an investigation of IQP circuits for Hamiltonian optimization that traces a mechanistic chain linking circuit connectivity, entanglement generation, and gradient scaling: increasing connectivity drives the circuit output toward Haar-like statistics and volume-law entanglement, and this increase in entanglement is consistently associated with an exponential decay of the gradient variance and degraded optimization performance, while sparser architectures retain stable gradients at the cost of global expressibility and entanglement capacity. This connectivity-entanglement-gradient mechanism produces a nontrivial connectivity-trainability trade-off, demonstrating that effective IQP-based Hamiltonian optimization requires a balance between global expressibility and trainability rather than maximal expressivity alone. In particular, on our benchmarks, a circuit generating no entanglement already matches the optimization performance of more highly connected circuits, and a circular topology retains entanglement capacity while preserving trainability, making it a broadly effective choice among the architectures considered.
Graph sparsification in large-scale networks often requires balancing structural accuracy and computational efficiency. Curvature-based methods, particularly Ollivier-Ricci curvature (ORC), have shown strong performance in identifying inter-community bridges, but their computational cost limits scalability. In this work, we propose a two-stage curvature-guided pipeline for graph sparsification with the goal of preserving community structure. We first use the Lower Ricci Curvature, a combinatorial lower bound of the ORC, to perform efficient edge filtering, substantially reducing graph size before applying the computationally intensive optimal-transport-based ORC curvature and Ricci flow. Results from empirical studies on synthetic and real-world datasets demonstrate that the combined LRC-ORC pipeline can achieve community-recovery accuracy comparable to ORC-only sparsification in terms of Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI), while reducing runtime on most evaluated benchmarks. Our findings suggest that integrating combinatorial and geometric curvature notions provides a computationally efficient and structurally principled framework for graph sparsification in complex networks.
BiliPicks documents seven years of Bilibili's Weekly Picks, a platform-curated editorial video series. The dataset covers 371 publicly listed issues and 13,758 curated videos from 22 March 2019 to 7 May 2026. It links issue dates, themes and editorial recommendations to video metadata, 47,799,657 top-level comments, 125,690,760 time-synchronised overlay comments (danmaku), and 13,739 cover images. Weekly issues provide a consistent unit for tracing changes in category composition, recurring creators and editorial framing. Corresponding comments and danmaku connect video-level discussion with audience expressions tied to particular moments, while covers add a visual dimension. We evaluate retrieval coverage and illustrate longitudinal analyses of selection, audience language and cover presentation. BiliPicks provides a dataset for future studies of platform curation, linking changes in published selections to their editorial framing and corresponding audience engagement.
Existing temporal link prediction methods typically rely on learned temporal representations to capture evolving interaction patterns. In this paper, we show that a classical structural proximity measure can match or outperform these methods without training or learned temporal sequence modeling. We revisit Personalized PageRank (PPR) on historical interaction graphs, using simple sliding-window retention or exponential time decay. Surprisingly, across 14 datasets, Time-decay PPR ranks first on nine and second on four among the compared methods, including datasets with few repeated edges. To understand this result, we examine the historical paths connecting source nodes to previously unseen destinations. We find that such paths contain useful predictive information, and that PPR outperforms a strong neural baseline on queries where historical structure clearly distinguishes the true destination from negative candidates. These results show that strong performance on current benchmarks does not necessarily require complex temporal models. More fundamentally, they raise the question of how much predictive value learned temporal modeling adds beyond historical structural proximity. Our findings motivate reassessing progress in temporal link prediction with strong classical baselines.
Xiaoyu Leng, Hongchao Qin, Rong-Hua Li, Guoren Wang
Hypergraphs capture higher-order interactions involving multiple entities. Existing decomposition models, such as $k$-core and nbr-$k$-core, rely on degree or neighborhood constraints and may overlook fine-grained density variations. We propose a $(k,δ)$-dense subhypergraph model based on capped hyperedge contributions. For each parameter pair, the unique dense set is the inclusionwise smallest maximizer of a penalized capped hyperedge contribution. These dense sets form a nested hierarchy characterized by egalitarian $δ$-orientations and hypergraph reachability. We develop three algorithms for dense subhypergraph search and two for complete density decomposition. Both decomposition algorithms use integral flow balancing: Flow-Decomp extracts density levels through strongly connected component condensation, while Reverse-Decomp uses direct reverse propagation. Once an egalitarian orientation is obtained, both recover all density levels in linear time. We further derive sandwich bounds that restrict computation to equivalent residual subproblems and develop incremental algorithms for maintaining the hierarchy under hyperedge insertions and deletions. Experiments on nine real-world hypergraphs, including datasets with over 15 million vertices, compare our methods with four established decomposition models. The results show that Reverse-Decomp improves decomposition efficiency, incremental maintenance outperforms recomputation in the tested settings, and the proposed model captures density structures overlooked by degree-based decompositions.
Marilou O. Espina, Sales G. Aribe, Mark Ian M. Mukara, Joseph L. Abella, Nurwahyu Alamsyah
IPOs contribute significantly to local economies, cultural preservation, and sustainable livelihoods within their ancestral communities. However, many IPOs remain marginalized in digital economies due to limited digital competencies, inadequate access to technology, and insufficient legal awareness related to online enterprise participation. This study examined the marketing practices, digital competencies, and legal-literacy needs of IPOs in the Philippines and developed a legal-digital empowerment framework for higher education extension and community engagement initiatives. Using a sequential mixed-methods design, data were collected through surveys and focus group discussions involving IPO members engaged in community-based enterprises. The study assessed existing marketing practices, identified competency and legal-awareness gaps, and determined capacity-building needs related to digital enterprise participation. Findings revealed that IPOs continue to rely heavily on traditional marketing channels, with limited use of digital platforms, inconsistent branding practices, and minimal engagement in strategic digital marketing activities. Participants also expressed concerns regarding intellectual property protection, online participation, and limited understanding of legal mechanisms related to digital enterprise engagement. Despite these limitations, IPO members showed strong willingness to participate in training and mentorship initiatives. The study proposes the Legal-Digital Empowerment Framework for Indigenous Community Enterprises, which integrates digital competencies, legal literacy, institutional support systems, and rights-based market participation into a structured and context-responsive model for higher education extension programs.
Information operations exploit coordinated activity on social media to amplify messages and manipulate public discourse. A common approach to coordination detection is to project account-entity interactions into account similarity networks based on temporal proximity, action counts, or behavioural similarity, and identify dense subgraphs or behavioural clusters. However, the current methodology finds groups of co-activated accounts or partitions all users into most similarly acting clusters, even when the accounts are acting independently of each other or the co-activity is due to some expected source. We formulate coordination detection based on hypothesis testing of account pairs and compare different null models. We connect the resulting pairwise scores to the planted-submatrix detection problem and derive statistical and computational limits as functions of group size, population size, and signal strength. We estimate the signal for 7 modalities of coordination over 26 labelled information-operation datasets, and compare the predicted regime with the performance of recovery heuristics. Our method achieves higher agreement with the labelled ground truth than previously reported, but the choice of null model is important and it changes the regime of most modalities. While it is always possible to extract a set of significantly densely co-acting accounts across nearly all modalities regardless of regime, density alone is uninformative of detection performance, rendering anomalous cluster discovery a poor proxy for recovering an information operation. Detectability boundaries establish whether an operation of a given size and signal can be recovered at all, and how much confidence a flagged group warrants.
Muhammad Ifte khairul Islam, Tanvir Hossain, Esra Akbas
Graph Contrastive Learning (GCL) has achieved remarkable success in self-supervised representation learning by maximizing agreement between augmented views of a graph. Although augmented view generation is the central step in GCL, existing models rely on stochastic augmentation of the input graph, such as edge perturbation and node masking, which lack structural awareness and frequently destroy critical topology or introduce semantically inconsistent views. While recent adaptive methods improve upon random perturbations, they primarily operate on localized node or edge neighborhoods and fail to capture higher-order, macro-level structural organization crucial for graph classification. To address this limitation, we propose Structure-aware Compression for Graph Contrastive Learning (SAC-GCL), a novel framework that employs structural decomposition and graph compression as a principled view generation strategy. Moving beyond random augmentations, SAC-GCL leverages structural decomposition techniques to extract cohesive subgraphs and condense them into supernodes, constructing coarsened graph views that preserve local density and capture higher-level structural patterns while reducing noise and redundancy. We leverage multiple cohesive subgraph models to derive complementary views of the graph, each capturing distinct structural properties. To capture multi-resolution information, a hierarchical view encoder processes these compressed views by initializing super-node features with shared-weight cohesive subgraph embeddings. Our results from extensive experiments on six benchmark datasets demonstrate that structure-aware compression provides a powerful alternative to random perturbations, yielding more robust and discriminative graph representations for graph classification tasks.
We introduce fastC-GC and fastC-GC$^*$, computationally efficient methods for time-series graph estimation using the two-test rule motivated by Reichenbach's common cause principle (RCCP) and the conditioning sets of causalised Granger causality (c-GC and c-GC$^*$). The methods use standardized data, ridge-residual correlation, and complete circular shift enumeration evaluated by Fourier transforms. Shared precision calculations and Schur downdates avoid repeated regression fits. On a single-threaded benchmark, fastC-GC was 68-277 times faster than the compared c-GC implementation, and fastC-GC$^*$ was 192-886 times faster than c-GC$^*$. Across four suites with reference graphs, aggregate mean area under the precision-recall curve (AUPRC) differed from the corresponding baseline by at most 0.019, but F1 was lower at the selected thresholds in every suite. The computational gains therefore accompany a change in estimator and selected graphs. Among eight methods, Bagged PCMCI+ had the highest mean AUPRC on sparse VAR, an autocorrelated structural process, and DREAM3; CUTS+ led on Lorenz-96. Wind-tunnel and river-discharge applications illustrate differences between inferred graphs without establishing causal accuracy. We prove the computational identities and a restricted population adjacency result for fastC-GC$^*$. Causal interpretation requires the stated graphical assumptions; validity of the implemented tests additionally requires a suitable shift-invariant null law. Empirical null rejection rates informed the conditional threshold $β= 0.005$, without establishing general test validity or graph-wide error control.
Stephen Prochaska, Mert Bayar, Ashlyn B. Aske, Joseph Schafer, Kate Starbird
Recent elections in the United States have seen online rumors become a regular feature of election discourse as voters and election officials work to navigate a sea of digitally spread claims and propaganda. For the uninitiated, these rumors can feel overwhelming and random, sowing doubt about the integrity and legitimacy of recent elections. In this paper, we reviewed and qualitatively coded a large dataset of 590 election rumors collected during the the 2020 and 2022 national elections. We found that there are tropes that recur time and time again in election rumors, highlighting how these rumors are not as random as they may first appear. We make two primary contributions in this work: first, we introduce a hierarchical framework that structures observed tropes in three tiers (Meta-category, Meso-trope, and Micro-trope). Second, combining the framework with our coded data, we identify a visible grammar of election rumors, highlighting how in the majority of cases a rumor contains an Actor, and Object, and an Action, with optional grammatical components of Suggested Remedies and Scene & Setting. The framework and grammar can be used by other researchers to act as the underlying information architecture of analyses examining similar rumors, enabling the rapid identification, analysis, and prediction of election rumors.
Anouar Imel, Imad Charradi, Abderrahman Sghir, Khalil El Khammar
When an LLM group's discussion is compressed into a short memory for a final decision maker, what should the memory keep? Two natural heuristics keep the rarest claims or the claims relevant to the decision criteria. On hidden-profile tasks both look attractive, because how widely a fact is held is confounded with whether it helps. We introduce CrossProfile, a diagnostic benchmark of 60 templates with automatically verified answer keys, in which the ownership of supporting, decoy, and irrelevant facts is crossed while their text is held fixed (480 tasks). Across Qwen2.5-7B, Llama-3.1-8B, and two DeepSeek models, both heuristics fail in predictable places. Selecting by relevance readmits decoys, because decoys are on topic: even a perfect relevance filter loses 23 to 31 points to rarest-first selection in the classic hidden-profile configuration. Rarest-first selection is no better than random selection on average and wins only where the rare facts happen to be the useful ones. In both cases accuracy follows the balance of evidence the memory keeps, not the policy that kept it. The decision model adds a third failure, a form of social proof: it gives extra weight to facts attributed to many agents. Removing the attribution tags, or reassigning them at random with line order fixed, raises accuracy in the classic configuration by 13 to 24 points. In natural discussions the evidence is lost before any memory acts: the complete transcript yields lower accuracy than no notes at all.
Eigenvalues are a workhorse of graph machine learning: spectral clustering, spectral graph neural networks, and graph kernels all summarize a network by the eigenvalues of a matrix. These eigenvalues are almost always treated as a static list of numbers. When a graph changes, however, its eigenvalues move, and when the matrix is not symmetric, they move in the complex plane, where they can wind around each other. The pattern of this winding is a braid, a classical object from topology. We propose spectral braids, a methodology for dynamic directed networks: each snapshot is aggregated into a community-level transition matrix, its complex eigenvalues are tracked over time, and the braid they trace is read off as a word. The word is then mapped to a matrix by the Burau representation, which gives a fixed-size feature vector for any classifier. The braid has a concrete meaning: each eigenvalue describes how a group of communities behaves, and each crossing records one group overtaking another. We evaluate the method on dynamic network classification, using synthetic networks of online communities whose labels describe how the network evolved between the same starting and ending states. Our experimental results show that the proposed features reach 97.1% accuracy on four classes, against 44.5% for the best feature set that does not track eigenvalues, and that the Burau features also separate networks containing the same events in a different order, which simple counts of crossings cannot do. To the best of our knowledge, this is the first study that defines braids and braid monodromy on graph data and uses them as features for learning on dynamic networks.
Much of the manipulation in influencer content is not false but disproportionate, framing accurate facts as historic, urgent, or catastrophic. Existing approaches either verify factual claims or detect clickbait and propaganda in single-platform, single-language text; however, they overlook where such framing originates, because they treat a post and the replies beneath it as one unit. To address this gap, we measure rhetorical distortion with one instrument applied identically across six platforms and two languages, classifying creator posts and their replies separately. Our three-tier pipeline detects five literature-grounded dimensions (Significance Inflation, Anxiety Manufacturing, Novelty Claims, Loaded Language, and Temporal Distortion): bilingual lexical rules propose candidates, negative filters cancel known false positives, and an LLM verifies or relabels the candidates. This design keeps labels transparent and comparable across platforms, calls the LLM on only 14% of posts, and supports validation against human annotators and a second model. On 13,060 posts from 330 accounts on RSS/newsletters, YouTube, Bluesky, Reddit, Weibo, and X/Twitter, platform is far more strongly associated with account-level distortion than content category (partial $η^2=0.49$ vs. $0.09$), and X/Twitter scores highest (DI 7.74 vs. 0.78 for RSS). On X/Twitter, the top-5 replies under a post carry 3.5x the distortion of the tweet they answer (1.5x at equal length), while creator tweets alone are indistinguishable from YouTube and Bluesky; two human annotators and GPT-4o confirm this gap. Measured distortion on threaded platforms thus lives largely in the audience layer, which calls for measuring creator and reply text separately.
Graph-level anomaly detection (GLAD) identifies unusual graphs in applications such as molecular screening, often using only normal graphs for training. Yet the evaluations of some detectors select models with test labels, and high benchmark scores alone do not show whether detectors recognize structural changes. We audit released code and compare the area under the receiver operating characteristic curve (AUROC) for model states (checkpoints) chosen with and without test labels from the same training run. We separately compare test-tuned hyperparameters with repository defaults and test sensitivity to structural changes while controlling graph size. We evaluate 21 detectors on the UB-GOLD benchmark and on synthetic and molecular graphs with controlled structural changes. About half of the checkpoint-selection gain is linked to choosing a checkpoint and reporting its performance on the same labeled test set. The mean gap persists across training seeds and when normal and anomalous graphs come from different datasets. Most detectors miss many tested structural changes that simple counts detect. With tuned hyperparameters, the two leaders remain the top two by AUROC under every checkpoint rule. Across nine main datasets, test-label checkpoint selection raises the mean AUROC of UB-GOLD's 12 detectors by 5.12 points relative to the final checkpoint.
Xiaojing Du, Chunlei Meng, Wentao Gao, Ke Sun, Feiyu Yang
On online social platforms, equal seed access to interventions can yield unequal group outcomes through social spillovers, so an access audit alone can miss outcome disparities. We characterise this trade-off through an exact allocation-structure decomposition and a sharp first-moment floor for graph-independent seeding under additive spillovers on stochastic block networks. Decoupled Procedural Allocation (DPA) combines group quotas with selection by seeds' contributions to each group's outcome. Rankings balance total outcome against disparity, and within-quota exchanges reduce the gap. Computable bounds quantify retained total outcome. DPA achieves the smallest mean outcome gap among the compared methods at the common access tolerances on both development and prospective suites. Exposure and independent-cascade studies show how the propagation mechanism changes this trade-off. On Pokec, DPA nearly equalises modelled gender outcomes at the closest access-parity quota while preserving most of the degree-based allocation's total outcome.
Qianyun Wu, Bruno T. Sugano, Genta Toya, Kei Ichikawa, Yasuhiro Hashimoto, Masashi Toyoda, Naoki Yoshinaga, Kazutoshi Sasahara
Information cascades on social media are conventionally conceptualized as directed, feedforward branching processes. However, real-world diffusion pathways frequently deviate from pure hierarchical trees due to localized clustering, reciprocal commentary, and multi-wave temporal surges. In this work, we quantify the directional asymmetry and hierarchical structure of empirical information cascades on X (formerly Twitter) using spectral non-normality via Henrici's departure from normality. Analyzing approximately 58,000 cascade networks across diverse topics (including politics, entertainment, natural disasters, etc.), we investigate (1) how non-normality relates to temporal dynamics such as endogenous-like versus exogenous-like patterns and burstiness, (2) whether non-normality is correlated with the peak concentration or overall size of a cascade, (3) whether the overall non-normality of a cascade's network structure can be predicted from its early stages. We find that non-normality strongly aligns with peak concentration (peak/N) rather than overall cascade size, characterizing cascades governed by rapid, asymmetric forwarding. Furthermore, while early-stage structural forecasting (<= 30% of nodes observed) exhibits expected baseline uncertainty (51%-72% accuracy at a +/- 20% error tolerance), predictability consolidates rapidly during intermediate growth, exceeding 80% across all dynamic clusters once 50%-60% of the network is observed. By identifying the topological and dynamic correlates of cascade structures, this study advances our understanding of information flow and establishes a quantifiable benchmark for forecasting directional diffusion architectures.
Intelligent reflecting surface (IRS) has emerged as a promising wireless technology for enhancing legitimate communications. In this paper, we investigate an IRS-assisted multiuser MISO downlink network in which an attacker reconfigures the IRS to degrade legitimate data transmission. Specifically, we consider a black-box attack setting in which the attacker has access to neither channel information nor symbol-level observations and can observe only the BER reported by each user. The objective of the attacker is to identify an IRS configuration that maximizes the minimum BER among all users under a limited budget of non-repeated tests. To address this BER-only black-box attack problem, we propose PRIS, an online search method that learns effective phase transitions from previous trials and progressively narrows the search from large-block modifications to single-element refinement. Specifically, an annealed user-balancing reward and prioritized replay are incorporated to exploit noisy BER feedback. Numerical results demonstrate that PRIS achieves a higher minimum BER than existing benchmarks.
Zhoufu Wang, Baoshen Guo, Zhiqing Hong, Junyi Li, Kailai Sun, Heye Huang, Alok Prakash, Shenhao Wang 等 9 位作者
Human mobility generation aims to synthesize realistic point-of-interest (POI) visitation trajectories and has become an important tool for travel behavior modeling, transportation management, and urban planning. Existing diffusion-based methods achieve high fidelity but require per-city generation, given the inherent heterogeneity of geospatial locations and POI categories, while large language model-based methods generalize across cities but remain too costly at scale, especially for long-horizon trajectory generation. To address this, we propose SeMoFlow, a Semantic human Mobility generation framework based on latent Flow matching. We first encode heterogeneous POIs from different cities into a shared cross-city representation space via hierarchical Semantic IDs, where shared prefixes capture transferable semantics, and successive codes progressively refine the representation toward individual POIs. Building on the semantic IDs, SeMoFlow follows a plan-to-synthesis hierarchical generation paradigm, in which an autoregressive planner generates coarse-grained semantic and recurrence patterns, and a flow matching realizer synthesizes fine-grained suffix latents. The generated latents are subsequently decoded and grounded to concrete POIs. Extensive experiments on large-scale multi-city datasets show that SeMoFlow achieves higher trajectory fidelity than existing baselines, preserves city-specific mobility motifs, and supports both joint multi-city generation and effective cross-city transfer. The code is available at \href{https://github.com/urban-mobility-generation/SeMoFlow}{https://github.com/urban-mobility-generation/SeMoFlow}.
When individuals in a social network learn about an unknown state from private signals and neighbors' actions, the network structure often causes information loss. We consider rational agents and Gaussian signals in the canonical sequential social-learning problem and ask how the network changes the efficiency of signal aggregation. Rational actions in our model are log-linear functions of observations and admit a signal-counting interpretation of accuracy. Networks where agents observe multiple neighbors but not their common predecessors confound information, and even a small amount of confounding can lead to much lower accuracy. In a class of networks where agents move in generations and observe the previous generations, we quantify the information loss with an aggregative efficiency index. Aggregative efficiency is a simple function of network parameters: increasing in observations and decreasing in confounding. Later generations contribute little additional information, even when generations are arbitrarily large and agents observe arbitrarily far into the past.
Marco Mancastroppa, Giulia Cencetti, Iacopo Iacopini, Giovanni Petri, Alain Barrat
Temporal hypergraphs provide an interesting representation for many social systems, as they integrate both the temporal and group nature of social interactions. Designing models of time-varying hyper-networks yields thus a convenient way to generate surrogates of empirical social systems, to understand the mechanisms underlying their emergent properties, and to investigate the evolution of dynamical processes taking place on such structures. Here, we focus on a specific and powerful class of these models, the activity-based temporal hypergraphs. We show how activity mechanisms provide control over the time scale of the dynamics, allow high modelling flexibility, and capture heterogeneity in agents' behavior. We also highlight how the activity can straightforwardly be extracted from empirical data to create surrogate datasets. We present paradigmatic examples of such models: the higher-order activity-driven models (HADx), which show that different underlying rules for the selection of active interactions produce different higher-order dynamics; the Emerging Activity Temporal Hypergraph model (EATH), able to generate surrogates of empirical systems by using higher-order mechanisms driven by both activity and group memory. We conclude discussing future perspectives for activity-based temporal hypergraphs, highlighting the potential of this framework to bridge realistic modelling of social dynamics with the study of the complex dynamical process they can support, from social contagion to opinion dynamics.
Ali Al-Lawati, Nafis Tripto, Abolfazl Ansari, Fali Wang, Jason Lucas, Suhang Wang, Dongwon Lee
The emergence of multi-agent systems introduces novel moderation challenges that extend beyond content filtering. Agents with $\textit{malicious intent}$ may contribute content that appears benign to evade content-based moderation, while serving a hidden objective. To address this challenge, we introduce Bot-Mod a moderation framework that grounds detection in agent intent rather than traditional content-level signals. Bot-Mod interrogates a target agent using a fixed set of diagnostic probes and infers its intent through Bayesian posterior inference over candidate intents. It starts with a calibrated content-only prior and updates the posterior using the $\textit{likelihood}$ of each answer under every candidate intent, as assigned by the moderator, until a stopping criterion is met. To evaluate our approach, we construct Molt-Intent, a dataset derived from Moltbook, capturing diverse benign and malicious behaviors within real community structures through posts and comments. Across Molt-Intent, an external social engineering dataset, and three agent LLMs, Bot-Mod outperforms content-only moderators, a fine-tuned classifier, and judge-based interrogators both in- and out-of-distribution. On Molt-Intent, it reaches a composite F1 of 0.736 in distribution and 0.684 out of distribution, 22% and 14% above the best baseline.
Demand on blockchain-based marketplaces arrives in bursts and at a high rate. On these platforms the data are completely visible, which makes it feasible to compare the bursty demand against the usual load. The congestion manifests as an increased demand for gas, the primary resource a blockchain sells. Existing studies consider how to price the gas. The demand itself, and what it is made of during bursts, has not been measured. We measure it on Robinhood Chain, a layer-2 blockchain that quickly became a venue for trading tokenized equities and memecoins. We collected the data from every block of its first three active months and more than 50 million Uniswap swaps that occurred during bursty periods. We find that a burst trades about as many pools as a quiet period, mostly different ones, and trades them harder, on top of elevated demand that accumulated for hours. With the bursts aligned on their peaks, the median peak is 2.5 times the usual load. Both the rise and the fall of the demand are described by the sum of two exponential functions with different timescales, a slow one of hours and a fast one of minutes. Against a matched quiet period, a burst has 1.05 times the pools, 1.40 times the trades, and 1.21 times the senders. We use two models to explain the arrival of bursts: externally triggered cascades and self-excitation. The former fits the data relatively better. Inside a burst, the entry of each trader into a pool is followed for about 24 seconds by more entries than the existing traders predict. Whether this is because of imitation or because of a shared signal is left open.