The Experts below are selected from a list of 2784 Experts worldwide ranked by ideXlab platform
Hongjie Liu - One of the best experts on this subject based on the ideXlab platform.
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can burt s theory of structural holes be applied to study social support among mid age female sex workers a multi site Egocentric Network study in china
Aids and Behavior, 2017Co-Authors: Hongjie LiuAbstract:The epidemic of HIV/AIDS continues to spread among older adults and mid-age female sex workers (FSWs) over 35 years old. We used Egocentric Network data collected from three study sites in China to examine the applicability of Burt's Theory of Social Holes to study social support among mid-age FSWs. Using respondent-driven sampling, 1245 eligible mid-age FSWs were interviewed. Network structural holes were measured by Network constraint and effective size. Three types of social Networks were identified: family Networks, workplace Networks, and non-FSW Networks. A larger effective size was significantly associated with a higher level of social support [regression coefficient (β) 5.43-10.59] across the three study samples. In contrast, a greater constraint was significantly associated with a lower level of social support (β -9.33 to -66.76). This study documents the applicability of the Theory of Structural Holes in studying Network support among marginalized populations, such as FSWs.
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Can Burt’s Theory of Structural Holes be Applied to Study Social Support Among Mid-Age Female Sex Workers? A Multi-Site Egocentric Network Study in China
AIDS and behavior, 2017Co-Authors: Hongjie LiuAbstract:The epidemic of HIV/AIDS continues to spread among older adults and mid-age female sex workers (FSWs) over 35 years old. We used Egocentric Network data collected from three study sites in China to examine the applicability of Burt's Theory of Social Holes to study social support among mid-age FSWs. Using respondent-driven sampling, 1245 eligible mid-age FSWs were interviewed. Network structural holes were measured by Network constraint and effective size. Three types of social Networks were identified: family Networks, workplace Networks, and non-FSW Networks. A larger effective size was significantly associated with a higher level of social support [regression coefficient (β) 5.43-10.59] across the three study samples. In contrast, a greater constraint was significantly associated with a lower level of social support (β -9.33 to -66.76). This study documents the applicability of the Theory of Structural Holes in studying Network support among marginalized populations, such as FSWs.
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serosorting and sexual risk for hiv infection at the ego alter dyadic level an Egocentric sexual Network study among msm in nigeria
Aids and Behavior, 2016Co-Authors: Cristina Rodriguezhart, Hongjie Liu, William A. Blattner, Rebecca G Nowak, Ifeanyi Orazulike, Samuel Zorowitz, Trevor A Crowell, Stefan Baral, Man CharuratAbstract:The objective of this Egocentric Network study was to investigate engagement in serosorting by HIV status and risk for HIV between seroconcordant and serodiscordant ego-alter dyads. Respondent-driving sampling was used to recruit 433 Nigerian men who have sex with men (MSM) from 2013 to 2014. Participant (ego) characteristics and that of five sex partners (alters) were collected. Seroconcordancy was assessed at the ego level and for each dyad. Among 433 egos, 18 % were seroconcordant with all partners. Among 880 dyads where participants knew their HIV status, 226 (25.7 %) were seroconcordant, with 11.7 % of HIV positive dyads seroconcordant and 37.0 % of HIV negative dyads seroconcordant. Seroconcordant dyads reported fewer casual sex partners, less partner concurrency, and partners who had ever injected drugs, but condom use did not differ significantly. Serosorting may be a viable risk reduction strategy among Nigerian MSM, but awareness of and communication about HIV status should be increased. Future studies should assess serosorting on a partner-by-partner basis.
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Egocentric Network and Condom Use Among Mid-Age Female Sex Workers in China: A Multilevel Modeling Analysis
AIDS patient care and STDs, 2016Co-Authors: Hongjie LiuAbstract:Abstract The epidemics of sexually transmitted infections (STIs) have spread among older adults in the world, including China. This study addresses the deficiency of studies about the multiple contextual influences on condom use among mid-age female sex workers (FSWs) over 35 years old. A combination of an Egocentric Network design and multilevel modeling was used to investigate factors of condom use over mid-age FSWs (egos) particular relationships with sexual partners (alters). Of the 1245 mid-age FSWs interviewed, 73% (907) reported having at least one sexual partner who would provide social support to egos. This generated a total of 1300 ego–alter sex ties in egos' support Networks. Condoms were consistently used among one-third of sex ties. At the ego level, condoms were more likely to be used consistently if egos received a middle school education or above, had stronger perceived behavioral control for condom use, or consistently used condoms with other sex clients who were not in their support netw...
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Network stigma towards people living with HIV/AIDS and their caregivers: An Egocentric Network study
Global public health, 2015Co-Authors: Jennifer Guida, Hongjie LiuAbstract:HIV stigma occurs among peers in social Networks. However, the features of social Networks that drive HIV stigma are not well understood. The objective of this study is to investigate anticipated HIV stigma within the social Networks of people living with HIV/AIDS (PLWHA) (N = 147) and the social Networks of PLWHA's caregivers (N = 148). The Egocentric social Network data were collected in Guangxi, China. More than half of PLWHA (58%) and their caregivers (53%) anticipated HIV stigma from their Network peers. Both PLWHA and their caregivers anticipated that spouses or other family members were less likely to stigmatise them, compared to friend peers or other relationships. Married Network peers were believed to stigmatise caregivers more than unmarried peers. The association between frequent contacts and anticipated stigma was negative among caregivers. Being in a close relationship with PLWHA or caregivers (e.g., a spouse or other family member) was associated with less anticipated stigma. Lower Network ...
Florence Sèdes - One of the best experts on this subject based on the ideXlab platform.
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Toward Egocentric Network-based Learner Profiling in Adaptive E-learning Systems: A concept paper
Proceedings of the 2019 7th International Conference on Information and Education Technology - ICIET 2019, 2019Co-Authors: Marie-françoise Canut, André Péninou, Kriangsak Srisombat, Florence SèdesAbstract:Adaptive E-learning systems, which provide personalized learning experiences based on learner's specific characteristics (e.g. knowledge, skills, and competencies), are essential for developing effective learning and teaching. This paper presents a conceptual proposition consisting in developing adaptive E-learning system by incorporating Egocentric Network-based user profiling, an existing contribution of our research team. Actually, this contribution has been proved in the online social Network context with empirical results on different social Networks data (Facebook, Delicious, Twitter and DBLP). We aim to apply the existing algorithms underlying this contribution in E-learning context and present in this work a conceptual model and the construction process of learner's user profile, called "learner profile". The learner profile can be exploited in adaptive E-learning systems to provide different personalized/adapted services (books recommendation, personalized courses search, ...) to the learner.
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A Parametric Study to Construct Time-Aware Social Profiles
Lecture Notes in Social Networks, 2017Co-Authors: Arnaud Quirin, Marie-françoise Canut, André Péninou, Nadine Baptiste-jessel, Florence SèdesAbstract:Online social Networks provide valuable information sources to collect and identify user information and user interests. This work focuses on using information shared on users’ Egocentric Network to extract user’s interests. We propose to apply a time-aware method into an existing social profile building process, which is one of our previous team contributions. This strategy aims at weighting user’s interests in the social profile according to their temporal relevance (temporal score). The temporal score of an interest is computed by combining the temporal score of information used to extract the interests (computed by taking into account their freshness) with the temporal score of individuals who share the information in the Network (computed by taking into account the freshness of the interaction with the user). In this paper, we show results of intensive experiments conducted on scientific publication Networks (DBLP/Mendeley) with presenting a parametric study, comparing the effectiveness of our technique with the time-agnostic technique. We study also the impact of the individual temporal score compared to the information temporal score. The experiments show that our proposition outperforms the existing time-agnostic Egocentric Network-based user profiling process in terms of precision and recall. Furthermore, we found that the individual temporal score has a larger importance than the information temporal score in calculating the final temporal score. This demonstrates that the dynamic links are more important than the dynamic information when using co-author Network data to build the social profile.
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Time-aware Egocentric Network-based User Profiling
2015Co-Authors: Marie-françoise Canut, André Péninou, Florence SèdesAbstract:Improving the Egocentric Network-based user's profile building process by taking into account the dynamic characteristics of social Networks can be relevant in many applications. To achieve this aim, we propose to apply a time-aware method into an existing Egocentric-based user profiling process, based on previous contributions of our team. The aim of this strategy is to weight user's interests according to their relevance and freshness. The time awareness weight of an interest is computed by combining the relevance of individuals in the user's Egocentric Network (computed by taking into account the freshness of their ties) with the information relevance (computed by taking into account its freshness). The experiments on scientific publications Networks (DBLP/Mendeley) allow us to demonstrate the effectiveness of our proposition compared to the existing time-agnostic Egocentric Network-based user profiling process.
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ASONAM - Time-aware Egocentric Network-based User Profiling
Proceedings of the 2015 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining 2015, 2015Co-Authors: Marie-françoise Canut, André Péninou, Florence SèdesAbstract:Improving the Egocentric Network-based user's profile building process by taking into account the dynamic characteristics of social Networks can be relevant in many applications. To achieve this aim, we propose to apply a time-aware method into an existing Egocentric-based user profiling process, based on previous contributions of our team. The aim of this strategy is to weight user's interests according to their relevance and freshness. The time awareness weight of an interest is computed by combining the relevance of individuals in the user's Egocentric Network (computed by taking into account the freshness of their ties) with the information relevance (computed by taking into account its freshness). The experiments on scientific publications Networks (DBLP/Mendeley) allow us to demonstrate the effectiveness of our proposition compared to the existing time-agnostic Egocentric Network-based user profiling process.
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ICDIM - Deriving user's profile from sparse Egocentric Networks: Using snowball sampling and link prediction
Ninth International Conference on Digital Information Management (ICDIM 2014), 2014Co-Authors: Marie-françoise Canut, André Péninou, Florence SèdesAbstract:Several studies demonstrate effectiveness and benefits of using user's social Network information to enrich user's profile. In this context, one of our contributions [1] proposes an algorithm enabling to compute user's interests using information from Egocentric Network extracted communities. Therefore, mining information from a small or a sparse Network remains challenging because there is not enough information to enrich a relevant user's profile. So, one of the main lock is to cope with the lack of information that is considered as an important issue to extract a relevant community and could lead to misinterpretations in the user's profile modeling process. We aim to improve the performance of [1], regarding the lack of information problem, in the case of a small and/or a sparse Network. We propose to add more information (i.e. relations) into user's Network before extracting the data and enriching his profile. To achieve this enrichment, we suggest using snowball sampling technique to identify and add user's distance-2 neighbors (friends of a friend) into the user's Egocentric Network. Our experimentation conducted in DBLP demonstrates the interest of node integration into small and sparse Network. This leads to the study of link prediction that enables us to provide better performances and results compared to the existing work.
André Péninou - One of the best experts on this subject based on the ideXlab platform.
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Toward Egocentric Network-based Learner Profiling in Adaptive E-learning Systems: A concept paper
Proceedings of the 2019 7th International Conference on Information and Education Technology - ICIET 2019, 2019Co-Authors: Marie-françoise Canut, André Péninou, Kriangsak Srisombat, Florence SèdesAbstract:Adaptive E-learning systems, which provide personalized learning experiences based on learner's specific characteristics (e.g. knowledge, skills, and competencies), are essential for developing effective learning and teaching. This paper presents a conceptual proposition consisting in developing adaptive E-learning system by incorporating Egocentric Network-based user profiling, an existing contribution of our research team. Actually, this contribution has been proved in the online social Network context with empirical results on different social Networks data (Facebook, Delicious, Twitter and DBLP). We aim to apply the existing algorithms underlying this contribution in E-learning context and present in this work a conceptual model and the construction process of learner's user profile, called "learner profile". The learner profile can be exploited in adaptive E-learning systems to provide different personalized/adapted services (books recommendation, personalized courses search, ...) to the learner.
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A Parametric Study to Construct Time-Aware Social Profiles
Lecture Notes in Social Networks, 2017Co-Authors: Arnaud Quirin, Marie-françoise Canut, André Péninou, Nadine Baptiste-jessel, Florence SèdesAbstract:Online social Networks provide valuable information sources to collect and identify user information and user interests. This work focuses on using information shared on users’ Egocentric Network to extract user’s interests. We propose to apply a time-aware method into an existing social profile building process, which is one of our previous team contributions. This strategy aims at weighting user’s interests in the social profile according to their temporal relevance (temporal score). The temporal score of an interest is computed by combining the temporal score of information used to extract the interests (computed by taking into account their freshness) with the temporal score of individuals who share the information in the Network (computed by taking into account the freshness of the interaction with the user). In this paper, we show results of intensive experiments conducted on scientific publication Networks (DBLP/Mendeley) with presenting a parametric study, comparing the effectiveness of our technique with the time-agnostic technique. We study also the impact of the individual temporal score compared to the information temporal score. The experiments show that our proposition outperforms the existing time-agnostic Egocentric Network-based user profiling process in terms of precision and recall. Furthermore, we found that the individual temporal score has a larger importance than the information temporal score in calculating the final temporal score. This demonstrates that the dynamic links are more important than the dynamic information when using co-author Network data to build the social profile.
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Time-aware Egocentric Network-based User Profiling
2015Co-Authors: Marie-françoise Canut, André Péninou, Florence SèdesAbstract:Improving the Egocentric Network-based user's profile building process by taking into account the dynamic characteristics of social Networks can be relevant in many applications. To achieve this aim, we propose to apply a time-aware method into an existing Egocentric-based user profiling process, based on previous contributions of our team. The aim of this strategy is to weight user's interests according to their relevance and freshness. The time awareness weight of an interest is computed by combining the relevance of individuals in the user's Egocentric Network (computed by taking into account the freshness of their ties) with the information relevance (computed by taking into account its freshness). The experiments on scientific publications Networks (DBLP/Mendeley) allow us to demonstrate the effectiveness of our proposition compared to the existing time-agnostic Egocentric Network-based user profiling process.
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ASONAM - Time-aware Egocentric Network-based User Profiling
Proceedings of the 2015 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining 2015, 2015Co-Authors: Marie-françoise Canut, André Péninou, Florence SèdesAbstract:Improving the Egocentric Network-based user's profile building process by taking into account the dynamic characteristics of social Networks can be relevant in many applications. To achieve this aim, we propose to apply a time-aware method into an existing Egocentric-based user profiling process, based on previous contributions of our team. The aim of this strategy is to weight user's interests according to their relevance and freshness. The time awareness weight of an interest is computed by combining the relevance of individuals in the user's Egocentric Network (computed by taking into account the freshness of their ties) with the information relevance (computed by taking into account its freshness). The experiments on scientific publications Networks (DBLP/Mendeley) allow us to demonstrate the effectiveness of our proposition compared to the existing time-agnostic Egocentric Network-based user profiling process.
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ICDIM - Deriving user's profile from sparse Egocentric Networks: Using snowball sampling and link prediction
Ninth International Conference on Digital Information Management (ICDIM 2014), 2014Co-Authors: Marie-françoise Canut, André Péninou, Florence SèdesAbstract:Several studies demonstrate effectiveness and benefits of using user's social Network information to enrich user's profile. In this context, one of our contributions [1] proposes an algorithm enabling to compute user's interests using information from Egocentric Network extracted communities. Therefore, mining information from a small or a sparse Network remains challenging because there is not enough information to enrich a relevant user's profile. So, one of the main lock is to cope with the lack of information that is considered as an important issue to extract a relevant community and could lead to misinterpretations in the user's profile modeling process. We aim to improve the performance of [1], regarding the lack of information problem, in the case of a small and/or a sparse Network. We propose to add more information (i.e. relations) into user's Network before extracting the data and enriching his profile. To achieve this enrichment, we suggest using snowball sampling technique to identify and add user's distance-2 neighbors (friends of a friend) into the user's Egocentric Network. Our experimentation conducted in DBLP demonstrates the interest of node integration into small and sparse Network. This leads to the study of link prediction that enables us to provide better performances and results compared to the existing work.
Marie-françoise Canut - One of the best experts on this subject based on the ideXlab platform.
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Toward Egocentric Network-based Learner Profiling in Adaptive E-learning Systems: A concept paper
Proceedings of the 2019 7th International Conference on Information and Education Technology - ICIET 2019, 2019Co-Authors: Marie-françoise Canut, André Péninou, Kriangsak Srisombat, Florence SèdesAbstract:Adaptive E-learning systems, which provide personalized learning experiences based on learner's specific characteristics (e.g. knowledge, skills, and competencies), are essential for developing effective learning and teaching. This paper presents a conceptual proposition consisting in developing adaptive E-learning system by incorporating Egocentric Network-based user profiling, an existing contribution of our research team. Actually, this contribution has been proved in the online social Network context with empirical results on different social Networks data (Facebook, Delicious, Twitter and DBLP). We aim to apply the existing algorithms underlying this contribution in E-learning context and present in this work a conceptual model and the construction process of learner's user profile, called "learner profile". The learner profile can be exploited in adaptive E-learning systems to provide different personalized/adapted services (books recommendation, personalized courses search, ...) to the learner.
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A Parametric Study to Construct Time-Aware Social Profiles
Lecture Notes in Social Networks, 2017Co-Authors: Arnaud Quirin, Marie-françoise Canut, André Péninou, Nadine Baptiste-jessel, Florence SèdesAbstract:Online social Networks provide valuable information sources to collect and identify user information and user interests. This work focuses on using information shared on users’ Egocentric Network to extract user’s interests. We propose to apply a time-aware method into an existing social profile building process, which is one of our previous team contributions. This strategy aims at weighting user’s interests in the social profile according to their temporal relevance (temporal score). The temporal score of an interest is computed by combining the temporal score of information used to extract the interests (computed by taking into account their freshness) with the temporal score of individuals who share the information in the Network (computed by taking into account the freshness of the interaction with the user). In this paper, we show results of intensive experiments conducted on scientific publication Networks (DBLP/Mendeley) with presenting a parametric study, comparing the effectiveness of our technique with the time-agnostic technique. We study also the impact of the individual temporal score compared to the information temporal score. The experiments show that our proposition outperforms the existing time-agnostic Egocentric Network-based user profiling process in terms of precision and recall. Furthermore, we found that the individual temporal score has a larger importance than the information temporal score in calculating the final temporal score. This demonstrates that the dynamic links are more important than the dynamic information when using co-author Network data to build the social profile.
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Time-aware Egocentric Network-based User Profiling
2015Co-Authors: Marie-françoise Canut, André Péninou, Florence SèdesAbstract:Improving the Egocentric Network-based user's profile building process by taking into account the dynamic characteristics of social Networks can be relevant in many applications. To achieve this aim, we propose to apply a time-aware method into an existing Egocentric-based user profiling process, based on previous contributions of our team. The aim of this strategy is to weight user's interests according to their relevance and freshness. The time awareness weight of an interest is computed by combining the relevance of individuals in the user's Egocentric Network (computed by taking into account the freshness of their ties) with the information relevance (computed by taking into account its freshness). The experiments on scientific publications Networks (DBLP/Mendeley) allow us to demonstrate the effectiveness of our proposition compared to the existing time-agnostic Egocentric Network-based user profiling process.
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ASONAM - Time-aware Egocentric Network-based User Profiling
Proceedings of the 2015 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining 2015, 2015Co-Authors: Marie-françoise Canut, André Péninou, Florence SèdesAbstract:Improving the Egocentric Network-based user's profile building process by taking into account the dynamic characteristics of social Networks can be relevant in many applications. To achieve this aim, we propose to apply a time-aware method into an existing Egocentric-based user profiling process, based on previous contributions of our team. The aim of this strategy is to weight user's interests according to their relevance and freshness. The time awareness weight of an interest is computed by combining the relevance of individuals in the user's Egocentric Network (computed by taking into account the freshness of their ties) with the information relevance (computed by taking into account its freshness). The experiments on scientific publications Networks (DBLP/Mendeley) allow us to demonstrate the effectiveness of our proposition compared to the existing time-agnostic Egocentric Network-based user profiling process.
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ICDIM - Deriving user's profile from sparse Egocentric Networks: Using snowball sampling and link prediction
Ninth International Conference on Digital Information Management (ICDIM 2014), 2014Co-Authors: Marie-françoise Canut, André Péninou, Florence SèdesAbstract:Several studies demonstrate effectiveness and benefits of using user's social Network information to enrich user's profile. In this context, one of our contributions [1] proposes an algorithm enabling to compute user's interests using information from Egocentric Network extracted communities. Therefore, mining information from a small or a sparse Network remains challenging because there is not enough information to enrich a relevant user's profile. So, one of the main lock is to cope with the lack of information that is considered as an important issue to extract a relevant community and could lead to misinterpretations in the user's profile modeling process. We aim to improve the performance of [1], regarding the lack of information problem, in the case of a small and/or a sparse Network. We propose to add more information (i.e. relations) into user's Network before extracting the data and enriching his profile. To achieve this enrichment, we suggest using snowball sampling technique to identify and add user's distance-2 neighbors (friends of a friend) into the user's Egocentric Network. Our experimentation conducted in DBLP demonstrates the interest of node integration into small and sparse Network. This leads to the study of link prediction that enables us to provide better performances and results compared to the existing work.
Carl A Latkin - One of the best experts on this subject based on the ideXlab platform.
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The Role of Geographic and Network Factors in Racial Disparities in HIV Among Young Men Who have Sex with Men: An Egocentric Network Study
AIDS and Behavior, 2015Co-Authors: Brian Mustanski, Lisa M. Kuhns, Michelle Birkett, Carl A Latkin, Stephen Q. MuthAbstract:The objective of this study was to characterize and compare individual and sexual Network characteristics of Black, White, and Latino young men who have sex with men (YMSM) as potential drivers of racial disparities in HIV. Egocentric Network interviews were conducted with 175 diverse YMSM who described 837 sex partners within 167 sexual-active egos. Sexual partner alter attributes were summarized by ego. Descriptives of ego demographics, sexual partner demographics, and Network characteristics were calculated by race of the ego and compared. No racial differences were found in individual engagement in HIV risk behaviors or concurrent sexual partnership. Racial differences were found in partner characteristics, including female gender, non-gay sexual orientations, older age, and residence in a high HIV prevalence neighborhood. Racial differences in relationship characteristics included type of relationships (i.e., main partner) and strength of relationships. Network characteristics also showed differences, including sexual Network density and assortativity by race. Most racial differences were in the direction of effects that would tend to increase HIV incidence among Black YMSM. These data suggest that racial disparities in HIV may be driven and/or maintained by a combination of racial differences in partner characteristics, assortativity by race, and increased sexual Network density, rather than differences in individual’s HIV risk behaviors.
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The analysis of multiple ties in longitudinal Egocentric Network data: A case study on bidirectional relationships between trust and drug use.
Social networks, 2012Co-Authors: W. Scott Comulada, Stephen Q. Muth, Carl A LatkinAbstract:We extend multi-level models to examine single Egocentric Network ties to the joint analysis of paired dynamic ties. Two analytic challenges are addressed. First, inference needs to account for multiple layers of nesting: ties are nested within pairs, pairs are nested within time points, and time points are nested within egos. Second, the focus is on the relationship between two dynamic ties; specification of outcome and predictor may be difficult. Instead, we treat both ties as outcomes. Our approach is used to analyze trust and reported drug use between egos and alters over time in a Bayesian framework.