Multivariate Relations Aggregation Learning in Social Networks

Multivariate relations are general in various types of networks, such as biological networks, social networks, transportation networks, and academic networks. Due to the principle of ternary closures and the trend of group formation, the multivariate relationships in social networks are complex and rich. Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important. Existing graph learning methods are based on the neighborhood information diffusion mechanism, which often leads to partial omission or even lack of multivariate relationship information, and ultimately affects the accuracy and execution efficiency of the task. To address these challenges, this paper proposes the multivariate relationship aggregation learning (MORE) method, which can effectively capture the multivariate relationship information in the network environment. By aggregating node attribute features and structural features, MORE achieves higher accuracy and faster convergence speed. We conducted experiments on one citation network and five social networks. The experimental results show that the MORE model has higher accuracy than the GCN (Graph Convolutional Network) model in node classification tasks, and can significantly reduce time cost.Comment: 11 pages, 6 figure

Experimental auctions, collective induction and choice shift: willingness-to-pay for rice quality in Senegal

We propose a collective induction treatment as an aggregator of information and preferences, which enables testing whether consumer preferences for food quality elicited through experimental auctions are robust to aggregation. We develop a two-stage estimation method based on social judgement scheme theory to identify the determinants of social influence in collective induction. Our method is tested in a market experiment aiming to assess consumers willingness-to-pay for rice quality in Senegal. No significant choice shift was observed after collective induction, which suggests that consumer preferences for rice quality are robust to aggregation. Almost three quarters of social influence captured by the model and the variables was explained by social status, market expertise and information

About the relations between Management Accounting Systems, Intellectual Capital and Performance

The present study is focused on the contribution of management accounting systems (MAS) in the development of intellectual capital (IC). Based on empirical evidence that supports the proposition that the value creation process is strongly associated to the level of IC, the study also examines the mediating effect of MAS on performance through their positive direct effect on IC. These relationships were consolidated into a model and empirically tested with data from 281 Portuguese firms using the Structural Equation Modeling (SEM). The findings show that six out of nine hypothesized relationships were supported by data with positive and significant causal links between MAS and the human and structural dimensions of IC. Results confirmed the conceptual validity of the circular model for the interactions among the three IC dimensions. Results also showed a positive and significant direct effect of structural capital on performance. Overall, the results confirmed the validity of the proposed model and contributed to the literature on the role of MAS in supporting the development of the I

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research