Computational Social Networks最新文献

英文中文

The structure of co-publications multilayer network 合作出版物多层网络的结构

Q1 Mathematics

Computational Social Networks

Pub Date : 2021-02-09 DOI: 10.1186/s40649-021-00089-w

Ghislain Romaric Meleu, Paulin Yonta Melatagia

Using the headers of scientific papers, we have built multilayer networks of entities involved in research namely: authors, laboratories, and institutions. We have analyzed some properties of such networks built from data extracted from the HAL archives and found that the network at each layer is a small-world network with power law distribution. In order to simulate such co-publication network, we propose a multilayer network generation model based on the formation of cliques at each layer and the affiliation of each new node to the higher layers. The clique is built from new and existing nodes selected using preferential attachment. We also show that, the degree distribution of generated layers follows a power law. From the simulations of our model, we show that the generated multilayer networks reproduce the studied properties of co-publication networks.

利用科学论文的标题，我们建立了涉及研究的实体的多层网络，即:作者、实验室和机构。我们分析了从HAL档案中提取的数据构建的网络的一些特性，发现每一层的网络都是一个幂律分布的小世界网络。为了模拟这种共同出版网络，我们提出了一种多层网络生成模型，该模型基于每一层的派系形成以及每个新节点与更高层的隶属关系。该集团是由使用优先连接选择的新节点和现有节点构建的。我们还表明，生成层的度分布遵循幂律。从我们模型的模拟中，我们表明生成的多层网络再现了共同出版网络的研究特性。

引用次数: 0

Influence network design via multi-level optimization considering boundedly rational user behaviours in social media networks 考虑社交媒体网络中有限理性用户行为，通过多层次优化影响网络设计

Q1 Mathematics

Computational Social Networks

Pub Date : 2021-02-08 DOI: 10.1186/s40649-020-00082-9

Guanxiang Yun, Q. Zheng, V. Boginski, E. Pasiliao

引用次数: 2

Discovering the maximum k-clique on social networks using bat optimization algorithm 利用蝙蝠优化算法发现社交网络上的最大k-clique

Q1 Mathematics

Computational Social Networks

Pub Date : 2021-02-02 DOI: 10.1186/s40649-021-00087-y

Akram Khodadadi, Shahram Saeidi

The k-clique problem is identifying the largest complete subgraph of size k on a network, and it has many applications in Social Network Analysis (SNA), coding theory, geometry, etc. Due to the NP-Complete nature of the problem, the meta-heuristic approaches have raised the interest of the researchers and some algorithms are developed. In this paper, a new algorithm based on the Bat optimization approach is developed for finding the maximum k-clique on a social network to increase the convergence speed and evaluation criteria such as Precision, Recall, and F1-score. The proposed algorithm is simulated in Matlab® software over Dolphin social network and DIMACS dataset for k = 3, 4, 5. The computational results show that the convergence speed on the former dataset is increased in comparison with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) approaches. Besides, the evaluation criteria are also modified on the latter dataset and the F1-score is obtained as 100% for k = 5.

k-团问题是识别网络上大小为k的最大完整子图的问题，它在社会网络分析(SNA)、编码理论、几何等领域有许多应用。由于问题的np完全性质，元启发式方法引起了研究人员的兴趣，并开发了一些算法。本文提出了一种基于Bat优化方法的新算法，用于寻找社交网络上的最大k-clique，以提高收敛速度和精度、召回率和f1分数等评价标准。该算法在Matlab®软件中对Dolphin社交网络和DIMACS数据集进行了仿真，k = 3,4,5。计算结果表明，与遗传算法(GA)和蚁群优化(ACO)方法相比，前者在数据集上的收敛速度有所提高。此外，对后一个数据集的评价标准也进行了修改，当k = 5时，f1得分为100%。

引用次数: 0

Understanding social media beyond text: a reliable practice on Twitter 理解文本之外的社交媒体：推特上的可靠实践

Q1 Mathematics

Computational Social Networks

Pub Date : 2021-01-30 DOI: 10.1186/s40649-021-00088-x

Qixuan Hou, Meng Han, Feiyang Qu, J. He

引用次数: 3

A hybrid metaheuristic for solving asymmetric distance-constrained vehicle routing problem 求解非对称距离约束车辆路径问题的混合元启发式算法

Q1 Mathematics

Computational Social Networks

Pub Date : 2021-01-22 DOI: 10.1186/s40649-020-00084-7

H. Ban, P. Nguyen

引用次数: 2

Non-submodular model for group profit maximization problem in social networks 社会网络群体利益最大化问题的非子模块模型

Q1 Mathematics

Computational Social Networks

Pub Date : 2021-01-07 DOI: 10.1186/s40649-020-00085-6

Jianming Zhu, Smita Ghosh, Weili Wu, Chuangen Gao

In social networks, there exist many kinds of groups in which people may have the same interests, hobbies, or political orientation. Sometimes, group decisions are made by simply majority, which means that most of the users in this group reach an agreement, such as US Presidential Elections. A group is called activated if $$beta$$ percent of users are influenced in the group. Enterprise will gain income from all influenced groups. Simultaneously, to propagate influence, enterprise needs pay advertisement diffusion cost. Group profit maximization (GPM) problem aims to pick k seeds to maximize the expected profit that considers the benefit of influenced groups with the diffusion cost. GPM is proved to be NP-hard and the objective function is proved to be neither submodular nor supermodular. An upper bound and a lower bound which are difference of two submodular functions are designed. We propose a submodular–modular algorithm (SMA) to solve the difference of two submodular functions and SMA is shown to converge to a local optimal. We present an randomized algorithm based on weighted group coverage maximization for GPM and apply sandwich framework to get theoretical results. Our experiments verify the efficiency of our methods.

在社交网络中，存在着各种各样的群体，其中的人们可能有相同的兴趣、爱好或政治取向。有时，群体决策是由简单的多数人做出的，这意味着这个群体中的大多数用户达成了一致，比如美国总统选举。如果群组中有$$beta$$百分比的用户受到影响，则称为激活群组。企业将从所有受影响的群体中获得收益。同时，为了传播影响力，企业需要支付广告扩散成本。群体利润最大化(GPM)问题的目标是在考虑受影响群体的利益和扩散成本的情况下，选取k粒种子，使期望利润最大化。证明了GPM是np困难的，并且证明了目标函数既不是子模也不是超模。设计了两个子模函数之差的上界和下界。我们提出了一种求解两个子模函数之差的子模-模算法(SMA)，并证明SMA收敛于局部最优。提出了一种基于加权群覆盖最大化的随机化GPM算法，并应用三明治框架得到了理论结果。我们的实验验证了我们方法的有效性。

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引用次数: 0

A review: preprocessing techniques and data augmentation for sentiment analysis 综述:情感分析的预处理技术和数据增强

Q1 Mathematics

Computational Social Networks

Pub Date : 2021-01-06 DOI: 10.1186/s40649-020-00080-x

Huu-Thanh Duong, Tram-Anh Nguyen-Thi

In literature, the machine learning-based studies of sentiment analysis are usually supervised learning which must have pre-labeled datasets to be large enough in certain domains. Obviously, this task is tedious, expensive and time-consuming to build, and hard to handle unseen data. This paper has approached semi-supervised learning for Vietnamese sentiment analysis which has limited datasets. We have summarized many preprocessing techniques which were performed to clean and normalize data, negation handling, intensification handling to improve the performances. Moreover, data augmentation techniques, which generate new data from the original data to enrich training data without user intervention, have also been presented. In experiments, we have performed various aspects and obtained competitive results which may motivate the next propositions.

在文献中，基于机器学习的情感分析研究通常是监督学习，在某些领域必须有足够大的预标记数据集。显然，这个任务冗长、昂贵且耗时，而且很难处理看不见的数据。本文探讨了数据集有限的越南语情感分析的半监督学习。本文总结了用于数据清理和归一化、否定处理、强化处理等方面的预处理技术。此外，还提出了数据增强技术，即在不需要用户干预的情况下从原始数据中生成新数据以丰富训练数据。在实验中，我们已经完成了各个方面，并获得了有竞争力的结果，这可能会激发下一个命题。

引用次数: 43

A model for the co-evolution of dynamic social networks and infectious disease dynamics. 动态社会网络和传染病动态共同进化模型。

Q1 Mathematics

Computational Social Networks

Pub Date : 2021-01-01 Epub Date: 2021-10-07 DOI: 10.1186/s40649-021-00098-9

Hendrik Nunner, Vincent Buskens, Mirjam Kretzschmar

Recent research shows an increasing interest in the interplay of social networks and infectious diseases. Many studies either neglect explicit changes in health behavior or consider networks to be static, despite empirical evidence that people seek to distance themselves from diseases in social networks. We propose an adaptable steppingstone model that integrates theories of social network formation from sociology, risk perception from health psychology, and infectious diseases from epidemiology. We argue that networking behavior in the context of infectious diseases can be described as a trade-off between the benefits, efforts, and potential harm a connection creates. Agent-based simulations of a specific model case show that: (i) high (perceived) health risks create strong social distancing, thus resulting in low epidemic sizes; (ii) small changes in health behavior can be decisive for whether the outbreak of a disease turns into an epidemic or not; (iii) high benefits for social connections create more ties per agent, providing large numbers of potential transmission routes and opportunities for the disease to travel faster, and (iv) higher costs of maintaining ties with infected others reduce final size of epidemics only when benefits of indirect ties are relatively low. These findings suggest a complex interplay between social network, health behavior, and infectious disease dynamics. Furthermore, they contribute to solving the issue that neglect of explicit health behavior in models of disease spread may create mismatches between observed transmissibility and epidemic sizes of model predictions.

最近的研究表明，人们越来越关注社会网络与传染病之间的相互作用。许多研究要么忽视了健康行为的明显变化，要么认为网络是静态的，尽管有经验证据表明，人们在社交网络中寻求与疾病保持距离。我们提出了一个可调整的阶梯模型，该模型整合了社会学中关于社会网络形成的理论、健康心理学中关于风险认知的理论以及流行病学中关于传染病的理论。我们认为，传染病背景下的网络行为可以被描述为一种在利益、努力和潜在危害之间的权衡。对一个特定模型案例的基于代理的模拟表明(i) 高（感知到的）健康风险会造成强烈的社会疏远，从而导致较低的流行病规模；(ii) 健康行为的微小变化会对疾病爆发是否演变成流行病起决定性作用；(iii) 社会关系的高收益会给每个人带来更多的联系，从而提供大量潜在的传播途径和机会，使疾病传播得更快；(iv) 只有当间接联系的收益相对较低时，与受感染者保持联系的较高成本才会降低流行病的最终规模。这些发现表明，社会网络、健康行为和传染病动态之间存在复杂的相互作用。此外，这些发现有助于解决在疾病传播模型中忽视明确的健康行为可能会造成观察到的传播性与模型预测的流行病规模不匹配的问题。

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引用次数: 0

Modeling the transmission dynamics of racism propagation with community resilience. 建立具有社区复原力的种族主义传播动态模型。

Q1 Mathematics

Computational Social Networks

Pub Date : 2021-01-01 Epub Date: 2021-11-06 DOI: 10.1186/s40649-021-00102-2

Dejen Ketema Mamo

Racism spreading can have a vital influence on people's lives, declining adherence, pretending political views, and recruiters' socio-economical crisis. Besides, Web 2.0 technologies have democratized the creation and propagation of racist information, which facilitated the rapid spreading of racist messages. In this research work, the impact of community resilience on the spread dynamics of racism was assessed. To investigate the effect of resilience-building, new SERDC mathematical model was formulated and analyzed. The racism spread is under control where $R_{0} < 1$ , whereas persist in the community whenever $R_{0} > 1$ . Sensitivity analysis of the parameters value of the model are conducted. The rising of transmission and racial extremeness rate provides the prevalence of racism spread. Effective community resilience decline the damages, mitigate, and eradicate racism propagation. Theoretical analysis of the model are backed up by numerical results. Despite the evidence of numerical simulations, reducing the transmission and racial extremeness rate by improving social bonds and solidarity through community resilience could control the spread of racism.

种族主义的传播会对人们的生活、依附性的下降、政治观点的伪装以及招募者的社会经济危机产生至关重要的影响。此外，Web 2.0 技术使种族主义信息的创造和传播民主化，这为种族主义信息的快速传播提供了便利。在这项研究工作中，我们评估了社区复原力对种族主义传播动态的影响。为了研究复原力建设的影响，我们建立并分析了新的 SERDC 数学模型。当 R 0 1 时，种族主义的传播受到控制，而当 R 0 > 1 时，种族主义在社区中持续存在。对模型的参数值进行了敏感性分析。传播率和种族极端化率的上升提供了种族主义传播的普遍性。有效的社区复原力可以降低种族主义传播造成的损害，减轻并根除种族主义传播。模型的理论分析得到了数值结果的支持。尽管有数值模拟的证据，但通过社区复原力改善社会纽带和团结来降低传播率和种族极端化率，可以控制种族主义的传播。

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引用次数: 0

Node-weighted centrality: a new way of centrality hybridization 节点加权中心性:一种新的中心性杂交方法

Q1 Mathematics

Computational Social Networks

Pub Date : 2020-11-13 DOI: 10.1186/s40649-020-00081-w

Anuj Singh, Rishi Ranjan Singh, S. R. S. Iyengar

Centrality measures have been proved to be a salient computational science tool for analyzing networks in the last two to three decades aiding many problems in the domain of computer science, economics, physics, and sociology. With increasing complexity and vividness in the network analysis problems, there is a need to modify the existing traditional centrality measures. Weighted centrality measures usually consider weights on the edges and assume the weights on the nodes to be uniform. One of the main reasons for this assumption is the hardness and challenges in mapping the nodes to their corresponding weights. In this paper, we propose a way to overcome this kind of limitation by hybridization of the traditional centrality measures. The hybridization is done by taking one of the centrality measures as a mapping function to generate weights on the nodes and then using the node weights in other centrality measures for better complex ranking.

在过去的二三十年里，中心性度量已经被证明是一个重要的计算科学工具，用于分析网络，帮助解决计算机科学、经济学、物理学和社会学领域的许多问题。随着网络分析问题的复杂性和生动性的增加，有必要对现有的传统中心性度量进行修正。加权中心性度量通常考虑边缘上的权值，并假设节点上的权值是均匀的。这种假设的主要原因之一是将节点映射到相应权重的难度和挑战。本文提出了一种克服这种局限性的方法，即对传统中心性测度进行杂交。杂交是通过将其中一个中心性度量作为映射函数来生成节点上的权值，然后使用其他中心性度量中的节点权值来实现更好的复杂排序。

引用次数: 22

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