Traditional Link Research Articles

Emiratos Emiratos Árabes Unidos en Oriente Medio. Antiislamismo, militarismo y estrategia de presión múltiple

TThe United Arab Emirates’ (UAE) foreign policy has experienced substantial changes since the beginning of the so-called Arab Spring in 2011. This paper describes the characteristics and main reasons of this transformation, as well as the possible consequences of a visible militaristic projection that goes beyond the limits of the Gulf region, the main scenario of Emirati diplomatic action until recently. Our framework takes into account, first of all, the traditional link between the United States and the UAE and how recent US administrations changed their perception about security in the Gulf; how Emir Mohammed bin Zayed has secured his grip over the country, and the relevance of his alliance with the Saudi Crown Prince Mohammed bin Salman; the aim of neutralizing the democratic stream that emerged from the Arab revolutions and, from there on, the Emirati perception about how political Islam and particularly the current best represented by the Muslim Brotherhood was becoming the main beneficiary of the erosion of the traditional Arab regime. Moreover, this paper proposes that despite the convergence with Saudi Arabia in a number of scenarios, the UAE has kept a foreign policy based on its own interests, which strengthened its increasing role as a regional main contender in the Middle East.

A Link Prediction Method Based on Neural Networks

Link prediction to optimize network performance is of great significance in network evolution. Because of the complexity of network systems and the uncertainty of network evolution, it faces many challenges. This paper proposes a new link prediction method based on neural networks trained on scale-free networks as input data, and optimized networks trained by link prediction models as output data. In order to solve the influence of the generalization of the neural network on the experiments, a greedy link pruning strategy is applied. We consider network efficiency and the proposed global network structure reliability as objectives to comprehensively evaluate link prediction performance and the advantages of the neural network method. The experimental results demonstrate that the neural network method generates the optimized networks with better network efficiency and global network structure reliability than the traditional link prediction models.

Beyond Brexit – Is the UK’s Fixed Quota Allocation (FQA) system in need of a fix?

This paper presents the results of interviews conducted with stakeholders in the North East Scotland commercial fishing industry who operate with Fixed Quota Allocation (FQA) units under the United Kingdom’s (UK’s) fisheries management system. The results derived from these interviews provide a view of the FQA system from those operating within it and identify strengths and weaknesses of the system. The implications of these results are that the FQA system, and the introduction of FQA units, led to increased investment and efficiency within the North East Scotland ‘over 10 m′ sector. This, however, has led to a consolidation of quota and the raising of barriers to entry. These may threaten the future of the industry and its traditional links to its coastal communities but the situation is complicated by concerns that any changes to the system to address these threats risks destabilising the economic structure of the industry which is built on the asset value of FQA units. The UK Government has indicated that the system will endure post-Brexit so it may be that any additional quota realised from exiting the European Union (EU) is applied to a system where those who will benefit most understand that it may risk the industry’s future. This paper calls for more research into the FQA system, with those who work with FQA units and those who do not, to ensure that the socioeconomic impacts of this system are better understood in order that future management safeguards the stability of the industry into the future.

Follower Link Prediction Using the XGBoost Classification Model with Multiple Graph Features

The Follower Link Prediction is an emerging application preferred by social networking sites to increase their user network. It helps in finding potential unseen individual and can be used for identifying relationship between nodes in social network. With the rapid growth of many users in social media, which users to follow leads to information overload problems. Previous works on link prediction problem are generally based on local and global features of a graph and limited to a smaller dataset. The number of users in social media is increasing in an extraordinary rate. Generating features for supervised learning from a large user network is challenging. In this paper, a supervised learning model (LPXGB) using XGBoost is proposed to consider the link prediction problem as a binary classification problem. Many hybrid graph feature techniques are used to represent the dataset suitable for machine learning. The efficiency of the LPXGB model is tested with three real world datasets Karate, Polblogs and Facebook. The proposed model is compared with various machine learning classifiers and also with traditional link prediction models. Experimental results are evident that the proposed model achieves higher classification accuracy and AUC value.

Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm.

Binary Generalized Linear Mixed Model (GLMM) is the most common method used by researchers to analyze clustered binary data in biological and social sciences. The traditional approach to GLMMs causes substantial bias in estimates due to steady shape of logistic and normal distribution assumptions thereby resulting into wrong and misleading decisions. This study brings forward an approach governed by skew generalized t distributions that belong to a class of potentially skewed and heavy tailed distributions. Interestingly, both the traditional logistic and probit mixed models, as well as other available methods can be utilized within the skew generalized t-link model (SGTLM) frame. We have taken advantage of the Expectation-Maximization algorithm accelerated via parameter-expansion for model fitting. We evaluated the performance of this approach to GLMMs through a simulation experiment by varying sample size and data distribution. Our findings indicated that the proposed methodology outperforms competing approaches in estimating population parameters and predicting random effects, when the traditional link and normality assumptions are violated. In addition, empirical standard errors and information criteria proved useful for detecting spurious skewness and avoiding complex models for probit data. An application with respiratory infection data points out to the superiority of the SGTLM which turns to be the most adequate model. In future, studies should focus on integrating the demonstrated flexibility in other generalized linear mixed models to enhance robust modeling.

Missing link prediction and spurious link detection based on attractive force and community.

With the rapid development of Internet and information technology, networks have become an important media of information diffusion in the global. In view of the increasing scale of network data, how to ensure the completeness and accuracy of the obtainable links from networks has been an urgent problem that needs to be solved. Different from most traditional link prediction methods only focus on the missing links, a novel link prediction approach is proposed in this paper to handle both the missing links and the spurious links in networks. At first, we define the attractive force for any pair of nodes to denote the strength of the relation between them. Then, all the nodes can be divided into some communities according to their degrees and the attractive force on them. Next, we define the connection probability for each pair of unconnected nodes to measure the possibility if they are connected, the missing links can be predicted by calculating and comparing the connection probabilities of all the pairs of unconnected nodes. Moreover, we define the break probability for each pair of connected nodes to measure the possibility if they are broken, the spurious links can also be detected by calculating and comparing the break probabilities of all the pairs of connected nodes. To verify the validity of the proposed approach, we conduct experiments on some real-world networks. The results show the proposed approach can achieve higher prediction accuracy and more stable performance compared with some existing methods.

CSIP: Enhanced Link Prediction with Context of Social Influence Propagation

Data mining in social networks brings an indispensable role for the construction of smart cities from the perspective of social development. Link prediction is an important task of data mining, especially in the knowledge graph, which is also called knowledge graph completion. Link prediction aims to find missing links or predict potential links according to the current social network. The most existing link prediction methods focus on static information in social networks, such as topology and node attributes, which are partly provided by users. When users are unwilling to provide or intentionally hide these static features, traditional link prediction methods cannot achieve ideal performance. The dynamic information of social influence propagation in social networks can avoid the user's subjective impact and better reflect the relationship between users. In addition, users show different degrees of interest and authority on various topics in the real world, leading to different influence propagation patterns. Therefore, we use context of social influence to optimize the topic-aware influence propagation model to improve the performance of link prediction. In this paper, we propose a new multi-output graph neural network framework to capture influence propagation in social networks and model the influence of users in different roles. In this way, the underlying information of influence between users can be used to construct new features to improve the performance of link prediction. Our experiments conduct the method on multiple benchmark datasets. The experimental results show that the modeling of context is effective, and our model outperforms the compared state-of-the-art link prediction methods.

The digital transformation and application prospect of military procurement management

Throughout the military and economic development of various countries and the frontier of public resources, the development from informatization to digitalization and intellectualization has become the inevitable call of The Times. In the era of digital logistics, strengthening the digital management ability of military procurement is an inevitable outcome to meet the needs of modern war. Procurement digital management integrates big data, cloud computing, Internet of Things, process automation and other technologies to help the digital upgrading of traditional procurement links. How to apply digital technology to promote the construction and development of modern military procurement has become a subject worth in-depth discussion.

Tensor decomposition for link prediction in temporal directed networks* *This work was supported by Research Funds for the Central Universities (No. 30918012204).

Link prediction is a challenging research topic that comes along with the prevalence of network data analysis. Compared with traditional link prediction, determining future links in temporal directed networks is more complicated. In this paper, we introduce a novel link prediction method based on non-negative tensor factorization that takes into account the link direction and temporal information. In the proposed method, the temporal directed networks are modeled as a fourth-order tensor, which considers the temporal correlation coefficient of adjacent snapshots. We obtain link information by the factor matrices of tensor decomposition and score node pairs related to the link information. We give the interpretation and prove the convergence of the proposed method. Experiments are conducted on several temporal directed networks. The experimental results show that compared to several well-known link prediction methods, the proposed method improves the performance of link prediction. It is mainly because we use structural and temporal information effectively.

The research of clinical temporal knowledge graph based on deep learning

Temporal knowledge base exists on various fields. Take medical medicine field as example, diabetes is a typical chronic disease which evolves slowly. This paper starts from actual EMR data of hospitals by combination of experience and knowledge of clinical doctors. Link prediction on clinical knowledge base such as diabetic complication requires the analysis on temporal characteristic of temporal knowledge base, which is a great challenge for traditional link prediction models. This paper proposes temporal knowledge graph link prediction model based on deep learning. This model selects the TransR transformation model suitable for big data and makes entity projection in relation space containing different semantic meanings, so as to vector the entities and complex semantic relations in graph. Then it adopts LSTM recursive neural network and adds the top-bottom relational information of the graph for sequential learning. Finally it constantly carries out deep learning through incremental calculation and LSTM recursive network to improve the accuracy of prediction. The incremental LSTM model highlights the hidden semantic and clinical temporal information and effectively utilizes sequential learning to mining forward-backward dependent information. It compensates the deficiency of lower prediction accuracy on timely knowledge graph caused by the traditional link prediction models. Finally, it is proved that the new model has better performance over temporal knowledge graph link prediction.

Research on the Link Prediction Model of Dynamic Multiplex Social Network Based on Improved Graph Representation Learning

In the natural and social systems of the real world, various network can be seen everywhere. The world where people live can be seen as a combination of network with different dimensions. Link prediction formalizes the interaction behavior between people. Traditional link prediction methods mainly study the user behavior of static social network. This article studied the dynamic graph representation learning so as to put forward an improved link prediction model in dynamic social network. Besides, the interactions in the real world can be multiple, links at different moments may have different meanings. The proposed model firstly solved the problem of link prediction on multiple kinds of edges. The whole embedding of each node is separated into two parts, basic embedding and edge embedding. Then the proposed model selected time slices for dynamic social network to get the graph embeddings in different snapshots. What's more, the t+1 time step embedding vector was used to validate t time step prediction effect and the proposed model performed better than traditional graph representation learning methods.

Similar but foreign: Link recommendation across communities

Complex networks have been widely used to model complex systems composed of interacting entities. It is possible to recommend new relationships between entities according to the network topology and the entities’ properties. The likelihood of forming a missing or potential relationship is often captured by similarity measures. As community clusters are also based on the entities’ similarities, traditional link recommendation/prediction methods naturally tend to recommend links within a community. However, potential links valuable across communities are often overlooked and may cause the problem of information cocoons. We focus on link recommendation across communities based on homogeneous and heterogeneous information networks, which aims to improve the diversity of recommender systems. We propose to solve this problem using a novel similarity calculation and heterogeneous network embedding methods. Our comprehensive experiments on real-world datasets and synthetic datasets show that our methods strike a good balance between accuracy and efficiency, while generating valuable unconventional recommendations in practical application scenarios.

Link prediction in growing networks with aging

The link prediction problem aims to predict new links for future, or missing links or unobserved links in complex networks. Traditional link prediction methods are mostly concentrated on static networks. In this paper, we mainly explore link prediction problems in growing networks. We propose a series of time-sliced metrics to estimate the likelihood of the existence of missing links between two nodes for evolving networks based on traditional link prediction indices. We found that these proposed metrics outperform existing metrics for growing networks with time decay factor, especially when the decay factors are small. Besides, to improve prediction efficiency and practicability, we propose the function expressions for optimal slice number and decay factor for real-world networks. The formula enables us to estimate the aging speed of real growing networks, resulting in accurate and fast prediction of missing links in growing networks.

The Matter of the Image: For a Material Aesthetics between Ontology and Linguistics

This essay aims at understanding the role of matter and outlining material aesthetics in the age of the digital image. To do so, it first analyses the traditional link between matter and form within the framework of classic ontology and aesthetics, according to which matter is the passive principle of reality subdued to the action of form and its logos. It then attempts to rethink this relationship through the activation of matter in its metaphysical, as well as aesthetical and linguistical dimensions. Such activation is outlined through the analysis of the concept of “concrete sense” and “concrete metaphysics” developed by Pavel Florensky in his inquiry into the Orthodox icon, and through that of “vital semantics” defined by Roland Barthes in his essay on 16th century Italian painter Arcimboldo. The argument is that these two concepts allow for a different relationship among art’s praxis, ontology, and linguistics, leading to the first formulation of a material aesthetics, while also rethinking the temporality of both traditional and digital image. Article received: April 30, 2020; Article accepted: June 23, 2020; Published online: September 15, 2020; Original scholarly paper

Do Age Norm Violations Favor or Endanger Firm Innovation? The Role of Environmental Dynamism

Due to the reduction of seniority-based promotion systems, the traditional link between employees’ age and hierarchical status is getting disrupted in many organizations. In this study we use theoretical arguments from career timetables and status incongruence research to argue for an effect of these changing age-status structures on organizational innovation. In detail, we propose a moderated mediation model with age discrimination climate as mediator and the firms’ environmental dynamism as moderator. Using large-scale multisource data of 69 firms with information provided by 12,869 employees, 69 top HR representatives and 230 top board members, we show that the impact of the age-status link on organizational innovation is different for firms with high and low levels of environmental dynamism. In firms with low levels of environmental dynamism, a weak age-status link harms organizational innovation through increased levels of age discrimination climate. In contrast, a weak age-status link has a direct positive effect on innovation in firms operating in highly dynamic environments. These findings suggest that the fading age-status relationships in organizations are not as unambiguously harmful as previously assumed and hold thus important practical implications as dealing with violations of the traditional career timetables will become more important for the current and future success of organizations.

The constitution of school autonomy in Australian public education: areas of paradox for social justice

ABSTRACT School autonomy policies have circulated through various modes of educational governance internationally, endorsing the view that more autonomy will improve schools and their systems. When subject to the discourses and practices of marketization, however, school ‘autonomy’ has been mobilized in ways that generate injustice. These injustices are the focus of this paper. We draw on preliminary findings from a three-year study that is exploring the social justice implications of school autonomy reform across four Australian states. Drawing on interviews with 42 stakeholders, the paper identifies four key areas of paradox for social justice currently confronting public schools and school systems. The language of paradox is drawn on to narrate the oppositional politics between the discourses and practices constituting school autonomy and the pursuit of social justice. Such narration raises important questions for Australian public education. It highlights how these discourses are changing what is meant by the public in public education. Engaging with the language of paradox in thinking about school autonomy reform, we argue, is important given the broader landscape where public schooling is being reconstituted and where traditional links to social justice and the common good are under threat.

Worlds Apart? Reassessing von Uexküll’s Umwelt in Embodied Cognition with Canguilhem, Merleau-Ponty, and Deleuze

Jakob von Uexküll’s (1864-1944) account of Umwelt has been proposed as a mediating concept to bridge the gap between ecological psychology’s realism about environmental information and enactivism’s emphasis on the organism’s active role in constructing the meaningful world it inhabits. If successful, this move would constitute a significant step towards establishing a single ecological-enactive framework for cognitive science. However, Uexküll’s thought itself contains different perspectives that are in tension with each other, and the concept of Umwelt is developed in representationalist terms that conflict with the commitments of both enactivism and ecological psychology. One central issue shared by all these approaches is the problem of how a living being experiences its environment. In this paper, we will look at Uexküll’s reception in French philosophy and highlight the different ways in which the concept of Umwelt functions in the work of Georges Canguilhem, Maurice Merleau-Ponty, and Gilles Deleuze. This analysis helps clarify different aspects of Uexküll’s thought and the deeper philosophical implications of importing his concepts into embodied cognitive science. This paper is part of a recent trend in which enactivism engages with continental philosophy in a way that both deepens and transcends the traditional links to phenomenology, including most recently the thought of Georg W. F. Hegel and Gilbert Simondon. However, no more than a brief outline and introduction to the potentials and challenges of this complex conceptual intersection can be given here. Our hope is that it serves to make more explicit the philosophical issues that are at stake for cognitive science in the question of experienced environments, while charting a useful course for future research.

On Preserving Urban Estates Within The New Limits Of Moscow

In the article, the author analyzes the state of estate complexes located on the territory of New Moscow. The author focuses his attention on several urban and suburban estates, including the country house of the Bergs, Valuevo and Klenovo villages, where remnants of cultural heritage are preserved, to consider several problems of contemporary restoration activities. It is noted that since 2011 the listed objects are under the protection of the Department of cultural heritage of the city of Moscow. The author considers the architectural features both of the estate-park complexes as a single whole, and their constituent elements, such as household buildings, elements of landscape design, and Orthodox church buildings made of stone. This article casts light on some of the difficulties that accompany the restoration of these church buildings, their safety hazards that arose in the post-industrial period, the period when traditional cultural links were disrupted. The comparative-historical method, as well as methods for retrospective analysis of historical sources, generalization and identification of the main features of architectural styles, serve as the basis of this research project.

The Swearer's Prayer

At the time elite Americans abandoned or modified Christian theologies of perdition, American procedural law relied ever more strongly on the traditional link between law and the theology of divine punishment: the testimonial oath. While conventional histories of American evidence law tell a rather straightforward modernization story of a move from premodern sacral modes of prooftaking to rational forensic modes of examination, this paper shows that oathtaking did not diminish under modern law reform but significantly expanded with the influence of New York's code of 1848. For the first time in America, procedure codes required all pleadings to be sworn by the parties, and all parties and interested witnesses were made competent to take the oath and testify on the stand. Lawyers at first expected the dread of cross examination to deter perjury, but they quickly adjusted their theories in practice to detect lying under oath, which they believed had become rampant under the codes. The lawyers' faith in their ability to detect truth were stymied as racialized witnesses came before the bar. In order to account for racial disqualifications on testimony in a world increasingly open to party testimony (and perjury), lawyers revived an older theology of perdition, swirling together supposedly premodern and rational modes of investigation well into the twentieth century.

Jianchuan Bai

The Bai language () is spoken by approximately 1.6 million people in northwest Yunnan Province, China. Of the 25 minority languages spoken in Yunnan, where 33% of the population are ethnic minorities and 67% are Han Chinese, the Bai ethnic minority is second in population only to the Yi (Wiersma 1990, 2003; 2010 census). Bai is classified as a Tibeto-Burman language (Xu & Zhao 1964, 1984), although arguments have been raised as to its possible early Sinitic origins (Starostin 1994, 1995). A summary in French reviews Chinese loanwords, ancient Bai, and comparative Bai dialects (Dell 1981). The historical influence of Chinese on Bai has been significant, but evidence is not compelling that Bai is Sinitic (Norman 2003: 73). There are three major dialects of Bái: Jiànchuān (), Dàlĭ (), and Bìjiāng (). The data in this illustration represent the variety of Jianchuan (jian1239, BCA). The third author (), who was about 60 years old at the time of recording, is a male native of the Jianchuan region, originating from QiÁohǒu, a mountain village some 50 km southwest of Jianchuan city – a remote area known for salt mining and where the language has been less influenced by modern Chinese. These locations are indicated on the map of Yunnan (the southwesternmost province of China in an intensely minority-language-populated area) in Figure 1. The traditional geographical link from Qiaohou is to Jianchuan to the north rather than to Dali to the south, and many of the most distinctive characteristics of Jianchuan Bai described here are not found in Dali Bai.