Network-Driven Reputation in Online Scientific Communities

The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. We propose here an algorithm which simultaneously computes reputation of users and fitness of papers in a bipartite network representing an online scientific community. Evaluation on artificially-generated data and real data from the Econophysics Forum is used to determine the method''s best-performing variants. We show that when the input data is extended to a multilayer network including users, papers and authors and the algorithm is correspondingly modified, the resulting performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher h-index than top papers and top authors chosen by other algorithms. We finally show that our algorithm is robust against persistent authors (spammers) which makes the method readily applicable to the existing online scientific communities.     

Information Filtering in Sparse Online Systems: Recommendation via Semi-Local Diffusion

With the rapid growth of the Internet and overwhelming amount of information and choices that people are confronted with, recommender systems have been developed to effectively support users’ decision-making process in the online systems. However, many recommendation algorithms suffer from the data sparsity problem, i.e. the user-object bipartite networks are so sparse that algorithms cannot accurately recommend objects for users. This data sparsity problem makes many well-known recommendation algorithms perform poorly. To solve the problem, we propose a recommendation algorithm based on the semi-local diffusion process on the user-object bipartite network. The simulation results on two sparse datasets, Amazon and Bookcross, show that our method significantly outperforms the state-of-the-art methods especially for those small-degree users. Two personalized semi-local diffusion methods are proposed which further improve the recommendation accuracy. Finally, our work indicates that sparse online systems are essentially different from the dense online systems, so it is necessary to reexamine former algorithms and conclusions based on dense data in sparse systems.     

Reputation and Competition in a Hidden Action Model

The economics models of reputation and quality in markets can be classified in three categories. (i) Pure hidden action, where only one type of seller is present who can provide goods of different quality. (ii) Pure hidden information, where sellers of different types have no control over product quality. (iii) Mixed frameworks, which include both hidden action and hidden information. In this paper we develop a pure hidden action model of reputation and Bertrand competition, where consumers and firms interact repeatedly in a market with free entry. The price of the good produced by the firms is contractible, whilst the quality is noncontractible, hence it is promised by the firms when a contract is signed. Consumers infer future quality from all available information, i.e., both from what they know about past quality and from current prices. According to early contributions, competition should make reputation unable to induce the production of high-quality goods. We provide a simple solution to this problem by showing that high quality levels are sustained as an outcome of a stationary symmetric equilibrium.     

Reputation and the evolution of conflict

The outcomes of conflicts in many human societies generate reputation effects that influence the nature of later conflicts. Those willing to escalate over even trivial offenses are considered honorable whereas those who do not are considered dishonorable (Nisbett & Cohen, 1996). Here I extend Maynard Smith's hawk-dove model of animal conflict to explore the logic of a strategy which uses reputation about its opponents to regulate its behavior. I show that a reputation-based strategy does well when (1) the value of the resource is large relative to the cost of losing a fight, (2) communities are stable, and (3) reputations are well known but subject to some amount of error. Reputation-based strategies may thus result in greater willingness to fight, but less fighting at equilibrium, depending upon the nature of the contests and the local socioecology. Additionally, this strategy is robust in the presence of poor knowledge about reputation.     

Reputation and the evolution of cooperation in sizable groups

The evolution of cooperation in social dilemmas has been of considerable concern in various fields such as sociobiology, economics and sociology. It might be that, in the real world, reputation plays an important role in the evolution of cooperation. Recently, studies that have addressed indirect reciprocity have revealed that cooperation can evolve through reputation, even though pairs of individuals interact only a few times. To our knowledge, most indirect reciprocity models have presumed dyadic interaction; no studies have attempted analysis of the evolution of cooperation in large communities where the effect of reputation is included. We investigate the evolution of cooperation in sizable groups in which the reputation of individuals affects the decision-making process. This paper presents the following: (i) cooperation can evolve in a four-person case, (ii) the evolution of cooperation becomes difficult as group size increases, even if the effect of reputation is included, and (iii) three kinds of final social states exist. In medium-sized communities, cooperative species can coexist in a stable manner with betrayal species.     

Systematic Monitoring of Male Circumcision Scale-Up in Nyanza,Kenya: Exploratory Factor Analysis of Service Quality Instrument and Performance Ranking

Background

Considerable conceptual and operational complexities related to service quality measurements and variability in delivery contexts of scaled-up medical male circumcision, pose real challenges to monitoring implementation of quality and safety. Clarifying latent factors of the quality instruments can enhance contextual applicability and the likelihood that observed service outcomes are appropriately assessed.

Objective

To explore factors underlying SYMMACS service quality assessment tool (adopted from the WHO VMMC quality toolkit) and; determine service quality performance using composite quality index derived from the latent factors.

Study design

Using a comparative process evaluation of Voluntary Medical Male Circumcision Scale-Up in Kenya site level data was collected among health facilities providing VMMC over two years. Systematic Monitoring of the Medical Male Circumcision Scale-Up quality instrument was used to assess availability of guidelines, supplies and equipment, infection control, and continuity of care services. Exploratory factor analysis was performed to clarify quality structure.

Results

Fifty four items and 246 responses were analyzed. Based on Eigenvalue >1.00 cut-off, factors 1, 2 & 3 were retained each respectively having eigenvalues of 5.78; 4.29; 2.99. These cumulatively accounted for 29.1% of the total variance (12.9%; 9.5%; 6.7%) with final communality estimates being 13.06. Using a cut-off factor loading value of ≥0.4, fifteen items loading on factor 1, five on factor 2 and one on factor 3 were retained. Factor 1closely relates to preparedness to deliver safe male circumcisions while factor two depicts skilled task performance and compliance with protocols. Of the 28 facilities, 32% attained between 90^th and 95^th percentile (excellent); 45% between 50^th and 75^th percentiles (average) and 14.3% below 25^th percentile (poor).

Conclusion

the service quality assessment instrument may be simplified to have nearly 20 items that relate more closely to service outcomes. Ranking of facilities and circumcision procedure using a composite index based on these items indicates that majority performed above average.     

Ranking differential hubs in gene co-expression networks

Identifying the genes that change their expressions between two conditions (such as normal versus cancer) is a crucial task that can help in understanding the causes of diseases. Differential networking has emerged as a powerful approach to detect the changes in network structures and to identify the differentially connected genes among two networks. However, existing differential network-based methods primarily depend on pairwise comparisons of the genes based on their connectivity. Therefore, these methods cannot capture the essential topological changes in the network structures. In this paper, we propose a novel algorithm, DiffRank, which ranks the genes based on their contribution to the differences between the two networks. To achieve this goal, we define two novel structural scoring measures: a local structure measure (differential connectivity) and a global structure measure (differential betweenness centrality). These measures are optimized by propagating the scores through the network structure and then ranking the genes based on these propagated scores. We demonstrate the effectiveness of DiffRank on synthetic and real datasets. For the synthetic datasets, we developed a simulator for generating synthetic differential scale-free networks, and we compared our method with existing methods. The comparisons show that our algorithm outperforms these existing methods. For the real datasets, we apply the proposed algorithm on several gene expression datasets and demonstrate that the proposed method provides biologically interesting results.     

Iso- and Anisoasymmetry Testing in Pre-post Rating Designs

It is suggested to test against anisoasymmetry rather than against marginal homogeneity if (1) a pre-post treatment matched pairs contingency table is ordinally scaled by clinical ratings and if (2) the alternative to marginal homogeneity is a shift in location from pre- to post treatment ratings. Testing against anisoasymmetry may detect treatment effects which are not manifest in marginal inhomogeneities.     

Ranking prognosis markers in cancer genomic studies

In cancer research, high-throughput genomic studies have been extensively conducted, searching for markers associated with cancer diagnosis, prognosis and variation in response to treatment. In this article, we analyze cancer prognosis studies and investigate ranking markers based on their marginal prognosis power. To avoid ambiguity, we focus on microarray gene expression studies where genes are the markers, but note that the methodology and results are applicable to other high-throughput studies. The objectives of this study are 2-fold. First, we investigate ranking markers under three commonly adopted semiparametric models, namely the Cox, accelerated failure time and additive risk models. Data analysis shows that the ranking may vary significantly under different models. Second, we describe a nonparametric concordance measure, which has roots in the time-dependent ROC (receiver operating characteristic) framework and relies on much weaker assumptions than the semiparametric models. In simulation, it is shown that ranking using the concordance measure is not sensitive to model specification whereas ranking under the semiparametric models is. In data analysis, the concordance measure generates rankings significantly different from those under the semiparametric models.     

Statistical Patterns in Movie Rating Behavior

Currently, users and consumers can review and rate products through online services, which provide huge databases that can be used to explore people’s preferences and unveil behavioral patterns. In this work, we investigate patterns in movie ratings, considering IMDb (the Internet Movie Database), a highly visited site worldwide, as a source. We find that the distribution of votes presents scale-free behavior over several orders of magnitude, with an exponent very close to 3/2, with exponential cutoff. It is remarkable that this pattern emerges independently of movie attributes such as average rating, age and genre, with the exception of a few genres and of high-budget films. These results point to a very general underlying mechanism for the propagation of adoption across potential audiences that is independent of the intrinsic features of a movie and that can be understood through a simple spreading model with mean-field avalanche dynamics.     

Neural Correlates of Gender Differences in Reputation Building

Gender differences in cooperative choices and their neural correlates were investigated in a situation where reputation represented a crucial issue. Males and females were involved in an economic exchange (trust game) where economic and reputational payoffs had to be balanced in order to increase personal welfare. At the behavioral level, females showed a stronger reaction to negative reputation judgments that led to higher cooperation than males, measured by back transfers in the game. The neuroanatomical counterpart of this gender difference was found within the reward network (engaged in producing expectations of positive results) and reputation-related brain networks, such as the self-control network (engaged in strategically resisting the temptation to defect) and the mentalizing network (engaged in thinking about how one is viewed by others), in which the dorsolateral prefrontal cortex (DLPFC) and the medial (M)PFC respectively play a crucial role. Furthermore, both DLPFC and MPFC activity correlated with the amount of back transfer, as well as with the personality dimensions assessed with the Big-Five Questionnaire (BFQ-2). Males, according to their greater DLPFC recruitment and their higher level of the BFQ-2 subscale of Dominance, were more focused on implementing a profit-maximizing strategy, pursuing this target irrespectively of others'' judgments. On the contrary, females, according to their greater MPFC activity and their lower level of Dominance, were more focused on the reputation per se and not on the strategic component of reputation building. These findings shed light on the sexual dimorphism related to cooperative behavior and its neural correlates.     

Ranking the role of RANK ligand in apoptosis

Many members of tumor necrosis factor (TNF) superfamily are characterized by their ability to induce apoptosis once they bind in a homotrimeric manner to their cognate receptors. The receptor activator of nuclear factor-kappaB ligand (RANKL), a member of the TNF superfamily identified seven years ago, was originally described as a factor that induced osteoclastogenesis and dendritic cell survival. Recent observations indicate that a growth inhibitory and apoptosis-inducing activity is associated with RANKL, as is the case for other members of TNF superfamily. This review describes the possible mechanisms of induction of RANKL-induced growth inhibition/apoptosis and discusses the role of various components in RANKL-signaling in this phenomenon, including TNF receptor-associated factor (TRAF)-6, nuclear factor-kappaB (NF-kappaB), c-jun N-terminal kinase JNK), phosphatidylinositol-3 kinase (PI3K).     

Ranking analysis of correlation coefficients in gene expressions

Development of statistical methods has become very necessary for large-scale correlation analysis in the current "omic" data. We propose ranking analysis of correlation coefficients (RAC) based on transforming correlation matrix into correlation vector and conducting a "locally ranking" strategy that significantly reduces computational complexity and load. RAC gives estimation of null correlation distribution and an estimator of false discovery rate (FDR) for finding gene pairs of being correlated in expressions obtained by comparison between the ranked observed correlation coefficients and the ranked estimated ones at a given threshold level. The simulated and real data show that the estimated null correlation distribution is exactly the same with the true one and the FDR estimator works well in various scenarios. By applying our RAC, in the null dataset, no gene pairs were found but, in the human cancer dataset, 837 gene pairs were found to have positively correlated expression variations at FDR≤5%. RAC performs well in multiple conditions (classes), each with 3 or more replicate observations.