Local Conventions in Game Play in an Evolving Dual Social Network Framework

People usually perform economic interactions within the social setting of a small group, while they obtain relevant information from a broader source. We capture this feature with a dynamic interaction model based on two separate social networks. Individuals play a coordination game in an interaction network, while updating their strategies using information from a separate influence network through which information is disseminated. In each time period, the interaction and influence networks co-evolve, and the individuals’ strategies are updated through a modified naive learning process. We show that both network structures and players’ strategies always reach a steady state, in which players form fully connected groups and converge to local conventions. We also analyze the influence exerted by a minority group of strongly opinionated players on these outcomes.

A two-step approach for the analysis of hybrids in comparative social policy analysis: a nuanced typology of childcare policies

Typologies have represented an important tool for the development of comparative social policy research and continue to be widely used in spite of growing criticism of their ability to capture the complexity of welfare states and their internal heterogeneity. In particular, debates have focused on the presence of hybrid cases and the existence of distinct cross-national pattern of variation across areas of social policy. There is growing awareness around these issues, but empirical research often still relies on methodologies aimed at classifying countries in a limited number of unambiguous types. This article proposes a two-step approach based on fuzzy-set-ideal-type analysis for the systematic analysis of hybrids at the level of both policies (step 1) and policy configurations or combinations of policies (step 2). This approach is demonstrated by using the case of childcare policies in European economies. In the first step, parental leave policies are analysed using three methods – direct, indirect, and combinatory – to identify and describe specific hybrid forms at the level of policy analysis. In the second step, the analysis focus on the relationship between parental leave and childcare services in order to develop an overall typology of childcare policies, which clearly shows that many countries display characteristics normally associated with different types (hybrids and. Therefore, this two-step approach enhances our ability to account and make sense of hybrid welfare forms produced from tensions and contradictions within and between policies.

Do hens have friends?

Recent interest in positive welfare has encouraged consideration of the formation of socio-positive relationships in farmed species which may provide a means by which to manage positive states. We investigated in detail the existence of dyadic preferential associations in small groups of domestic laying hens. Spatial and temporal associations were examined in two contexts (day activity and evening roosting), within 8 identical pens of 15 laying hens over 8 weeks. Little aggression was observed. Social network analysis was performed to investigate correlations in who associated with whom using weighted degree (number) and binary (presence or absence) data for shared resource areas and proximity to other individuals. No consistent evidence was found for hens actively preferring others in their choice of resource area, or in companion proximity. Perch-roosting positions chosen by the hens were compared with data generated from a random-choice model. Hens showed no position preferences. Most dyads were never observed roosting together and, although some apparently perched together frequently, the low number of nights perching and proportion of nights spent together indicates these findings should be interpreted with caution. Overall, we found no convincing evidence of dyadic preferential relationships expressed by close active and resting proximities. Further work is required to confirm whether these findings hold true in other experimental contexts, are affected by social experience and if they hold in common with the progenitor sub-species. © 2012 Elsevier B.V.

Evaluating the effectiveness of community-based dementia care networks:The Dementia Care Networks' Study

Purpose: The Dementia Care Networks' Study examined the effectiveness of four community-based, not-for-profit dementia networks. The study involved assessing the relationship between the types of administrative and service-delivery exchanges that occurred among the networked agencies and the network members' perception of the effectiveness of these exchanges. Design and Methods: With the use of a case-study method, the evolution, structure, and processes of each network were documented. Social network analysis using a standardized questionnaire completed by member agencies identified patterns of administrative and clinical exchanges among networked agencies. Results: Differences were found between the four networks in terms of their perceptions of service-delivery effectiveness; perceptions of administrative effectiveness did not factor significantly. Exchanges between groups of agencies (cliques) within each of the four networks were found to be more critical than those between individual agencies within each network. Implications: Integration-measured by the types of exchanges within as opposed to across networks-differentiated the four networks studied. This research contributes to our understanding of the use of multiple measures to evaluate the inner workings of service delivery and their impact on elder health and elder health care. Copyright 2005 by The Gerontological Society of America.

Recovering Social Networks from Individual Attributes

One of the most important challenges of network analysis remains the scarcity of reliable information on existing connection structures. This work explores theoretical and empirical methods of inferring directed networks from nodes attributes and from functions of these attributes that are computed for connected nodes. We discuss the conditions, under which an underlying connection structure can be (probabilistically) recovered, and propose a Bayesian recovery algorithm. In an empirical application, we test the algorithm on the data from the European School Survey Project on Alcohol and Other Drugs.

Genetic differentiation across the social transition in a socially polymorphic sweat bee, Halictus rubicundus

Eusociality is widely considered a major evolutionary transition. The socially polymorphic sweat bee Halictus rubicundus, solitary in cooler regions of its holarctic range and eusocial in warmer parts, is an excellent model organism to address this transition, and specifically the question of whether sociality is associated with a strong barrier to gene flow between phenotypically divergent populations. Mitochondrial DNA (COI) from specimens collected across the British Isles, where both solitary and social phenotypes are represented, displayed limited variation, but placed all specimens in the same European lineage; haplotype network analysis failed to differentiate solitary and social lineages. Microsatellite genetic variability was high and enabled us to quantify genetic differentiation among populations and social phenotypes across Great Britain and Ireland. Results from conceptually different analyses consistently showed greater genetic differentiation between geographically distant populations, independently of their social phenotype, suggesting that the two social forms are not reproductively isolated. A landscape genetic approach revealed significant isolation by distance (Mantel test r = 0.622, p

The discursive turn in policy analysis and the validation of policy stories

This paper is concerned with the language of policy documents in the field of health care, and how ‘readings’ of such documents might be validated in the context of a narrative analysis. The substantive focus is on a comparative study of UK health policy documents (N=20) as produced by the various assemblies, governments and executives of England, Scotland, Wales and Northern Ireland during the period 2000-2009. Following an identification of some key characteristics of narrative structure the authors indicate how text-mining strategies allied with features of semantic and network analysis can be used to unravel the basic elements of policy stories and to facilitate the presentation of data in such a way that readers can verify the strengths (and weaknesses) of any given analysis – with regard to claims concerning, say, the presence, absence, or relative importance of key ideas and concepts. Readers can also ‘see’ how the different components of any one story might fit together, and to get a sense of what has been excluded from the narrative as well as what has been included, and thereby assess the reliability and validity of interpretations that have been placed upon the data.

Trusting Groups in Coalition Formation using Social Distance

In environments where distributed team formation is key, and defections are possible, the use of trust as social capital allows social norms to be defied and compared. An agent can use this information, when invited to join a group or collation, to decide whether or not its utility will be increased by joining. In this work a social network approach is used to define and reason about the relationships contained in the agent community. Previous baseline work is extended with two decision making mechanisms. These are compared by simulating an abstract grid-like environment, and preliminary results are reported.

The level playing field: the impact of assessment practice on professional development

Context: The effects of assessment practice on students’ learning are unclear, particularly regarding professional development. Corralling in objective structured clinical examinations (OSCEs) is designed to reduce illicit passing of examination information. Candidates completing an examination are kept secluded until the next cohort of examinees has begun. We used the introduction of corralling as a context in which to explore social influences on examination misconduct, with the aims of improving understanding of the hidden effects of assessment, and evaluating the acceptability of corralling from the student perspective.

Methods: A questionnaire was administered to students corralled post-OSCE for the first time. Eleven semi-structured interviews were subsequently conducted. Questionnaire data were analysed for descriptive statistics and thematic analysis of interview transcripts was carried out.

Results: The questionnaire response rate was 95.4% (251/263). Before corralling, 80.9% (203/251) of students were aware of the sharing of information among peers and 78.5% (197/251) agreed that such misconduct was unprofessional. The majority were in favour of corralling (90.8%, 228/251). Four themes emerged from the semi-structured interviews: the student network versus the individual; assessment-driven culture; the deferring of professionalism, and the ‘level playing field’. Students saw interaction within the student network, on a background of assessment-driven culture, as the key driver in examination misconduct. Conforming to the rules of the social network was prioritised over individual agency, although the mismatch between the rules of the network and the dominant professional discourse caused some conflict for individuals. Deferred professionalism (described as the practice of taking on the norms of professional behaviour only when qualified) was a rationalisation used to minimise this conflict. Corralling provided a ‘level playing field’ in which the influences of the network were minimised.

Conclusions: Examination misconduct is thus a complex social construction with implications for individual learners in terms of professional development. Corralling is one mechanism for addressing misconduct that is acceptable to students, but assessment processes have important hidden effects which educators should acknowledge.

Resolving candidate genes of mouse skeletal muscle QTL via RNA-Seq and expression network analyses

BACKGROUND:

We have recently identified a number of Quantitative Trait Loci (QTL) contributing to the 2-fold muscle weight difference between the LG/J and SM/J mouse strains and refined their confidence intervals. To facilitate nomination of the candidate genes responsible for these differences we examined the transcriptome of the tibialis anterior (TA) muscle of each strain by RNA-Seq.

13,726 genes were expressed in mouse skeletal muscle. Intersection of a set of 1061 differentially expressed transcripts with a mouse muscle Bayesian Network identified a coherent set of differentially expressed genes that we term the LG/J and SM/J Regulatory Network (LSRN). The integration of the QTL, transcriptome and the network analyses identified eight key drivers of the LSRN (Kdr, Plbd1, Mgp, Fah, Prss23, 2310014F06Rik, Grtp1, Stk10) residing within five QTL regions, which were either polymorphic or differentially expressed between the two strains and are strong candidates for quantitative trait genes (QTGs) underlying muscle mass. The insight gained from network analysis including the ability to make testable predictions is illustrated by annotating the LSRN with knowledge-based signatures and showing that the SM/J state of the network corresponds to a more oxidative state. We validated this prediction by NADH tetrazolium reductase staining in the TA muscle revealing higher oxidative potential of the SM/J compared to the LG/J strain (p<0.03).

Thus, integration of fine resolution QTL mapping, RNA-Seq transcriptome information and mouse muscle Bayesian Network analysis provides a novel and unbiased strategy for nomination of muscle QTGs.

Improving Patient Safety Through Formal and Informal Learning Networks

This paper explores a novel perspective on patient safety improvements, which draws on
contemporary social network and learning theories. A case study was conducted at a Portuguese
acute university hospital. Data collection followed a staged approach, whereby 46 interviews
were conducted involving 49 respondents from a broad array of departments and professional
backgrounds. This case study highlights the importance of two major interlinked factors in
contributing to patient safety improvements. The first of these is the crucial role of formal and
informal, internal and external social networks. The second is the importance and the possible
advantage of combining formal and informal learning. The analysis suggests that initiatives
rooted in formal learning approaches alone do not necessarily lead to the creation of long-term
grounded internal safety networks, and that patient safety improvements can crucially depend on
bottom-up initiatives of communities of practice and informal learning. Traditional research on
patient safety places a strong emphasis on top-down and managerialist approaches and is often
based on the assumption that „safety? learning is primarily formal and context-independent. This
paper suggests that bottom-up initiatives and a combination of formal and informal learning can
make a major contribute to patient safety improvements.

Towards a general framework for social media research using big data

The increasing adoption of cloud computing, social networking, mobile and big data technologies provide challenges and opportunities for both research and practice. Researchers face a deluge of data generated by social network platforms which is further exacerbated by the co-mingling of social network platforms and the emerging Internet of Everything. While the topicality of big data and social media increases, there is a lack of conceptual tools in the literature to help researchers approach, structure and codify knowledge from social media big data in diverse subject matter domains, many of whom are from nontechnical disciplines. Researchers do not have a general-purpose scaffold to make sense of the data and the complex web of relationships between entities, social networks, social platforms and other third party databases, systems and objects. This is further complicated when spatio-temporal data is introduced. Based on practical experience of working with social media datasets and existing literature, we propose a general research framework for social media research using big data. Such a framework assists researchers in placing their contributions in an overall context, focusing their research efforts and building the body of knowledge in a given discipline area using social media data in a consistent and coherent manner.

Integrative analysis of chemo-transcriptomic profiles for drug-feature specific gene expression signatures

One of the major challenges in systems biology is to understand the complex responses of a biological system to external perturbations or internal signalling depending on its biological conditions. Genome-wide transcriptomic profiling of cellular systems under various chemical perturbations allows the manifestation of certain features of the chemicals through their transcriptomic expression profiles. The insights obtained may help to establish the connections between human diseases, associated genes and therapeutic drugs. The main objective of this study was to systematically analyse cellular gene expression data under various drug treatments to elucidate drug-feature specific transcriptomic signatures. We first extracted drug-related information (drug features) from the collected textual description of DrugBank entries using text-mining techniques. A novel statistical method employing orthogonal least square learning was proposed to obtain drug-feature-specific signatures by integrating gene expression with DrugBank data. To obtain robust signatures from noisy input datasets, a stringent ensemble approach was applied with the combination of three techniques: resampling, leave-one-out cross validation, and aggregation. The validation experiments showed that the proposed method has the capacity of extracting biologically meaningful drug-feature-specific gene expression signatures. It was also shown that most of signature genes are connected with common hub genes by regulatory network analysis. The common hub genes were further shown to be related to general drug metabolism by Gene Ontology analysis. Each set of genes has relatively few interactions with other sets, indicating the modular nature of each signature and its drug-feature-specificity. Based on Gene Ontology analysis, we also found that each set of drug feature (DF)-specific genes were indeed enriched in biological processes related to the drug feature. The results of these experiments demonstrated the pot- ntial of the method for predicting certain features of new drugs using their transcriptomic profiles, providing a useful methodological framework and a valuable resource for drug development and characterization.

CSFinder: A Cold-Start Friend Finder in Large-Scale Social Networks

Recommending users for a new social network user to follow is a topic of interest at present. The existing approaches rely on using various types of information about the new user to determine recommended users who have similar interests to the new user. However, this presents a problem when a new user joins a social network, who is yet to have any interaction on the social network. In this paper we present a particular type of conversational recommendation approach, critiquing-based recommendation, to solve the cold start problem. We present a critiquing-based recommendation system, called CSFinder, to recommend users for a new user to follow. A traditional critiquing-based recommendation system allows a user to critique a feature of a recommended item at a time and gradually leads the user to the target recommendation. However this may require a lengthy recommendation session. CSFinder aims to reduce the session length by taking a case-based reasoning approach. It selects relevant recommendation sessions of past users that match the recommendation session of the current user to shortcut the current recommendation session. It selects relevant recommendation sessions from a case base that contains the successful recommendation sessions of past users. A past recommendation session can be selected if it contains recommended items and critiques that sufficiently overlap with the ones in the current session. Our experimental results show that CSFinder has significantly shorter sessions than the ones of an Incremental Critiquing system, which is a baseline critiquing-based recommendation system.

Finding Relevant Tweets

When a user of a microblogging site authors a microblog
post or browses through a microblog post, it provides cues as to what
topic she is interested in at that point in time. Example-based search
that retrieves similar tweets given one exemplary tweet, such as the one
just authored, can help provide the user with relevant content. We investigate
various components of microblog posts, such as the associated
timestamp, author’s social network, and the content of the post, and
develop approaches that harness such factors in finding relevant tweets
given a query tweet. An empirical analysis of such techniques on real
world twitter-data is then presented to quantify the utility of the various
factors in assessing tweet relevance. We observe that content-wise similar
tweets that also contain extra information not already present in the
query, are perceived as useful. We then develop a composite technique
that combines the various approaches by scoring tweets using a dynamic
query-specific linear combination of separate techniques. An empirical
evaluation establishes the effectiveness of the composite technique, and
that it outperforms each of its constituents.