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arXiv2026

Equilibria in Network Constrained Markets with System Operator

We study a networked economic system composed of $n$ producers supplying a single homogeneous good to a number of geographically separated markets and of a centralized authority, called the market maker. Producers compete à la Cournot, by choosing the quantities of good to supply to each market they have access to in order to maximize their profit. Every market is characterized by its inverse demand functions returning the unit price of the considered good as a function of the total available quantity. Markets are interconnected by a dispatch network through which quantities of the considered good can flow within finite capacity constraints and possibly satisfying additional linear physical constraints. Such flows are determined by the action of a system operator, who aims at maximizing a designated welfare function. We model such competition as a strategic game with $n+1$ players: the producers and the system operator. For this game, we first establish the existence of pure-strategy Nash equilibria under standard concavity assumptions. We then identify sufficient conditions for the game to be exact potential with an essentially unique Nash equilibrium. Next, we present a general result that connects the optimal action of the system operator with the capacity constraints imposed on the network. For the commonly used Walrasian welfare, our finding proves a connection between capacity bottlenecks in the market network and the emergence of price differences between markets separated by saturated lines. This phenomenon is frequently observed in real-world scenarios, for instance in power networks. Finally, we validate the model with data from the Italian day-ahead electricity market.

cs.GT↗

arXiv2026

When Design Rules Break: Benchmark Composition Determines Whether Label Informativeness Predicts GNN Aggregator Choice

We examine whether graph neural network (GNN) design rules generalize across benchmark families by studying aggregator selection (sum, mean, max) on 24 node-classification datasets spanning citation, heterophilic, LINKX Facebook-100, co-purchase, and co-authorship graphs. Edge homophily is only weakly predictive of the GIN-Sum versus GIN-Mean performance gap. Label informativeness predicts this gap well on legacy benchmarks but degrades substantially when Facebook-100 graphs are included. In these dense friendship networks, near-zero label informativeness coexists with a strong preference for sum aggregation, producing gains of 7-10% and up to 13% under extended training. Stochastic block model ablations, including degree-corrected variants matching Facebook-100 degree scales, fail to reproduce this behavior, indicating that mean degree alone does not explain the effect. Among several label-independent graph statistics, the spectral gap uniquely distinguishes these graphs from other low-informativeness datasets, with the effect localized to one-hop neighborhoods and replicated across architectures. We further identify training regimes that interact with aggregator choice and show that PNA can underperform the best single-aggregator GIN on standard citation benchmarks. Our results suggest that benchmark composition, rather than numerical insufficiency, determines whether design rules appear to generalize, and that the Facebook-100 regime provides a concrete target for future adaptive aggregation methods.

cs.LG↗

arXiv2026

CoDiNG -- Naming Game with Continuous Latent Opinions of Individual Agents

Understanding the mechanisms behind opinion formation is crucial for gaining insight into the processes that shape the spread of political beliefs, cultural attitudes, consumer choices, and social movements in society. This work introduces a realistic model of opinion dynamics that captures the intricacies of real-world opinion dynamics by synthesizing principles from cognitive science. The proposed model is a hybrid continuous-discrete extension of the well-known Naming Game opinion model. The continuous layer captures the strength of each opinion through reinforcement and forgetting in the human brain, akin to memory imprints. The discrete layer allows for converting intrinsic continuous opinion into a discrete form, which often occurs when we publicly verbalize our opinions. We evaluated our model on longitudinal data combining real communication events with repeated surveys of the same individuals, comparing it against the Naming Game, the hybrid SJBO model, and four simple baselines at the population and the individual level. Unlike rigid baselines and the classic Naming Game, which inherently capture only a single aspect, hybrid models can be tuned to model either individual-level opinions or aggregate opinion dynamics. However, this flexibility comes with a strict trade-off, as they cannot accurately reproduce both simultaneously. Out of the six analysed topics, our model exceeds or matches SJBO, showing that reinforcement and forgetting grounded in cognition contribute to explaining opinion dynamics. Additionally, in our empirical data, individuals change their opinions while the aggregate distribution stays almost stationary, so a model reproducing no dynamics can still score well. This observation indicates that evaluating opinion models must be multidimensional and rely on more than one metric.

cs.SI↗

arXiv2026

Dilemmas and trade-offs in the diffusion of conventions

Outside ideal settings, conventions are shaped by heterogeneous competing processes that can challenge the emergence of norms. In order to acknowledge this complexity, this paper develops a generalized account of conventions and identifies three trade-offs involved in their diffusion: (I) the trade-off between the imperatives of social, sequential, and contextual consistency that individuals balance when choosing between conventions; (II) the competition between local (bottom-up) and global (top-down) coordination, depending on whether individuals coordinate their behavior via interactions throughout a social network or external factors transcending the network; and (III) the balance between decision optimality (e.g., collective satisfaction) and decision costs when collectives with conflicting preferences choose a convention. A broadly applicable statistical physics framework for exploring these trade-offs is developed and applied to a sign convention in physics. The method can infer the structure of the underlying coordination game, the networks of social interactions involved, and the processes through which conflicts are resolved. This shows that the purpose of conventions may exceed coordination, and that individual preferences towards conventions are concurrently shaped by cultural factors and multiple social networks. Finally, this work emphasizes the role of leadership in the resolution of conflicts.

physics.soc-ph↗

arXiv2026

Who Should Teach? Confidence-Aware Dual-Teacher Learning for Few-Shot Node Classification on Text-Attributed Graphs

Text-Attributed Graphs (TAGs) integrate graph structures and node-associated textual attributes, and recent studies have increasingly leveraged Large Language Models (LLMs) to improve TAG learning in few-shot settings. However, existing approaches typically utilize LLM-derived information uniformly across all nodes, despite substantial variations in its reliability, while also incurring considerable monetary costs. We argue that the most appropriate source of supervision may differ across nodes, as Graph Neural Networks (GNNs) and LLMs exhibit complementary strengths in exploiting structural and semantic information, respectively. To this end, we propose CoTeach, a Confidence-aware dual-teacher learning framework that dynamically selects the more reliable teacher for each node. Experimental results demonstrate that CoTeach consistently improves few-shot node classification performance while reducing unnecessary LLM utilization and associated monetary costs.

cs.LG↗

arXiv2026

Scalable dynamic community detection on temporal graphs using graph neural networks

Dynamic community detection on temporal graphs seeks to identify evolving community structures while allowing node memberships to change over time. In this work, we formulate dynamic community detection over observed node-time instances, where each node-time instance in the temporal interaction stream is assigned a cluster label. We propose a diffusion-guided contrastive learning framework that uses a local temporal diffusion affinity matrix to construct positive and negative node-time pairs and organise the learned representations according to their temporal structural relationships. We then apply a clustering algorithm to the resulting embedding space to detect dynamic communities. Experiments on synthetic temporal networks show that the proposed method outperforms static community detection baselines and achieves competitive or better performance than existing dynamic community detection methods in terms of AMI and ARI, while maintaining good scalability. We further apply the method to a large-scale OpenAlex computer science collaboration network from 2016 to 2025, revealing persistent and evolving collaboration communities in real scientific data. These results suggest that time-node-level representation learning provides an effective framework for scalable dynamic community detection on temporal graphs.

cs.SI↗

arXiv2026

Structural Change and Random Graph Models in Global Oil Trade Networks

We studied structural change in global oil trade using a network approach. Using UN Comtrade data, we examined the temporal evolution of international trade networks, with an emphasis on crude oil. Weighted in-degree identified major changes in country rankings in 1991, 2011, 2017, and 2021, while PageRank detected pronounced changes around 1991 and 2024. The Louvain algorithm identified clear geographic communities within the overall trade network. In the oil trade network, modularity declined from the 1990s to the 2010s, with node2vec embeddings showing weaker clustering in 2011 than in 1991. We also compared the oil trade network with several random graph models using 3- and 4-node subgraph profiles and machine learning classification. The oil trade network was consistently classified as a Chung-Lu graph, while the Geometric model was not favored, suggesting that a model based on the degree distribution better matched its subgraph profiles than the other models considered.

cs.SI↗

arXiv2026

Understanding Venture Capital Syndication in Information Technology Sectors: A Network Formation Perspective

Venture capital syndication enables investors to pool diligence, share risk, and signal venture quality, while shaping the relationships through which investment networks develop. We examine how prior relationships, network embeddedness, and organizational similarity structure annual co-investment link formation in U.S. information technology venture finance. Using dyad-complete PitchBook panels for the hardware, software, and hybrid subsectors from 1966 to 2024, we test seven mechanisms through full-sample dyadic logit models with dyad-clustered standard errors. Across subsectors, prior collaboration is the most consistent correlate of co-investment; shared partners, geographic proximity, and organizational-type similarity are also positively associated with link formation, while domain overlap, prominence, and experience vary across settings. A static ERGM of the 2024 software network among 1,100 persistently active investors likewise produces positive estimates for triadic closure and geographic homophily and a smaller positive estimate for type homophily. By combining complete dyadic risk sets with a whole-network specification, the study shows how relational persistence, network closure, and homophily jointly structure IT venture syndication networks. In future work, we will extend the analysis with temporal network models, counterfactual simulations of market shocks, and evaluations of network-aware partner recommendations.

cs.SI↗

arXiv2026

Algorithmic Simplification for Million-Vertex Diffusion History Reconstruction

Diffusion history reconstruction infers latent node states between sparse observations of SI or SIR processes. HERMES combines parameter fitting, a learned graph-neural proposal, and feasibility-aware Markov chain Monte Carlo. We remove these stages one at a time and evaluate each version on all 12 canonical datasets. The final method uses deterministic mean-field forward-backward inference, threshold decoding, and fixed rates. This fixed-rate variant, Battus-Z, achieves mean macro-F1 of 0.8726 and NRMSE of 0.1010, compared with published HERMES aggregates of 0.8692 and 0.1483. The benchmark pins the final observed frame before scoring, so we also exclude all observed frames. Under this metric, Battus-Z obtains macro-F1 0.8431 and NRMSE 0.1181. Thus, the learned proposal, MCMC, and fitting stages can be removed while retaining the published aggregate quality on the evaluated HERMES benchmark and scoring protocol. A CUDA implementation processes generated histories with up to 4.84M vertices on LiveJournal and 117M edges on Orkut. On the same CUDA backend, Battus-Z reduces the geometric-mean algorithm interval relative to fitted Battus by 5.1x for SI and 20.3x for SIR. Its event-weighted causal-violation rates are 7.50% for SI and 8.77% for SIR; graph-constrained decoding remains future work.

cs.SI↗

arXiv2026

Are Widely Known Findings Easier to Retract?

Failures of retraction are common in science. Why do they occur? And what determines whether a retraction is successful? We use data from citation records and Altmetrics to test proposed answers to these questions. LaCroix et al. employ network models to argue the social spread of information helps explain failures of retraction. One prediction is that widely known results, surprisingly, should be easier to retract, since their retraction is more relevant. Our results support this conclusion. We find highly cited papers show more significant reductions in citation after retraction and garner more attention to their retractions as they occur.

cs.DL↗

arXiv2026

Community corrections have divergent downstream effects across corrected accounts

Community-based fact-checking can reduce the spread of annotated misleading posts, but whether it produces lasting behavioral change among corrected authors remains unclear. Here, we conduct a large-scale quasi-experimental study of the Community Notes system on X (formerly Twitter), tracking four weeks of activity before and after note display for 19,854 accounts and 57,935 corrections (noted posts and matched controls), covering 11,909,591 original posts. Difference-in-Differences estimates show that note display is followed by an average 2.9% increase in corrected accounts' original-post activity. This aggregate conceals two divergent trajectories. Accounts corrected only once reduce their activity by 2.4% and subsequently publish less toxic and less misleading content. Repeatedly corrected accounts, which constitute 28.6% of corrected accounts but produce 73.4% of fact-checked posts, instead increase their activity by 4.4% after their first correction and show no detectable response to later ones. They exhibit no comparable content improvement, and instead publish more highly misleading posts, cite lower-quality domains, and post more political content. Community notes can thus constrain individual misleading posts without durably improving the behavior of the accounts most responsible for them, indicating that correcting content and changing its producers are distinct objectives for platform design.

cs.SI↗

arXiv2026

Why People Share Social Media Screenshots

Posting screenshots of social media posts is a common way of increasing platform functionality, enabling inter-platform interoperability, and gives users more control over how content is presented and interpreted. While these are legitimate purposes, screenshot sharing is also used in ways that can remove context, hide the original source, or mislead audiences. We examine the motivations behind sharing screenshots of social media posts on social media platforms like Twitter/X, Instagram, and Facebook. By focusing on user motivations, our study contributes to understanding how screenshot practices shape the sharing and interpretation of information in online spaces.

cs.SI↗

arXiv2026

Criticality and universality in network dismantling

Identifying the smallest set of elements whose removal dismantle a complex network, known as the network dismantling problem, is a fundamental task with many practical applications. Whereas network dismantling has been extensively studied over the past decade, most work has focused on developing efficient algorithms for large but finite networks. By contrast, the physics of the network dismantling process, namely how the network structural connectivity is affected by the removal of nodes or edges, remains largely unexplored in the thermodynamic limit. Here, we shed light on this understudied aspect of network dismantling by introducing an adaptive biased percolation process able to optimally dismantle a network. Through a systematic analysis of synthetic network models, we find that the proposed percolation process displays a universal phase transition, characterized by the abrupt and simultaneous disappearance of both the giant connected component and the largest 2-core, across networks with markedly different degree distributions. Simulations on real networks further support this universality, indicating that the physics of network dismantling is insensitive to a broad range of topological properties. Together, these results suggest that a topology-agnostic theory could be developed to explain the critical behavior of network dismantling.

physics.soc-ph↗

arXiv2026

A Novel Information Workflow for Structural Behavioural Analysis in Dynamic Attributed Graphs

Social systems play a vital role through interactions around shared interests and the maintenance of relationships, which are part of society. These can be represented as networks, and their analysis plays a crucial role in dynamic environments, which can be achieved through link prediction and community detection tasks. Several link prediction approaches estimate new relationships, such as suggesting friends on social media. On the other hand, a social faction is a group of connected people that reveals the internal structure of the social system and can be identified through community detection approaches. The evolution of social systems can be understood using these approaches by analysing their structure and behaviour. To handle this, a combination of these approaches, along with interaction-based analysis, is used to form cohesive communities, thereby enabling the study of social systems dynamically. In addition, attribute information plays a critical role. Most real-world graphs, like Facebook and Twitter, have attribute information that provides context, and integrating them with structural information offers deeper insights into patterns, which is a critical task. However, existing approaches have limitations in handling dynamics of social systems. Additionally, no existing approaches in the literature help identify potential links and form densely connected communities for attributed graphs. To address these challenges, we have proposed an information workflow, i.e., inc-LPCDAG (\underline{inc}remental Link Prediction and Community Detection in Attributed Graphs). We conducted experiments to evaluate efficiency, and results demonstrate the effectiveness of our proposed workflow by analysing and understanding structural and behavioural processes.

cs.SI↗

arXiv2026

Discretization-free exact recovery in geometric community detection

Geometric community detection seeks to recover latent communities in networks where connectivity depends jointly on community structure and continuous spatial geometry. Existing exact-recovery approaches typically discretize the underlying space, which can impose restrictive structural assumptions on the connectivity functions. We develop a polynomial-time, discretization-free algorithm for exact recovery in the Geometric Hidden Community Model (GHCM), operating directly on the continuous geometry. Our method succeeds even when connectivity functions coincide on a nontrivial portion of their visibility range and when two communities can be distinguished only through their connectivity to a third community. We prove exact recovery under these weaker conditions and provide experiments showing that the algorithm succeeds beyond the theoretically guaranteed regime.

cs.SI↗

arXiv2026

Evaluating GNNs for Success Prediction in Artist Collaboration Networks

As the music industry becomes an increasingly collaborative effort, understanding the underlying structures of the artist network has become a focal point in cultural data analytics. This study expands on the previous analyses of the Italian and Danish networks by introducing a novel dataset of the Polish music scene. By utilizing methodologies used in the prior studies, this work enables a direct comparison between three distinct European music landscapes and allows to merged the created networks into one. Furthermore, this research introduces a framework to test the efficacy of Graph Neural Networks (GNNs) for artist popularity predictions based on the metadata and the position in the network. The statistical analysis revealed that the Polish and tri-national network exhibit similar properties and clustering behaviours, consistent with prior models. An evaluation of the predictive architectures reveals that while GNN models achieve a comparable F1-macro scores to the Multilayer Perceptron (MLP) in specific cases however the MLP remains a superior model regarding the success metric. The results suggest that internal node features - such as genre and label affiliation might carry more predictive capabilities than the topology of the network. The higher performance of the GNN models in the tri-national network might also suggests that the relational features become more informative when the network spans multiple linguistic and geographic boundaries, with the GNNs potentially capturing complex 'bridge' structures between the merged networks.

cs.SI↗

arXiv2026

Measuring Collective Semantic Change in Populations of Language Model Agents

Collective semantic change in populations of language model agents is a measurable dynamical phenomenon. We present a passive longitudinal instrument called Kopterix that observes the semantic state of an agent population as a sequence of bounded observations under a protocol defined before the observations begin. Each observation divides the sampled feed by post age into surface, mid-stream, and residue layers, which makes semantic differences across content age measurable alongside run-to-run change. We validate the instrument on Moltbook, an agent-native social platform, over a two-month window of scheduled observations, with the periodicity check extended across approximately four months. At the lexical level, rarefied entropy resolves an April-May difference in the evenness of the stored top 200 unigram distributions, and adjacent states are lexically closer than states paired after timestamp shuffling. At the geometric level, grand mean centering exposes the scale of a common embedding direction, and scheduled shuffle checks support a recurring excess in the mid-stream to residue separation relative to the shuffled reference. At the temporal level, detrended scalar quantities and centered layer centroids lose much of their similarity over several hours, and a weaker positive component declines across longer separations with no strong weekly recurrence. Several attractive apparent structures failed their controls, and each reading is limited to the level its controls support. The design applies wherever a population of agents produces a timestamped language environment that can be observed repeatedly and divided by content age.

physics.soc-ph↗

arXiv2026

Graph Neural Team Recommendation: An Integrated Approach

Team recommendation aims to select an optimal subset of experts who can form an almost surely successful collaborative team for a given set of required skills. State-of-the-art methods are neural multi-label classifiers that transfer dense vector representations of skills into a sparse occurrence vector representing the optimal subset of experts. Such methods, however, overlook experts' relational and structural information encoded in the expert collaboration graph and, thus, fall short of capturing complex inter-dependencies among experts and their associated skills within teams. Moreover, the skills' dense vectors are pretrained disjointly and independently of the underlying neural classifier, hence, preventing end-to-end optimization. In this paper, we propose to reformulate the team recommendation problem into end-to-end link predictions in the expert collaboration graph to consume multi-hop intra-team and cross-team collaborations among experts while eschewing the unnecessary complexities of the disjoint two-phase training procedure. Our experiments on two large-scale datasets from various domains with distinct distributions of skills in teams demonstrate the superiority of the end-to-end approach and establish a new state of the art. Our code is available at https://github.com/fani-lab/OpeNTF.

cs.SI↗