Search Results (103)

Accurate identification of road network intersections is essential for urban planning, autonomous navigation, and traffic safety analysis. However, standard approaches relying on local geometric attributes often overlook essential topological information. This limitation is particularly problematic for intersection types that are locally similar but topologically distinct. To address this, we propose a hybrid framework that augments intrinsic node attributes with Generalized Random Dot Product Graph embeddings and neighbor-aggregated features. We utilize tree-based ensemble classifiers, specifically Random Forest and Extreme Gradient Boosting, to process this enriched feature set. Unlike standard spectral methods that assume homophily, this approach explicitly models heterophilous connectivity to capture structural patterns where dissimilar nodes connect. Experiments on a real-world urban road network demonstrate that this topological augmentation yields consistent and robust improvements. The proposed integration with the Extreme Gradient Boosting model achieves a Macro ROC AUC of 0.8966 and a Micro F1 score of 0.7005, outperforming the baseline model (ROC AUC 0.8100, Micro F1 0.5919). Performance gains are most pronounced for topologically ambiguous intersection classes, confirming that local attributes alone fail to capture structural distinctions. These results demonstrate that latent structural context is a critical discriminator for granular road intersection classification. Full article

Traditional social recommendation methods often focus on static representations of users and items, neglecting dynamic changes in user interests and item attractiveness over time, which makes it challenging to adapt to temporal variations in user interests. Additionally, the propagation of information along explicit social relationships tends to over-smooth features and weaken individual preferences, while static implicit relationships may increase short-term noise. Thus, a Dual-channel Controllable Diffusion Network based on Hybrid Representations (HR-DCDN) is proposed for social recommendation. The HR-DCDN first incorporates temporal factors by combining dynamic and static representations to capture changes in user interests and item attractiveness. Then, our method proposes a dual-channel aggregation mechanism to obtain higher-order representations of users and items. Explicit social relationships serve as the social-influence channel, while implicit social relationships discovered via dynamic implicit relationship mining constitute the preference-homophily channel. In addition, a learnable polynomial spectral filter incorporates residual connections and dual-channel fusion information at each propagation step, stabilizing deep propagation and alleviating representation homogenization to a limited extent while preserving high-frequency preference information. Finally, we jointly optimize a cross-layer InfoNCE objective on the perturbed interaction branch with the supervised rating loss, which provides an additional empirical regularization effect, improves robustness, and helps preserve representation diversity without altering the graph structure. Experimental results demonstrate that our model outperforms baseline methods on two real-life social datasets. Full article

Background: Occupational gender segregation is a key driver of labor market inequality and is prominent across occupations, within occupations, and within workplace task groups. This paper explores how structural arrangements and cultural messages shape women’s preferences for working with men vs. women. With respect to structural arrangements, we analyze how women’s relative power on a team influences their partner preference. With respect to cultural messages, we examine how one common source of information that has the potential to either challenge or reify notions of gender difference—sexual harassment policy training—affects partner preference. Methods: We conducted a laboratory experiment in which we placed 100 college-aged women in positions they may commonly find themselves in at the start of a new job—identifying coworkers to partner with on group tasks—and varied (1) their relative power on the team (leader or helper) and (2) exposure to workplace training (sexual harassment or ergonomic computer setup). We then assessed their attitudinal and behavioral preference for working with a female vs. a male partner on a decision-making task. Results: Women, particularly women assigned to a leadership position, more often chose to work with a female partner. Sexual harassment training did not affect women leaders’ attitudes about working with a male partner but those in a helper role expressed more positive attitudes about working with a man after sexual harassment training. These findings document how macro-level processes can shape workplace gender segregation, thus identifying mechanisms underlying the reproduction of gender inequality. Full article

Graph anomaly detection (GAD) is pivotal for security-critical applications like cybersecurity and financial fraud detection. While reconstruction-based Graph Neural Networks (GNNs) are prevalent, their efficacy is often compromised by two phenomena: (1) anomaly overfitting, where expressive models capture anomalous patterns, and (2) homophily-induced attenuation, where message passing smooths localized anomaly cues. This paper proposes CLEAN-GAD, a contamination-aware framework that mitigates anomaly influence during training through multi-view robust learning. Specifically, we develop a contrastive augmentation module that utilizes local inconsistency scores to identify and suppress pseudo-anomalous nodes and edges, thereby yielding a purified augmented view. To capture diverse anomaly signals, a frequency-adaptive encoder with dual-pass channels is designed to integrate low- and high-frequency information. Furthermore, we introduce a distribution-separation regularizer and cross-view alignment to stabilize learning and resolve view shifts. Theoretical analysis confirms that reducing the contamination ratio $\rho$ expands the reconstruction-risk gap between normal and anomalous nodes, inherently boosting detection performance. Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance of CLEAN-GAD. Full article

As social media influencers increasingly shape sustainable consumption, understanding the psychological mechanisms underlying consumer responses is essential. Drawing on social influence theory and reactance theory, this study examines how influencer characteristics affect sustainable behavioral intentions through perceived pressure and consumer reactance, while considering the moderating role of green self-identity. Using survey data from 382 respondents, the proposed model was tested using partial least squares structural equation modeling (PLS-SEM). Given the cross-sectional research design and the reliance on self-reported data, the findings should be interpreted as associational rather than strictly causal. The results show that influencer expertise, homophily, and social influence significantly increase perceived pressure. Perceived pressure, in turn, positively influences consumer reactance, which negatively affects sustainable purchase intention and positively affects avoidance intention. In addition, green self-identity significantly moderates the relationship between perceived pressure and reactance, such that consumers with a stronger green self-identity exhibit heightened sensitivity to perceived pressure and experience stronger reactance responses, indicating heightened sensitivity among environmentally self-identified consumers. These findings extend existing sustainability and influencer marketing research by revealing the dual and potentially counterproductive effects of persuasive communication. The study highlights the importance of autonomy-supportive and identity-consistent messaging for promoting sustainable consumption and provides practical guidance for designing effective influencer-based sustainability strategies. Full article

Behavioral deviations from subjective expected utility theory, most famously captured by the Allais paradox and the Ellsberg paradox, have inspired extensive theoretical and experimental research into risk and ambiguity preferences. While the existing analyze these paradoxes independently, little work explores how such heterogeneously biased agents interact in networked strategic environments. Our paper fills this gap by modeling a convergent Markov–network game between Allais-type and Ellsberg-type players, each endowed with fully enriched loss matrices that reflect their distinct probabilistic and ambiguity attitudes. We define convergent priors as those inducing a spectral radius of <1 in iterated enriched matrices, ensuring iterative convergence under a matrix-based update rule. Players minimize their losses under these priors in each iteration, converging to an equilibrium where no further updates are feasible. We analyze this convergence under three learning regimes—homophily, heterophily, and type-neutral randomness—each defined via distinct neighborhood learning dynamics. To validate the equilibrium, we construct a risk-neutral measure by transforming losses into payoffs and derive a riskless rate of return representing players’ subjective indifference to risk. This applies risk-neutral pricing logic to behavioral matrices, which is novel. This framework unifies paradox-type decision makers within a networked Markovian environment (stochastic adjacency matrix), extending models of dynamic learning and providing a novel equilibrium characterization for heterogeneous, ambiguity-averse agents in structured interactions. Full article

This study investigates how emotionally structured online interaction networks shape consumer perception of new energy vehicle (NEV) technology. Drawing on discussion forum data from two leading NEV brands, Brand_T and Brand_B, we focus on how users respond to brand technological narratives and how these responses translate into distinct patterns of peer-to-peer interaction. Using a behavioral network analysis framework, we integrate sentiment analysis, topic modeling, and Exponential Random Graph Modeling (ERGM) to uncover the psychological and structural mechanisms underlying consumer engagement. Three main findings emerge. First, users display brand-specific emotional-cognitive profiles: Brand_T communities show broader technological engagement but more heterogeneous emotional responses, whereas Brand_B communities exhibit more emotionally aligned discussions. Second, emotional homophily is a robust driver of interaction ties, particularly in Brand_B forums, where positive sentiment clusters into dense and supportive discussion subnetworks. Third, perceived technological benefits, rather than risk sensitivity, are consistently associated with higher interaction intensity, underscoring the motivational salience of anticipated gains over cautionary concerns in shaping engagement behavior. The study contributes to behavioral science and transportation behavior research by linking consumer sentiment, cognition, and social interaction dynamics in digital environments, offering an integrated theoretical account that bridges the Elaboration Likelihood Model, social identity processes, and behavioral network formation. This advances the understanding of technology perception from static individual evaluations to dynamic, group-structured outcomes. It highlights how emotionally patterned interaction networks can reinforce or recalibrate technology-related perceptions, offering practical implications for NEV manufacturers and policymakers seeking to design psychologically informed communication strategies that support sustainable technology adoption. Full article

The occurrence of filter bubbles and echo chambers in social media recommendation systems poses a significant threat to information diversity and democratic discourse. Although graph neural networks (GNNs) achieve leading accuracy in user recommendation, their optimization for engagement metrics inadvertently reinforces homophily, creating isolated information ecosystems. This research developed community-aware two-stage diversification with GNNs (CATD-GNN), a method that leverages the inherent community structure of social networks to promote diversity without sacrificing recommendation quality. CATD-GNN integrates community detection with GNN learning through a two-stage diversification process. The proposed method employs the Louvain method to identify community structures as pseudo-categories, then applies submodular neighbor selection and community-based loss reweighting during GNN training (Stage 1), followed by coverage and redundancy-aware reranking (Stage 2). Twitter data capturing Black Lives Matter discourse and Reddit political discussion networks were used to evaluate the method. CATD-GNN achieves improvements in diversity metrics while maintaining competitive accuracy. The two-stage architecture demonstrates a synergistic effect: the combination of diversity-aware training and coverage-based reranking produces greater improvements than either component alone. The proposed method successfully identifies and recommends users from different communities while preserving recommendation relevance. Full article

Digital well-being refers to the subjective balance between the benefits and drawbacks of technological connectivity. Although it is a relatively recent construct, research has shown that it can be measured reliably. The Digital Well-Being Scale, comprising three dimensions—Digital Satisfaction, Digital Wellness, and Safe and Responsible Behavior—has been validated in other countries, but not yet in Argentina. This study aimed to adapt and validate the scale in the Argentine context and to examine its associations with Fear of Missing Out (FoMO) and identity bubbles, two variables previously linked to digital experiences. A total of 895 participants (55.2% women; aged 18–65) completed an online survey including the Digital Well-Being Scale, the FoMO Scale, and the Identity Bubble Reinforcement Scale (IBRS-9). Exploratory and confirmatory factor analyses supported the original three-factor structure, and all dimensions showed an adequate internal consistency. A significant negative correlation was found between FoMO and the Digital Wellness dimension, suggesting that individuals with higher FoMO experience lower emotional balance in their digital lives. In contrast, associations between identity bubble dimensions and digital well-being were modest and selective. Only Digital Satisfaction and Digital Wellness were weakly related to social identification and homophily; no relationship was observed with safe digital behavior. These findings support the adapted scale’s psychometric soundness in the Argentine context and provide initial insights into how FoMO and digital identity processes may influence digital well-being. Further research is needed to explore these relationships in more diverse populations and cultural contexts. Full article

Social media has become the core channel through which people communicate, and the important role of influencer marketing in creating a fan base for brands is widely recognized. Grounded in Source Credibility, Homophily Theory and Signaling Theory, the purpose of this study is to investigate how influencer efficacy affects the fan effect of green food brands under digital social media. This paper adopts a quantitative research method. A cross-sectional survey was conducted on the Wenjuanxing platform and collected 417 valid responses from consumers who had previously purchased green food based on an influencer’s recommendation. A conceptual model was tested through the structural equation modelling procedure. The results showed that professionalism (β = 0.166, p = 0.011), trustworthiness (β = 0.291, p < 0.001), and similarity (β = 0.267, p < 0.001) had positive effects on perceived quality. Furthermore, perceived quality (β = 0.333, p < 0.001) significantly promoted the formation of the brand fan effect and partially mediated the effects of these characteristics of influencers on the brand fan effect. This study provides new insight into the fan effect of green food brands and also provides a theoretical basis for green food companies to accurately match their brands with suitable influencers, enhance the brand fan effect, and rationally formulate operational strategies. Full article

Affiliation networks, with their bipartite structure and non-binary features, pose unique challenges due to their complex relationships and diverse node attributes. These challenges differ from those in symmetric one-mode networks. To address them, we propose a generalized logistic affiliation network model. Despite the structural asymmetry, the model incorporates node attributes and includes parameters for actor activeness, event popularity, and symmetric patterns in actor–event interactions. We study the theoretical properties of this model under an asymptotic framework, where the number of actors and events grows to infinity. Using maximum likelihood estimation, we show that the estimators for degree heterogeneity and node homophily converge to multivariate normal distributions under mild conditions. To validate the model and our theory, we conduct experiments on both simulated data and a movie-rating dataset. Full article

We investigate how homophily in adherence to anti-epidemic measures affects the final size of epidemics in social networks. Using a modified SIR model, we divide agents into two behavioral groups—compliant and non-compliant—and introduce transmission probabilities that depend asymmetrically on the behavior of both the infected and susceptible individuals. We simulate epidemic dynamics on two types of synthetic networks with tunable inter-group connection probability: stochastic block models (SBM) and networks with triadic closure (TC) that better capture local clustering. Our main result reveals a counterintuitive effect: under conditions where compliant infected agents significantly reduce transmission, increasing the separation between groups may lead to a higher fraction of infections in the compliant population. This paradoxical outcome emerges only in networks with clustering (TC), not in SBM, suggesting that local network structure plays a crucial role. These findings highlight that increasing group separation does not always confer protection, especially when behavioral traits amplify within-group transmission. Full article

Graph Neural Networks (GNNs) capture complex information in graph-structured data by integrating node features with iterative updates of graph topology. However, they inherently rely on the homophily assumption—that nodes of the same class tend to form edges. In contrast, real-world networks often exhibit heterophilous structures, where edges are frequently formed between nodes of different classes. Consequently, conventional GNNs, which apply uniform smoothing over all nodes, may inadvertently aggregate both task-relevant and task-irrelevant information, leading to suboptimal performance on heterophilous graphs. In this work, we propose TRed-GNN, a novel end-to-end GNN architecture designed to enhance both the performance and robustness of node classification on heterophilous graphs. The proposed approach decomposes the original graph into a task-relevant subgraph and a task-irrelevant subgraph and employs a dual-channel mechanism to independently aggregate features from each topology. To mitigate the interference of task-irrelevant information, we introduce a reverse process mechanism that, without compromising the main task, extracts potentially useful information from the task-irrelevant subgraph while filtering out noise, thereby improving generalization and resilience to perturbations. Theoretical analysis and extensive experiments on multiple real-world datasets demonstrate that TRed-GNN not only achieves superior classification performance compared to existing methods on most benchmarks, but also exhibits strong adaptability and stability under graph structural perturbations and over-smoothing scenarios. Full article

The proliferation of social network data has unlocked unprecedented opportunities for extensive, data-driven exploration of human behavior. The structural intricacies of social networks offer insights into various computational social science issues, particularly concerning social influence and information diffusion. However, modeling large-scale social network data comes with computational challenges. Though large language models make it easier than ever to model textual content, any advanced network representation method struggles with scalability and efficient deployment to out-of-sample users. In response, we introduce a novel approach tailored for modeling social network data in user-detection tasks. This innovative method integrates localized social network interactions with the capabilities of large language models. Operating under the premise of social network homophily, which posits that socially connected users share similarities, our approach is designed with scalability and inductive capabilities in mind, avoiding the need for full-graph training. We conduct a thorough evaluation of our method across seven real-world social network datasets, spanning a diverse range of topics and detection tasks, showcasing its applicability to advance research in computational social science. Full article

Based on questionnaire data and in-depth interviews with newly arrived students (NAS) from mainland China, this study investigates the construction of their social networks and the mechanisms through which they access social support in the context of migration. Drawing on Berry’s acculturation theory, Bronfenbrenner’s ecological systems theory, and Bourdieu’s concept of social capital, this study provides a theoretically grounded analysis of how NAS balance cultural distance and social needs. The findings reveal that NAS do not form social connections uniformly; rather, they strategically allocate social resources according to the degree of homophily and the strength of social ties. Specifically, weak ties with mainland peers—characterized by high cultural homophily—primarily offer emotional support; strong ties with local Hong Kong peers—marked by low homophily but high interaction frequency—mainly serve instrumental needs such as academic assistance and daily companionship; while strong ties with Hong Kong peers of mainland background combine both emotional and instrumental support, functioning as a core relational bridge in the NAS’s adaptation process. These three types of relationships form a complementary structure within NAS’s social networks. Reliability and validity tests further confirmed that four items (social satisfaction, peer attitude, sense of belonging, integration/adaptation) provide a coherent measure of social integration. The study suggests that NAS’s social practices are not merely about “integration” or “alienation,” but rather represent a dynamic strategy of balancing relational costs, cultural distance, and practical needs in the operation of social capital and characterised by dynamic negotiation and contextual adjustment. Full article