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- [1] arXiv:2506.04271 [中文pdf, pdf, html, 其他]
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标题： ExDiff：一种集成可解释人工智能的复杂网络上扩散过程仿真框架标题： ExDiff: A Framework for Simulating Diffusion Processes on Complex Networks with Explainable AI Integration主题： 社会与信息网络 (cs.SI)
理解和控制复杂网络中的扩散过程在从流行病学到信息科学等多个领域都至关重要。 在这里，我们提出了ExDiff，这是一个交互式且模块化的计算框架，它结合了网络模拟、图神经网络（GNN）和可解释人工智能（XAI），用于建模和解读扩散动力学。 ExDiff将经典的分室模型与深度学习技术相结合，以捕捉不同网络拓扑结构下扩散的结构和时间特性。 该框架具有专门用于网络分析、神经建模、模拟和可解释性的模块，所有这些都可以通过一个直观的界面访问，该界面基于Google Colab构建。 通过Susceptible Infectious Recovered Vaccinated Dead (SIRVD) 模型的案例研究，我们展示了能够模拟疾病传播、评估干预策略、分类节点状态，并通过XAI技术揭示传染的结构决定因素的能力。 通过统一模拟和可解释性， ExDiff提供了一个强大、灵活且易于使用的平台，用于研究网络化系统中的扩散现象，促进了方法论创新和实用见解。
Understanding and controlling diffusion processes in complex networks is critical across domains ranging from epidemiology to information science. Here, we present ExDiff, an interactive and modular computational framework that integrates network simulation, graph neural networks (GNNs), and explainable artificial intelligence (XAI) to model and interpret diffusion dynamics. ExDiff combines classical compartmental models with deep learning techniques to capture both the structural and temporal characteristics of diffusion across diverse network topologies. The framework features dedicated modules for network analysis, neural modeling, simulation, and interpretability, all accessible via an intuitive interface built on Google Colab. Through a case study of the Susceptible Infectious Recovered Vaccinated Dead (SIRVD) model, we demonstrate the capacity to simulate disease spread, evaluate intervention strategies, classify node states, and reveal the structural determinants of contagion through XAI techniques. By unifying simulation and interpretability, ExDiff provides a powerful, flexible, and accessible platform for studying diffusion phenomena in networked systems, enabling both methodological innovation and practical insight.
- [2] arXiv:2506.04292 [中文pdf, pdf, html, 其他]
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标题： 针对Smurfing的GARG-AML：一种可扩展且可解释的基于图的反洗钱框架标题： GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering主题： 社会与信息网络 (cs.SI) ; 机器学习 (cs.LG) ; 应用 (stat.AP)
洗钱行为构成重大挑战，据估计其规模占全球GDP的2%-5%。 这迫使监管机构对金融机构实施严格的管控措施。 其中一种规避这些管控的典型洗钱方法被称为“拆分交易”（smurfing），即把大额交易分解为多个小额交易。 鉴于拆分交易计划的复杂性，涉及多个分布在不同方之间的交易，网络分析已成为反洗钱的重要工具。 然而，近期的研究进展主要集中在黑盒网络嵌入方法上，这阻碍了它们在商业中的应用。 本文介绍了一种名为GARG-AML的新图基方法，该方法通过从网络中每个节点的二阶交易网络结构派生出一个可解释的指标来量化拆分交易风险。 与传统方法不同， GARG-AML在计算效率、检测能力和透明度之间取得了有效的平衡，这使其能够集成到现有的反洗钱工作流程中。 为了增强其能力，我们将GARG-AML评分计算与不同的基于树的方法结合，并且还纳入了节点邻居的得分。 在大规模合成和开源网络上的实验评估表明，GARG-AML优于当前最先进的拆分交易检测方法。 通过利用二阶邻域的邻接矩阵和基本网络特征，这项工作突显了基础网络属性在推进欺诈检测方面的潜力。
Money laundering poses a significant challenge as it is estimated to account for 2%-5% of the global GDP. This has compelled regulators to impose stringent controls on financial institutions. One prominent laundering method for evading these controls, called smurfing, involves breaking up large transactions into smaller amounts. Given the complexity of smurfing schemes, which involve multiple transactions distributed among diverse parties, network analytics has become an important anti-money laundering tool. However, recent advances have focused predominantly on black-box network embedding methods, which has hindered their adoption in businesses. In this paper, we introduce GARG-AML, a novel graph-based method that quantifies smurfing risk through a single interpretable metric derived from the structure of the second-order transaction network of each individual node in the network. Unlike traditional methods, GARG-AML strikes an effective balance among computational efficiency, detection power and transparency, which enables its integration into existing AML workflows. To enhance its capabilities, we combine the GARG-AML score calculation with different tree-based methods and also incorporate the scores of the node's neighbours. An experimental evaluation on large-scale synthetic and open-source networks demonstrate that the GARG-AML outperforms the current state-of-the-art smurfing detection methods. By leveraging only the adjacency matrix of the second-order neighbourhood and basic network features, this work highlights the potential of fundamental network properties towards advancing fraud detection.
- [3] arXiv:2506.04475 [中文pdf, pdf, html, 其他]
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标题： 什么驱动了团队的成功？ 关于“团队成员效应”作用的大规模证据标题： What Drives Team Success? Large-Scale Evidence on the Role of the Team Player Effect评论： 20页，7个图，5个表格。已被2025年ACM经济与计算会议（EC '25）接受为报告；仅一页扩展摘要将出现在EC '25论文集中。主题： 社会与信息网络 (cs.SI) ; 计算机与社会 (cs.CY)
在从专业组织到高风险竞争环境的结构化、以绩效为导向的环境中，有效的团队合作至关重要。 随着任务变得更加复杂，实现高水平的表现不仅需要技术娴熟，还需要强大的人际交往能力，使个人能够在团队中有效协调。 尽管先前的研究已经确定了社交技能和熟悉度是团队成功的关键驱动因素，但由于数据和方法论的限制，它们的联合效应——特别是在临时团队中的效应——仍未得到充分探索。 为了解决这一差距，我们分析了来自实时战略游戏《帝国时代II》的大规模面板数据集，在游戏中玩家被随机分配到临时团队中，并必须在动态高压条件下进行协调。 我们通过比较观察到的比赛结果与基于任务熟练度的预测结果来隔离个体贡献。 我们的研究结果证实了一个强有力的“团队成员效应”：某些个体始终能够超越其技术技能所预测的表现，显著改善团队的结果。 这种效应因团队熟悉度而显著增强——具有先前共同经验的团队从这些个体的存在中受益更多。 此外，该效应随着团队规模的增长而增加，表明随着协调需求的上升，社交技能变得越来越有价值。 我们的结果表明，社交技能和熟悉度以互补而非叠加的方式相互作用。 这些发现通过在一个准随机、高风险的设置中记录团队成员效应的强度和结构，为团队绩效文献做出了贡献，这对组织和劳动力市场的团队合作具有启示意义。
Effective teamwork is essential in structured, performance-driven environments, from professional organizations to high-stakes competitive settings. As tasks grow more complex, achieving high performance requires not only technical proficiency but also strong interpersonal skills that allow individuals to coordinate effectively within teams. While prior research has identified social skills and familiarity as key drivers of team success, their joint effects -- particularly in temporary teams -- remain underexplored due to data and methodological constraints. To address this gap, we analyze a large-scale panel dataset from the real-time strategy game Age of Empires II, where players are assigned quasi-randomly to temporary teams and must coordinate under dynamic, high-pressure conditions. We isolate individual contributions by comparing observed match outcomes with predictions based on task proficiency. Our findings confirm a robust 'team player effect': certain individuals consistently improve team outcomes beyond what their technical skills predict. This effect is significantly amplified by team familiarity -- teams with prior shared experience benefit more from the presence of such individuals. Moreover, the effect grows with team size, suggesting that social skills become increasingly valuable as coordination demands rise. Our results demonstrate that social skills and familiarity interact in a complementary, rather than additive, way. These findings contribute to the literature on team performance by documenting the strength and structure of the team player effect in a quasi-randomized, high-stakes setting, with implications for teamwork in organizations and labor markets.
新提交 (展示 3 之 3 条目 )
- [4] arXiv:2506.04435 (交叉列表自 physics.soc-ph) [中文pdf, pdf, html, 其他]
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标题： 边缘干预可以减轻计算机科学合著网络中的人口和声望差异标题： Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship networkKate Barnes, Mia Ellis-Einhorn, Carolina Chávez-Ruelas, Nayera Hasan, Mohammad Fanous, Blair D. Sullivan, Sorelle Friedler, Aaron Clauset评论： 14页，6个图，2个附录（5个图，6个表格）主题： 物理与社会 (physics.soc-ph) ; 计算机与社会 (cs.CY) ; 社会与信息网络 (cs.SI)
社会因素（如人口特征和机构声望结构）影响着学术出版中的思想创造与传播。这些影响的一个观察点在于研究人员在合著网络中的中心性或边缘性。 我们调查了一组手收集的数据集，其中包括5670名在美国博士授予计算机科学系工作的教职员工及其DBLP合著关系，研究了网络中心性的不平等现象。 我们引入了算法，通过最大化与我们普查中教师调查的自我报告人口统计数据的一致性来结合基于姓名和感知的人口统计标签。 我们发现女性和少数族裔身份的个体在计算机科学合著网络中处于较不中心的位置，这表明他们获取信息和传播信息的能力较差。 中心性也高度相关于声望，因此顶级部门的教师位于计算机科学合著网络的核心，而低排名部门的教师则位于外围。 我们展示了这些差异可以通过模拟边干预来缓解，这些干预可以被解释为促进的合作。 我们的干预通过将目标个体（独立于网络结构选择）与来自高排名机构的研究人员联系起来，提高了他们的中心性。 当应用于博士生时，该干预还提高了他们在学术就业市场中所在机构的预测排名。 这项工作遵循了一种改善方法：揭示社会不平等以解决它们。 通过根据机构声望针对学者进行干预，我们可以提高他们在合著网络中的中心性，这对工作安置和长期学术成功起着关键作用。
Social factors such as demographic traits and institutional prestige structure the creation and dissemination of ideas in academic publishing. One place these effects can be observed is in how central or peripheral a researcher is in the coauthorship network. Here we investigate inequities in network centrality in a hand-collected data set of 5,670 U.S.-based faculty employed in Ph.D.-granting Computer Science departments and their DBLP coauthorship connections. We introduce algorithms for combining name- and perception-based demographic labels by maximizing alignment with self-reported demographics from a survey of faculty from our census. We find that women and individuals with minoritized race identities are less central in the computer science coauthorship network, implying worse access to and ability to spread information. Centrality is also highly correlated with prestige, such that faculty in top-ranked departments are at the core and those in low-ranked departments are in the peripheries of the computer science coauthorship network. We show that these disparities can be mitigated using simulated edge interventions, interpreted as facilitated collaborations. Our intervention increases the centrality of target individuals, chosen independently of the network structure, by linking them with researchers from highly ranked institutions. When applied to scholars during their Ph.D., the intervention also improves the predicted rank of their placement institution in the academic job market. This work was guided by an ameliorative approach: uncovering social inequities in order to address them. By targeting scholars for intervention based on institutional prestige, we are able to improve their centrality in the coauthorship network that plays a key role in job placement and longer-term academic success.
- [5] arXiv:2506.04525 (交叉列表自 cs.GT) [中文pdf, pdf, html, 其他]
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标题： 推荐系统中的用户利他主义标题： User Altruism in Recommendation Systems主题： 计算机科学与博弈论 (cs.GT) ; 计算机与社会 (cs.CY) ; 人机交互 (cs.HC) ; 社会与信息网络 (cs.SI)
基于推荐系统（RecSys）的社交媒体平台用户（例如 TikTok、X、YouTube）战略性地与平台内容互动，以影响未来的推荐。在某些此类平台上，有记录显示用户会形成大规模草根运动，鼓励他人故意与算法压制的内容互动，以“提升”其推荐；我们将这种行为称为用户利他主义。 为了捕捉这种行为，我们研究了用户与 RecSys 之间的博弈，其中用户向 RecSys 提供对其可用内容的（可能被操纵的）偏好，而 RecSys 在数据和计算限制下构建一个低秩近似偏好矩阵，并最终为每位用户提供她（近似）最偏好的项目。 我们比较了用户在真实偏好报告和一类捕捉用户利他主义策略下的社会福利。在我们的理论分析中，我们提供了确保用户利他主义下社会福利严格增加的充分条件，并提供了一种找到有效利他策略的算法。 有趣的是，我们表明，对于通常假设的推荐器效用函数，有效的利他策略也会提高 RecSys 的效用！ 我们展示了我们的结果对多种模型误设具有鲁棒性，从而加强了我们的结论。 我们的理论分析得到了 GoodReads 数据集上有效利他策略的经验结果以及关于现实世界用户如何在 RecSys 中表现出利他主义的在线调查的补充。 总体而言，我们的发现证明了传统 RecSys 可能激励用户形成集体和/或遵循利他策略的原因。
Users of social media platforms based on recommendation systems (RecSys) (e.g. TikTok, X, YouTube) strategically interact with platform content to influence future recommendations. On some such platforms, users have been documented to form large-scale grassroots movements encouraging others to purposefully interact with algorithmically suppressed content in order to "boost" its recommendation; we term this behavior user altruism. To capture this behavior, we study a game between users and a RecSys, where users provide the RecSys (potentially manipulated) preferences over the contents available to them, and the RecSys -- limited by data and computation constraints -- creates a low-rank approximation preference matrix, and ultimately provides each user her (approximately) most-preferred item. We compare the users' social welfare under truthful preference reporting and under a class of strategies capturing user altruism. In our theoretical analysis, we provide sufficient conditions to ensure strict increases in user social welfare under user altruism, and provide an algorithm to find an effective altruistic strategy. Interestingly, we show that for commonly assumed recommender utility functions, effectively altruistic strategies also improve the utility of the RecSys! We show that our results are robust to several model misspecifications, thus strengthening our conclusions. Our theoretical analysis is complemented by empirical results of effective altruistic strategies on the GoodReads dataset, and an online survey on how real-world users behave altruistically in RecSys. Overall, our findings serve as a proof-of-concept of the reasons why traditional RecSys may incentivize users to form collectives and/or follow altruistic strategies when interacting with them.
- [6] arXiv:2506.04701 (交叉列表自 physics.soc-ph) [中文pdf, pdf, html, 其他]
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标题： 基于分数阶方法的内存驱动的有界置信意见动力学：一种基于赫格塞曼-克拉瑟模型的分析标题： Memory-Driven Bounded Confidence Opinion Dynamics: A Hegselmann-Krause Model Based on Fractional-Order Methods主题： 物理与社会 (physics.soc-ph) ; 多智能体系统 (cs.MA) ; 社会与信息网络 (cs.SI) ; 适应性与自组织系统 (nlin.AO)
记忆效应在社会互动和决策过程中起着至关重要的作用。 本文提出了一种新的分数阶有界置信意见动力学模型，以表征系统状态中的记忆效应。 基于赫格尔曼-克拉默框架和分数阶差分，建立了全面的模型，捕捉了历史信息的持续影响。 通过严格的理论分析，研究了包括收敛性和共识性在内的基本特性。 结果表明，与经典的意见动力学相比，所提出的模型不仅保持了良好的收敛性和共识特性，还解决了诸如有界意见单调性等局限性。 这使得对现实场景中意见演化的更真实表示成为可能。 本研究的发现为理解意见形成和演化提供了新的见解和方法论途径，具有理论意义和实际应用价值。
Memory effects play a crucial role in social interactions and decision-making processes. This paper proposes a novel fractional-order bounded confidence opinion dynamics model to characterize the memory effects in system states. Building upon the Hegselmann-Krause framework and fractional-order difference, a comprehensive model is established that captures the persistent influence of historical information. Through rigorous theoretical analysis, the fundamental properties including convergence and consensus is investigated. The results demonstrate that the proposed model not only maintains favorable convergence and consensus characteristics compared to classical opinion dynamics, but also addresses limitations such as the monotonicity of bounded opinions. This enables a more realistic representation of opinion evolution in real-world scenarios. The findings of this study provide new insights and methodological approaches for understanding opinion formation and evolution, offering both theoretical significance and practical applications.
交叉提交 (展示 3 之 3 条目 )
- [7] arXiv:2501.07327 (替换) [中文pdf, pdf, html, 其他]
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标题： 社区感知的时间网络生成标题： Community Aware Temporal Network GenerationNicolò Alessandro Girardini, Antonio Longa, Gaia Trebucchi, Giulia Cencetti, Andrea Passerini, Bruno Lepri主题： 社会与信息网络 (cs.SI) ; 物理与社会 (physics.soc-ph)
时序网络在捕捉复杂动力学（例如扩散和传染）方面的优势，推动了众多领域现实系统中的突破性进展。 对于人类行为而言，面对面交互网络使我们能够理解社区如何通过交互而随时间形成和演变的动态过程，这在流行病学、社会学研究以及城市科学等领域至关重要。 然而，现有的先进数据集存在诸多不足之处，例如数据收集的时间跨度较短以及参与者数量较少。 此外，还存在参与者隐私保护和数据收集成本等问题。 多年来，许多针对静态网络生成的算法被提出，但它们往往无法处理交互的社会结构及其时间特性。 在这项工作中，我们将最近的一种网络生成方法扩展到捕获不同社区之间交互的演化过程。 我们的方法根据节点所属的社区对其进行标记，并构建反映原始时序网络中具有不同标签节点之间交互的代理网络。 这使得生成能够复制现实行为的合成网络成为可能。 我们通过比较多个面对面交互数据集中原始网络与生成网络之间的结构度量来验证我们的方法。
The advantages of temporal networks in capturing complex dynamics, such as diffusion and contagion, has led to breakthroughs in real world systems across numerous fields. In the case of human behavior, face-to-face interaction networks enable us to understand the dynamics of how communities emerge and evolve in time through the interactions, which is crucial in fields like epidemics, sociological studies and urban science. However, state-of-the-art datasets suffer from a number of drawbacks, such as short time-span for data collection and a small number of participants. Moreover, concerns arise for the participants' privacy and the data collection costs. Over the past years, many successful algorithms for static networks generation have been proposed, but they often do not tackle the social structure of interactions or their temporal aspect. In this work, we extend a recent network generation approach to capture the evolution of interactions between different communities. Our method labels nodes based on their community affiliation and constructs surrogate networks that reflect the interactions of the original temporal networks between nodes with different labels. This enables the generation of synthetic networks that replicate realistic behaviors. We validate our approach by comparing structural measures between the original and generated networks across multiple face-to-face interaction datasets.
- [8] arXiv:2506.01642 (替换) [中文pdf, pdf, html, 其他]
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标题： 接住 stray 球：足球、球迷与数字话语的影响标题： Catching Stray Balls: Football, fandom, and the impact on digital discourse主题： 社会与信息网络 (cs.SI) ; 计算机与社会 (cs.CY)
本文探讨了对足球比赛的情绪反应如何影响Reddit上各个数字空间中的在线 discourse。 通过分析数十个子版块的数百万帖子，它展示了现实世界事件会触发情绪转变，并在社区之间传播。 研究显示负面情绪与问题性语言相关；比赛结果直接影响情绪和发帖习惯；情绪可以在无关社区间转移；并提供了关于这种变化情绪 discourse 内容的见解。 这些发现揭示了数字空间并非孤立的环境，而是相互连接的情感生态系统，容易受到由现实世界事件引发的跨领域传播的影响，有助于我们理解在线毒性的传播。 虽然足球被用作案例研究以计算测量情感原因和运动，但这些模式对于理解广泛意义上的在线社区具有重要意义。
This paper examines how emotional responses to football matches influence online discourse across digital spaces on Reddit. By analysing millions of posts from dozens of subreddits, it demonstrates that real-world events trigger sentiment shifts that move across communities. It shows that negative sentiment correlates with problematic language; match outcomes directly influence sentiment and posting habits; sentiment can transfer to unrelated communities; and offers insights into the content of this shifting discourse. These findings reveal how digital spaces function not as isolated environments, but as interconnected emotional ecosystems vulnerable to cross-domain contagion triggered by real-world events, contributing to our understanding of the propagation of online toxicity. While football is used as a case-study to computationally measure affective causes and movements, these patterns have implications for understanding online communities broadly.
- [9] arXiv:2409.19257 (替换) [中文pdf, pdf, html, 其他]
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标题： 诱导社会时间上下文中的内群体语言词典标题： Inducing lexicons of in-group language with socio-temporal context评论： 被ACL 2025接受主题： 计算与语言 (cs.CL) ; 社会与信息网络 (cs.SI)
内群体语言是群体动态的一个重要标志。 本文提出了一种新颖的方法来诱导内群体语言的词汇表，该方法结合了其社会-时间背景。 现有的词汇表诱导方法无法捕捉内群体语言的演变特性，也无法捕捉社区的社会结构。 利用在在线反女性群体对话中训练的动态词语和用户嵌入，我们的方法优于先前的词汇表诱导方法。 我们为词汇表诱导任务开发了一个测试集和一个新的男性主义语言词汇表，由人类专家验证，量化了每个术语在特定时刻对特定子群体的相关性。 最后，我们展示了关于内群体语言的新见解，这些见解展示了这种方法的实用性。
In-group language is an important signifier of group dynamics. This paper proposes a novel method for inducing lexicons of in-group language, which incorporates its socio-temporal context. Existing methods for lexicon induction do not capture the evolving nature of in-group language, nor the social structure of the community. Using dynamic word and user embeddings trained on conversations from online anti-women communities, our approach outperforms prior methods for lexicon induction. We develop a test set for the task of lexicon induction and a new lexicon of manosphere language, validated by human experts, which quantifies the relevance of each term to a specific sub-community at a given point in time. Finally, we present novel insights on in-group language which illustrate the utility of this approach.