Social influence on selection behaviour: distinguishing local- and global-driven preferential attachment

Social influence drives human selection behaviours when numerous objects competing for limited attentions, which leads to the 'rich get richer' dynamics where popular objects tend to get more attentions. However, evidences have been found that, both the global information of the whole system and the local information among one's friends have significant influence over the one's selection. Consequently, a key question raises that, it is the local information or the global information more determinative for one's selection? Here we compare the local-based influence and global-based influence. We show that, the selection behaviour is mainly driven by the local popularity of the objects while the global popularity plays a supplementary role driving the behaviour only when there is little local information for the user to refer to. Thereby, we propose a network model to describe the mechanism of user-object interaction evolution with social influence, where the users perform either local-driven or global-driven preferential attachments to the objects, i.e., the probability of an objects to be selected by a target user is proportional to either its local popularity or global popularity. The simulation suggests that, about 75% of the attachments should be driven by the local popularity to reproduce the empirical observations. It means that, at least in the studied context where users chose businesses on Yelp, there is a probability of 75% for a user to make a selection according to the local popularity. The proposed model and the numerical findings may shed some light on the study of social influence and evolving social systems

Do reviews from friends and the crowd affect online consumer posting behaviour differently?

User-generated reviews are valuable resources for consumers to gain information of products which has significant impact on their following decision-making. With the development of social network service, consumers are exposed to reviews coming from both friends and the crowds (non-friends). However, the impact of friends’ and crowds’ reviews on consumer posting behaviour has not been well differentiated. Using the online review information as well as the underlying social network from Yelp, this paper develops a multilevel mixed effect probit model to study the impact of consumer characteristics and reviews of different sources, i.e. friends or crowds, on the possibility of consumer further engaging in posting behaviour. Despite the common perception that the volume, valance and variance of reviews significantly impact the possibility of following posting behaviour, we show that such influence majorly comes from the friend reviews. The volume of friend reviews has much stronger impact on the target user’s posting behaviour than that of the crowds. The valance and variance of the crowd reviews show no significant influence when ignoring the friend reviews, but negative influence when considering it. The friend reviews and crowd reviews are further divided as positive and negative ones, and only the positive friend reviews and negative crowd review are found significantly enhancing the posting possibility

The effect of product distance on the eWOM in recommendation network

The online Product Recommendation Networks (PRNs), connecting similar products with hyperlinks, have been widely implemented in user-generated content websites and ecommerce systems. With the PRNs as the virtual shelves, this paper explores the impact of the distance between products on the formation of product electronic Word-of-Mouth (eWOM). Employing an empirical book recommendation network of Amazon, the study one explores the effect of a focal product’s neighborhood (nearby others) on its eWOM, and study two explores the eWOM similarity between product pairs that are at one, two and three clicks away from each other. The results reveal the significant role played by the product distance on the association of their eWOM. On one hand, a focal product’s eWOM is largely influenced by that of its neighborhood. On the other hand, the good connectivity between two products, which is defined as the number of paths connecting them, is closely associated with the eWOM similarity between them. The findings suggest that the products should be considered as interactive collectives rather than separated individuals particularly in the eWOM studies

Medical service unity: an effective approach for medical care in rural areas in China

Medical care in rural China has long suffered because of a concentration of medical resources in major hospitals in cities. The patients in rural areas thus do not have affordable access to quality medical services. To tackle such issues, a tiered medical scheme (TMS) was promoted by the Chinese State Council in 2015. It divides hospitals into three tiers and encourages collaborations among different tiers within a region in order to provide better accessibility to medical care for patients in rural areas. The implementation of the TMS policy has not been successful, because the previous funding model, which allocated funding to each hospital according to the number of patients treated, did not facilitate close collaborations between different hospitals. In this report, the medical service unity (MSU) approach, which has been piloted in Funan county, is reported. The MSU organises the tiered hospitals as a unity in terms of medical capabilities and financial abilities. With the radical reform of financial decentralisation, three flows are thereby enabled: the funding flow binds together the hospitals into a unity, the patient flow shares the load across the providers and eases barriers to access, and the resource flow ensures accessibility and affordability for patients. The MSU approach has been shown by the pilot project in Funan to be effective for the realisation of the TMS policy, benefiting hospitals, doctors and patients. The successful experience of the Funan MSU could be introduced to other regions across China and other countries. In particular, future finance reform policies for the health system would largely benefit the health reforms and especially the decentralisation of medical resources to rural areas

Improvement of Phylogenetic Method to Analyze Compositional Heterogeneity

Background: Phylogenetic analysis is a key way to understand current research in the biological processes and detect theory in evolution of natural selection. The evolutionary relationship between species is generally reflected in the form of phylogenetic trees. Many methods for constructing phylogenetic trees, are based on the optimization criteria. We extract the biological data via modeling features, and then compare these characteristics to study the biological evolution between species. Results: Here, we use maximum likelihood and Bayesian inference method to establish phylogenetic trees; multi-chain Markov chain Monte Carlo sampling method can be used to select optimal phylogenetic tree, resolving local optimum problem. The correlation model of phylogenetic analysis assumes that phylogenetic trees are built on homogeneous data, however there exists a large deviation in the presence of heterogeneous data. We use conscious detection to solve compositional heterogeneity. Our method is evaluated on two sets of experimental data, a group of bacterial 16S ribosomal RNA gene data, and a group of genetic data with five homologous species. Conclusions: Our method can obtain accurate phylogenetic trees on the homologous data, and also detect the compositional heterogeneity of experimental data. We provide an efficient method to enhance the accuracy of generated phylogenetic tre

Solar-Driven H_2O_2 Generation From H_2O and O_2 Using Earth-Abundant Mixed-Metal Oxide@Carbon Nitride Photocatalysts

Light-driven generation of H_2O_2 only from water and molecular oxygen could be an ideal pathway for clean production of solar fuels. In this work, a mixed metal oxide/graphitic-C_3N_4 (MMO@C_3N_4) composite was synthesized as a dual-functional photocatalyst for both water oxidation and oxygen reduction to generate H_2O_2. The MMO was derived from a NiFe-layered double hydroxide (LDH) precursor for obtaining a high dispersion of metal oxides on the surface of the C_3N_4 matrix. The C_3N_4 is in the graphitic phase and the main crystalline phase in MMO is cubic NiO. The XPS analyses revealed the doping of Fe^(3+) in the dominant NiO phase and the existence of surface defects in the C3N4 matrix. The formation and decomposition kinetics of H_2O_2 on the MMO@C_3N_4 and the control samples, including bare MMO, C_3N_4 matrix, Ni- or Fe-loaded C_3N_4 and a simple mixture of MMO and C_3N_4, were investigated. The MMO@C_3N_4 composite produced 63 μmol L^(−1) of H_2O_2 in 90 min in acidic solution (pH 3) and exhibited a significantly higher rate of production for H_2O_2 relative to the control samples. The positive shift of the valence band in the composite and the enhanced water oxidation catalysis by incorporating the MMO improved the light-induced hole collection relative to the bare C_3N_4 and resulted in the enhanced H_2O_2 formation. The positively shifted conduction band in the composite also improved the selectivity of the two-electron reduction of molecular oxygen to H_2O_2

The effect of 5 cycles of biparental mass selection on a narrow base maize population based on phenotype, combining ability, and SSR analyses

Five cycles of biparental mass selection (MS) were carried out to improve the narrow-base maize population P4C0. In different ecological environments, the phenotypes of the developed populations were analyzed, the combining abilities were tested according to an incomplete diallel model to study the effects of selection, and the effects of MS on genetic diversity of the populations were also analyzed by using 51 pairs of SSR markers. It was found that MS was effective in improving the main traits and general combing ability (GCA), and it was effective on maintain¬ing the genetic diversity of the population. At the same time, the genetic structure was changed with advance of selection

Balancing the popularity bias of object similarities for personalised recommendation

Network-based similarity measures have found wide applications in recommendation algorithms and made signicant contributions for uncovering users' potential interests. However, existing measures are generally biased in terms of popularity, that the popular objects tend to have more common neighbours with others and thus are considered more similar to others. Such popularity bias of similarity quantification will result in the biased recommendations, with either poor accuracy or poor diversity. Based on the bipartite network modelling of the user-object interactions, this paper firstly calculates the expected number of common neighbours of two objects with given popularities in random networks. A Balanced Common Neighbour similarity index is accordingly developed by removing the random-driven common neighbours, estimated as the expected number, from the total number. Recommendation experiments in three data sets show that balancing the popularity bias in a certain degree can significantly improve the recommendations' accuracy and diversity simultaneously