Morality, responsibility and risk: Gay men and proximity to HIV. London

Network analysis and network theory have emerged in various strands of research on HIV. Epidemiological research has used network analysis to map and predict the course of the HIV and STI epidemics among Gay men (Doherty et al. 2005; Piqueira et al. 2004). It constitutes an advance on cruder epidemiological models of random mixing (see Keeling & Eames 2005). Social network analysis and social attachment have been important concepts in informing more recent HIV prevention interventions (see Fernandez et al. 2003; Latkin and Knowlton 2005) and have had specific applications in the case of disadvantaged communities such as injecting drug users and sex workers (Latkin et al. 2003; Rhodes et al. 2005). Moreover, network analysis has been useful in understanding social support of disadvantaged groups living with HIV such as African migrants and ethnic minority women (Hough et al. 2005; Asander et al. 2004; Sivaram et al. 2005). Considering a Gay man as part of a social network involves engaging with the social and cultural factors that shape his experience. Rather than thinking of his relationships as essentially random, we characterise them as being profoundly influenced by his social environment; an environment made up by other individuals who share common understandings and social norms. This social network is generally self-perpetuating and limited. Individuals come into contact and hence derive friends and partners from this finite network. Network analysis is especially valuable when examining a population that is highly heterogeneous and made up of individuals who enter that population as autonomous adults. Gay men are such a population being made up of socially mobile individuals deriving from a range of social, ethnic and geographical backgrounds. Social networks are central to our understanding of the dynamics of HIV risk among Gay men. The nature and density of social networks have been found to be connected to sexual risk practices and susceptibility to HIV infection in Gay men (Smith et al. 2004). Moreover, networks influence Gay men’s perceptions and understandings of the HIV epidemic (Grierson 2005). In addition, social networks may have a role in influencing an individuals’ knowledge and understandings of, and access to new technologies such as PEP (see Dodds & Hammond 2006; Korner et al. 2005). Social norms have been found to be important in influencing Gay men’s attitudes towards safer sex and risk-taking especially among groups that have been traditionally disempowered or marginalised such as young Gay men (see Amirkhanian et al. 2005a) and Black/ethnic minority Gay men (see Wilson et al. 2002; Peterson et al. 2003; Zea et al. 2005). Finally social network analysis has been useful in describing social support for Gay men living with HIV and their carers (Shippy et al. 2003; White and Cant 2003; Cant 2004; Zea et al. 2005). A range of HIV prevention interventions have been based around social networks and innovation diffusion theory (see Amirkhanian et al. 2005a). Such interventions would seem to have most salience with disadvantaged groups of Gay and Bisexual men and have achieved some success (see Amirkhanian et al. 2005b). Other authors point out the limitations of network interventions in reaching men at relatively low risk or stress limitations in their efficacy over time (see Martin et al. 2003). Findings from the 2003 Gay Men’s Sex Survey (GMSS) highlight the importance of proximity to HIV. That is, men in certain social and cultural networks had limited experience of HIV in their social network and these men tended to have greater HIV prevention need (see Reid et al. 2004). GMSS 2003 established a range of indicators to measure personal and social proximity to the epidemic. These included: • Having tested for HIV. • Not having tested positive, but believing you are or could be infected. • Being in or having had a sero-discordant relationship. • Personally knowing someone with HIV. At the population level proximity to HIV was mediated by a range of demographic factors. • Area of residence: Men resident in London had greater proximity than men resident elsewhere, although men with low proximity to HIV were present in every city and town and in every area of the UK. • Age: Men in their 30's and 40's had greater proximity than either older or younger men. • Ethnicity: Black men and White men of ethnicities other than British had greater proximity than men in other ethnic groups. • Education: Better educated men had greater proximity (even though less well educated men were more likely to have HIV). • Income: Men in higher income brackets had greater proximity than men in lower income brackets. • Gender of sexual partners: Exclusively homosexually active men had greater proximity to HIV than men who were behaviourally bisexual. • Numbers of male partners: Men with greater numbers of partners had greater proximity than men with fewer partners. While these differences are important it is essential to note that they denote difference at the population level. In fact, there are men with low proximity to HIV in every city and town in the UK (including London); in every age group and ethnic group; with every level of formal education and at every income level; and with a range of sexual identities and sexual practices. These population differences in proximity to HIV present an interesting health promotion dilemma. Those men with greatest proximity have less unmet needs but are more likely to be involved in HIV exposure. Those with less proximity have the greatest unmet need and will therefore be vulnerable if they do come into contact with HIV (either knowingly or unknowingly) but they are probably less likely to do so. In response, the original research recommends “a diverse portfolio of interventions that are encountered by men with a wide variety of relationships to HIV” (Reid et al. 2004). The study presented in this report is in response to these findings. That is, a qualitative examination of social proximity to the epidemic among Gay men. However, we must start with a caveat. Neither GMSS nor this study measures actual proximity to HIV, that is the numbers of social and sexual contacts an individual has who are actually HIV positive, or the percentages of a social network who are actually positive. Rather, GMSS sets up a range of proxy markers to indicate proximity (such as testing history, beliefs about one’s own status and beliefs about the HIV status of social and sexual partners). Likewise, this study measures perceptions of proximity to the epidemic rather than actual proximity (to study actual proximity would require an ambitious network analysis where we recruited all the social and sexual contacts of respondents and asked them about their actual or known HIV status). Studying men’s perceptions of their proximity to the epidemic allows us to examine the ways in which men’s perceptions of their social surroundings influence how they experience and negotiate sexual risk. Moreover, an individual’s perception of the world around him influences the types of information and messages he is likely to notice. The purpose of this study is to inform the nature of interventions targeting men based on their perceived proximity to the epidemic. We will do so by exploring how their perceptions of proximity influence management of HIV-related sexual risk among men who assume or know themselves to be HIV negative

Constructing personal networks in light of COVID-19 containment measures

The policies for containing the spread of the SARS-CoV2 virus include a number of measures aimed at reducing physical contacts. In this paper, we explore the potential impact of such containment measures on social relations of both young adults and the elderly in Italy. We propose two ego-centered network definitions accounting for physical distance in light of the COVID-19 containment measures: the easy-to-reach network, that represents an accessible source of support that can be activate in case of new lockdown; the accustomed-to-reach network, which includes proximity and habit to meet in person. The approach used for constructing personal (ego-centered) networks on data from the most recent release of Families and Social Subject survey allows us to bring to the foreground people exposed to relational vulnerability. The analysis of the most vulnerable individuals by age, gender, and place of residence reveals that living alone is often associated with a condition of relational vulnerability for both the elderly and for young adults

Social capital and health: the problematic roles of social networks and social surveys

Social capital, social networks, social support and health have all been linked, both theoretically and empirically. However, the relationships between them are far from clear. Surveys of social capital and health often use measures of social networks and social support in order to measure social capital, and this is problematic for two reasons. First, theoretical assumptions about social networks and social support being part of social capital are contestable. Second, the measures used inadequately reflect the complexity and ambivalence of social relationships, often assuming that all social ties and contacts are of similarly value, are mutually reinforcing, and, in some studies, are based on neighbourhoods. All these assumptions should be questioned. Progress in our understanding requires more qualitative research and improved choice of indicators in surveys; social network analysis may be a useful source of methodological and empirical insight

A survey on Human Mobility and its applications

Human Mobility has attracted attentions from different fields of studies such as epidemic modeling, traffic engineering, traffic prediction and urban planning. In this survey we review major characteristics of human mobility studies including from trajectory-based studies to studies using graph and network theory. In trajectory-based studies statistical measures such as jump length distribution and radius of gyration are analyzed in order to investigate how people move in their daily life, and if it is possible to model this individual movements and make prediction based on them. Using graph in mobility studies, helps to investigate the dynamic behavior of the system, such as diffusion and flow in the network and makes it easier to estimate how much one part of the network influences another by using metrics like centrality measures. We aim to study population flow in transportation networks using mobility data to derive models and patterns, and to develop new applications in predicting phenomena such as congestion. Human Mobility studies with the new generation of mobility data provided by cellular phone networks, arise new challenges such as data storing, data representation, data analysis and computation complexity. A comparative review of different data types used in current tools and applications of Human Mobility studies leads us to new approaches for dealing with mentioned challenges

Strategic Knowledge Measurement and Management

Knowledge and intellectual capital are now recognized as vital resources for organizational survival and competitive advantage. A vast array of knowledge measures has evolved, spanning many disciplines. This chapter reviews knowledge measures focusing on groups of individuals (such as teams, business and organizations), as they reflect the stock or flow of knowledge, as well as enabling processes that enhance knowledge stocks and flows. The chapter emphasizes the importance of organizational value chains, pivotal talent pools and the link between knowledge and competitive success, in understanding the significance of today’s knowledge measures, and opportunities for future research and practice to enhance them

Gravity-Inspired Graph Autoencoders for Directed Link Prediction

Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods. In particular, graph AE and VAE were successfully leveraged to tackle the challenging link prediction problem, aiming at figuring out whether some pairs of nodes from a graph are connected by unobserved edges. However, these models focus on undirected graphs and therefore ignore the potential direction of the link, which is limiting for numerous real-life applications. In this paper, we extend the graph AE and VAE frameworks to address link prediction in directed graphs. We present a new gravity-inspired decoder scheme that can effectively reconstruct directed graphs from a node embedding. We empirically evaluate our method on three different directed link prediction tasks, for which standard graph AE and VAE perform poorly. We achieve competitive results on three real-world graphs, outperforming several popular baselines.Comment: ACM International Conference on Information and Knowledge Management (CIKM 2019