Predicting Positive and Negative Links with Noisy Queries: Theory & Practice

Abstract. Procrastination at work has been examined relatively scarcely, partly due to the lack of a globally validated and context-specific workplace procrastination scale. This study investigates the psychometric characteristics of the Procrastination at Work Scale (PAWS) among 1,028 office employees from seven countries, namely, Croatia, the Czech Republic, Finland, Slovenia, Turkey, Ukraine, and the United Kingdom. Specifically, it was aimed to test the measurement invariance of the PAWS and explore its discriminant validity by examining its relationships with work engagement and performance. Multi-group confirmatory factor analysis shows that the basic factor structure and item loadings of the PAWS are invariant across countries. Furthermore, the two subdimensions of procrastination at work exhibited different patterns of relationships with work engagement and performance. Whereas soldiering was negatively related to work engagement and task performance, cyberslacking was unrelated to engagement and performance. These results indicate further validity evidence for the PAWS and the psychometric characteristics show invariance across various countries/languages. Moreover, workplace procrastination, especially soldiering, is a problematic behavior that shows negative links with work engagement and performance.

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Relation Representation Learning Via Signed Graph Mutual Information Maximization for Trust Prediction

Symmetry ◽

10.3390/sym13010115 ◽

2021 ◽

Vol 13 (1) ◽

pp. 115

Author(s):

Yongjun Jing ◽

Hao Wang ◽

Kun Shao ◽

Xing Huo

Keyword(s):

Mutual Information ◽

Representation Learning ◽

Signed Graph ◽

Trust Prediction ◽

Trust Networks ◽

Trust Relation ◽

Information Maximization ◽

Real World Datasets ◽

Negative Links ◽

Mutual Information Maximization

Trust prediction is essential to enhancing reliability and reducing risk from the unreliable node, especially for online applications in open network environments. An essential fact in trust prediction is to measure the relation of both the interacting entities accurately. However, most of the existing methods infer the trust relation between interacting entities usually rely on modeling the similarity between nodes on a graph and ignore semantic relation and the influence of negative links (e.g., distrust relation). In this paper, we proposed a relation representation learning via signed graph mutual information maximization (called SGMIM). In SGMIM, we incorporate a translation model and positive point-wise mutual information to enhance the relation representations and adopt Mutual Information Maximization to align the entity and relation semantic spaces. Moreover, we further develop a sign prediction model for making accurate trust predictions. We conduct link sign prediction in trust networks based on learned the relation representation. Extensive experimental results in four real-world datasets on trust prediction task show that SGMIM significantly outperforms state-of-the-art baseline methods.

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An algorithm Walktrap-SPM for detecting overlapping community structure

International Journal of Modern Physics B ◽

10.1142/s0217979217501211 ◽

2017 ◽

Vol 31 (15) ◽

pp. 1750121 ◽

Cited By ~ 1

Author(s):

Fang Hu ◽

Youze Zhu ◽

Yuan Shi ◽

Jianchao Cai ◽

Luogeng Chen ◽

...

Keyword(s):

Community Structure ◽

Random Walk ◽

Community Detection ◽

Real World ◽

Overlapping Community Detection ◽

Overlapping Communities ◽

Running Time ◽

Overlapping Community ◽

Negative Links ◽

Novel Algorithm

In this paper, based on Walktrap algorithm with the idea of random walk, and by selecting the neighbor communities, introducing improved signed probabilistic mixture (SPM) model and considering the edges within the community as positive links and the edges between the communities as negative links, a novel algorithm Walktrap-SPM for detecting overlapping community is proposed. This algorithm not only can identify the overlapping communities, but also can greatly increase the objectivity and accuracy of the results. In order to verify the accuracy, the performance of this algorithm is tested on several representative real-world networks and a set of computer-generated networks based on LFR benchmark. The experimental results indicate that this algorithm can identify the communities accurately, and it is more suitable for overlapping community detection. Compared with Walktrap, SPM and LMF algorithms, the presented algorithm can acquire higher values of modularity and NMI. Moreover, this new algorithm has faster running time than SPM and LMF algorithms.

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Genome-Wide Identification and Comparative Analysis for OPT Family Genes in Panax ginseng and Eleven Flowering Plants

Molecules ◽

10.3390/molecules24010015 ◽

2018 ◽

Vol 24 (1) ◽

pp. 15 ◽

Cited By ~ 2

Author(s):

He Su ◽

Yang Chu ◽

Junqi Bai ◽

Lu Gong ◽

Juan Huang ◽

...

Keyword(s):

Transcription Factors ◽

Panax Ginseng ◽

Expression Patterns ◽

Flowering Plants ◽

Functional Development ◽

Wide Range ◽

Organ Specific ◽

Genome Level ◽

Negative Links ◽

Global Platform

Herb genomics and comparative genomics provide a global platform to explore the genetics and biology of herbs at the genome level. Panax ginseng C.A. Meyer is an important medicinal plant for a variety of bioactive chemical compounds of which the biosynthesis may involve transport of a wide range of substrates mediated by oligopeptide transporters (OPT). However, information about the OPT family in the plant kingdom is still limited. Only 17 and 18 OPT genes have been characterized for Oryza sativa and Arabidopsis thaliana, respectively. Additionally, few comprehensive studies incorporating the phylogeny, gene structure, paralogs evolution, expression profiling, and co-expression network between transcription factors and OPT genes have been reported for ginseng and other species. In the present study, we performed those analyses comprehensively with both online tools and standalone tools. As a result, we identified a total of 268 non-redundant OPT genes from 12 flowering plants of which 37 were from ginseng. These OPT genes were clustered into two distinct clades in which clade-specific motif compositions were considerably conservative. The distribution of OPT paralogs was indicative of segmental duplication and subsequent structural variation. Expression patterns based on two sources of RNA-Sequence datasets suggested that some OPT genes were expressed in both an organ-specific and tissue-specific manner and might be involved in the functional development of plants. Further co-expression analysis of OPT genes and transcription factors indicated 141 positive and 11 negative links, which shows potent regulators for OPT genes. Overall, the data obtained from our study contribute to a better understanding of the complexity of the OPT gene family in ginseng and other flowering plants. This genetic resource will help improve the interpretation on mechanisms of metabolism transportation and signal transduction during plant development for Panax ginseng.

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Social Media Addiction and Personality: A Review of Research

RUDN Journal of Psychology and Pedagogics ◽

10.22363/2313-1683-2021-18-3-607-630 ◽

2021 ◽

Vol 18 (3) ◽

pp. 607-630

Author(s):

Viktor P. Sheinov

Keyword(s):

Social Networks ◽

Social Media ◽

Foreign Language ◽

Self Esteem ◽

Way Of Life ◽

Face To Face ◽

Social Media Addiction ◽

Physical Health Problems ◽

Negative Links

Social networks are taking up more and more place in the daily life of modern people, becoming an integral part of our existence. At the same time, the role of social networks is constantly growing along with the rapid growth in the number of their active users. As online interaction for many has become more used than face-to-face communication, social networks have begun to seriously affect the way of life, communication, interests and psychology of people. The use of social networks is growing exponentially and has covered more than a third of the worlds population; therefore, researchers from different countries are actively studying social networks. Considerable empirical data has been accumulated that requires generalization and understanding, which is the purpose of this review. We found positive links between social media addiction and depression, anxiety, stress, neuroticism, emotional problems, low self-esteem, cyber-victimization, physical health problems, mental disorders, loneliness, procrastination, smartphone and internet addiction, and infidelity in relationships. Negative links were revealed between social media addiction and life satisfaction, academic performance of schoolchildren and students, labor productivity and commitment to the organization of its employees, social capital, and age. The main reason for social media addiction is the need for communication, and women are generally more active in social networks than men. This review provides only those links of social media addiction that have been established in a number of studies conducted in different countries. The presented results were obtained abroad using foreign language questionnaires that determine social media addiction. The lack of such a reliable and valid tool among Russian-speaking psychologists has become a serious factor hindering the conduct of similar domestic research. With this in view, the author developed a specially designed social media addiction questionnaire.

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Heterogeneity in spontaneous sleep arousals: positive and negative links with early amyloid-beta and cognition

10.21203/rs.3.rs-271256/v1 ◽

2021 ◽

Author(s):

Daphne Chylinski ◽

Maxime Van Egroo ◽

Justinas Narbutas ◽

Ekaterina Koshmanova ◽

Christian Berthomier ◽

...

Keyword(s):

Amyloid Beta ◽

Recent Literature ◽

Sleep Stage ◽

Brain Regions ◽

Sleep Disruption ◽

Sleep Spindles ◽

Older Individuals ◽

Brain Areas ◽

Stage Transition ◽

Negative Links

Abstract Recent literature is pointing towards a tight relationship between sleep quality and amyloid-beta (Aβ) accumulation, a hallmark of Alzheimer’s disease (AD). Sleep arousals are considered to induce sleep disruption, and though their heterogeneity has been suggested, their correlates remain to be established. We classified arousals in sleep of 100 healthy older individuals according to their association with muscular tone increase (E+/E-) and sleep stage transition (T+/T-), and show differences in EEG oscillatory compositions across arousal types. We found that T + E- arousals, which interrupt sleep stability, were positively correlated with Aβ burden in brain regions earliest affected by AD neuropathology. By contrast, more prevalent T-E + arousals, upholding sleep continuity, were associated with lower cortical Aβ burden, and better cognition. We provide empirical evidence that spontaneous arousals are diverse and differently associated with brain integrity and cognition. Sleep arousals may offer opportunities to transiently synchronise distant brain areas, akin to sleep spindles.

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Constructing Negative Links from Multi-facet of Social Media

KSII Transactions on Internet and Information Systems ◽

10.3837/tiis.2017.05.010 ◽

2017 ◽

Vol 11 (5) ◽

Keyword(s):

Social Media ◽

Negative Links

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Blind at First Sight: The Role of Distinctively Accurate and Positive First Impressions in Romantic Interest

Psychological Science ◽

10.1177/0956797620919674 ◽

2020 ◽

Vol 31 (6) ◽

pp. 715-728 ◽

Cited By ~ 1

Author(s):

Lauren Gazzard Kerr ◽

Hasagani Tissera ◽

M. Joy McClure ◽

John E. Lydon ◽

Mitja D. Back ◽

...

Keyword(s):

Relationship Satisfaction ◽

First Impressions ◽

Speed Dating ◽

Negative Links ◽

Romantic Interest

Viewing other people with distinctive accuracy—the degree to which personality impressions correspond with targets’ unique characteristics—often predicts positive interpersonal experiences, including liking and relationship satisfaction. Does this hold in the context of first dates, or might distinctive accuracy have negative links with romantic interest in such evaluative settings? We examined this question using two speed-dating samples (Sample 1: N = 172, N = 2,407 dyads; Sample 2: N = 397, N = 1,849 dyads). Not surprisingly, positive impressions of potential dating partners were strongly associated with greater romantic interest. In contrast, distinctively accurate impressions were associated with significantly less romantic interest. This association was even stronger for potential partners whose personalities were less romantically appealing, specifically, those lower in extraversion. In sum, on a first date, distinctive accuracy tends to be paired with lower romantic interest. The potential implications of distinctive accuracy for romantic interest and of romantic interest for distinctive accuracy are discussed.

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Spectral clustering for link prediction in social networks with positive and negative links

Social Network Analysis and Mining ◽

10.1007/s13278-013-0128-6 ◽

2013 ◽

Vol 3 (4) ◽

pp. 1433-1447 ◽

Cited By ~ 17

Author(s):

Panagiotis Symeonidis ◽

Nikolaos Mantas

Keyword(s):

Social Networks ◽

Spectral Clustering ◽

Link Prediction ◽

Negative Links

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A model of social network formation under the impact of structural balance

International Journal of Modern Physics B ◽

10.1142/s021797921650051x ◽

2016 ◽

Vol 30 (09) ◽

pp. 1650051 ◽

Cited By ~ 4

Author(s):

Pei Li ◽

Jiajun Cheng ◽

Yingwen Chen ◽

Hui Wang

Keyword(s):

Social Networks ◽

Social Network ◽

Network Formation ◽

Common Phenomenon ◽

Structural Balance ◽

Negative Links ◽

Social Network Formation ◽

The Impact ◽

Negative Relationships ◽

Industrial Societies

Social networks have attracted remarkable attention from both academic and industrial societies and it is of great importance to understand the formation of social networks. However, most existing research cannot be applied directly to investigate social networks, where relationships are heterogeneous and structural balance is a common phenomenon. In this paper, we take both positive and negative relationships into consideration and propose a model to characterize the process of social network formation under the impact of structural balance. In this model, a new node first establishes a link with an existing node and then tries to connect to each of the newly connected node’s neighbors. If a new link is established, the type of this link is determined by structural balance. Then we analyze the degree distribution of the generated network theoretically, and estimate the fractions of positive and negative links. All analysis results are verified by simulations. These results are of importance to understand the formation of social networks, and the model can be easily extended to consider more realistic situations.

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