Purpose: We adapted egocentric social networks as an approach to evaluate the impact of a community-driven systems-level intervention designed to bridge community power with organizational power to root and grow a local food system that works for all. The intervention is informed by Lin’s (1999) theory of social capital and by Lukes’ theory of multidimensional power (2021). Both highlight that change, including at the system level, is a relational phenomenon accelerated or delayed by access to power. This analysis examines baseline social networks and existing power relationships among local food justice leaders participating in the intervention.

Methods: This analysis is based on 38 egocentric social network maps collected from food justice leaders between September 2022 and January 2025 in Cleveland, OH, USA. We conducted a latent profile analysis of seven key social network metrics for each participant, including measures of size, density, and centrality. Descriptive analysis of different types of power within the food system and access to powerful actors related to local food systems change were analyzed for each typology described in the latent profile analysis.

Contributions: To our knowledge, results provide the first description of power structures shaping local systems change necessary for realizing nutrition equity. Findings highlight variability in perceptions of power influencing local food systems. The social network typologies identified will serve as a foundation for examining how initial network attributes change over time and influence access to power sources (e.g., decision makers, gatekeepers, people with resources, influencers) among local food justice leaders innovating transformative change.

The shift from traditional media into the digital media brings into question how collective memory will be shaped in the digital environment, social media platform as space where people meet, exchange and interact become not only a vehicle of communication but a vehicle for recognition transformative power to easily change traditionals ways of communication can help shaping our collective remembering. The process of these exchanges are in a fluid flow, traversing the digital and seamlessly spilling into the physical, where they continuously relate and reshape both

individual and collective memory. This is a process not linear but one of continual nature which blurs the boundaries between the virtual and corporeal to facilitate an unceasing re-territorialisation of memory: thus estranging all static contiguity points. .

Memory, in this sense, emerges as a multiplicity—perpetually in motion and flux.

Personal narratives with transitional layers are deeply connected to other narratives such as family histories, news, politics, and sports. These interactions create a dynamic composition that includes multiple nuances, rhythms, and patterns by happening on several levels. The parts interact continuously, creating complex and fluid mixes that

represent the complexity and interconnection of lived experiences that are the result of a future-oriented present and past experiences that influence the present through ongoing processes of forgetting and remembering.

Thus, utilizing a map enriched with visuals from a three-year, long-term collaborative ethnography with Turkish university students aged 18 to 23—who inhabit social media not as outsiders, but as natives of its realm—this research goes beyond mere analysis of governance frameworks or the influence of technology on collective memories. It looks into the complexities of collective memory in the digital age, not by strictly categorizing social structures, technology, or the actors who shape or challenge collective narratives, but by examining these topics through the lenses of proximity,

relationships, and transitions within these categories. It addresses a multifaceted set of questions related to security, home, family, love, success, violence, power, and responsibility to understand not just the formation of memory but to trace its underlying patterns and connections which is related to social media. It engages in a nuanced exploration of how people employ complex ways and subtle maneuvers to forge connections in online and offline spaces in order to maintain safety and authenticity in social and political contexts where trust and accuracy are compromised, and how these connections are intertwined with remembering and forgetting, shedding light on the broader implications for collective memory formation and disruption.

Background and Aims: Connecting for Care is transdisciplinary social network analysis (SNA) study in Canada. It examines ties among families, health care providers, researchers and other key groups involved in child development and rehabilitation. This presentation describes our team’s research co-design and co-production process, emphasizing our SNA data interpretation approach, which integrates Western and Indigenous knowledge and perspectives.

Methods: Contributions of our community team members (family and Indigenous partners) at every stage of the research process shaped our study design, data gathering, analysis, interpretation and knowledge mobilization significantly. Indigenous team members supported the interpretation of network visualizations using cultural symbolism.

Results: Overlays of our conventional network visualizations were transformed into symbolic network images. Participant groups’ nodes were converted into symbols from nature (e.g., flowers [love], juniper [medicine], branches [growth]). The peripheral network structure represents the outer support circle surrounding children with exceptionalities and their families. The core structure represents protection [cedar bed] where Bear [caregiver] and Cub [child] have a soft place to land. This imagery echos themes from qualitative interviews, which identified relationships and trust as key to strong knowledge exchange for families. Overall, the Indigenist network graph symbolizes possibility, which is created when individuals come together to share and apply knowledge for a collective good.

Conclusion: Weaving together diverse viewpoints allowed us to represent and share our findings in a meaningful way for non-SNA experts while highlighting the study’s key findings. For sense-making, the symbolism’s foundation in nature makes it relatable/accessible to many cultures around the world.

Community energy initiatives (CEIs) throughout Europe struggle with low participation numbers, hindering the attainment of sustainability goals and exacerbating socioeconomic divides. While research attending to community and social network characteristics seems promising for designing intervention strategies (Geskus et al., 2024; Goedkoop, 2021; Middlemiss et al., 2024; Nientimp et al., 2024), translating findings into practice remains challenging. Current research lacks stakeholder input, leaving local insights underutilized so that stakeholders often feel that intervention strategies are imposed on them, reducing effectiveness. To address this, we collaborated with three Dutch CEIs to start learning communities organized in four sessions:

1: Discuss the socioeconomic composition of the community and reflect on initiators in relation to the rest of the community.

2: Map the local social network.

3: The research team conducts an empirical network and survey study. Collectively, the empirical results are compared to the outcomes of the first two sessions.

4: Insights are turned into an intervention strategy.

The participatory modelling process is constantly evaluated by both researchers and initiators, and everything is captured in a protocol accessible to other community energy projects. Hence, this study promises to boost participation and offers valuable insights for other communities. Comparing initiators' perceived (cognitive) networks with empirical data can reveal biases in network perception and their impact on participation and collective action. We will report on the outcomes of the project relating to the literature on stakeholder engagement in environmental SNA, literature on social network perception and direct policy implications.

Creators on social media platforms are increasingly engaging in collaborative content creation. Given the importance of integrating diverse perspectives and expertise from different domains to foster innovation, this study aims to explore the conditions that drive heterogeneity in collaboration relationships, particularly in the form of cross-domain collaboration relationships. Specifically, we investigate how content-related and influence-related factors, such as content diversity and influencer status, affect the formation of cross-domain collaboration relationships. Our data were collected from Bilibili, one of the largest Chinese video-sharing social media platforms, which offers a joint submission feature allowing multiple creators to publish their collaboratively generated videos. We employ exponential random graph models (ERGMs) to analyze the formation of a collaboration network comprising 2,499 creators. The findings indicate that creators from different content domains are less likely to form collaboration relationships than those within the same domain. Furthermore, creators with greater content diversity are more inclined to form cross-domain collaboration relationships. Interestingly, creators with individual influencer status are more likely, while creators with institutional influencer status are less likely, to conduct cross-domain collaboration compared to those without influencer status. While previous research emphasizes the homophily effect in network formation, this analysis deepens our understanding of the conditions that promote heterogeneity in social networks and paves avenues for future research on the effectiveness of these heterogeneous relationships.

Identification of key nodes in complex networks is the driving force that controls or informs of the situation of such a network. Most real-world network systems are shown to be graphs of weighted networks (i.e. networks with link-weights and/or node-weights). In this paper, considerations are given to the weighted traditional centrality measures of degree, betweenness and closeness of graphs and their shortcomings identified/addressed in comparism with hybridised centrality measures which combined isolated centrality (with the traditional centralities), for the purpose of identifying the most influential nodes. This has a good impact on the dissemination of information and helps with the identification of the subgraphs/cliques and in turn the community structure. However, despite some few disadvantages of these hybridised centrality methods, such as high cost of computation and reliance on tuning parameters, the new method is seen to have an improvement of close to 50% over the traditional methods.

Purpose: This study employs a novel, theoretically-driven social network analysis methodology to capture the complex, non-traditional kinship experiences of queer and trans adults. We examine how chosen family ties may differ from other relationship ties regarding closeness, social support, and mental health among queer and trans individuals.

Data: We use two egocentric network datasets: (1) Queer and Trans Vietnamese American Advocates Network (QTVAAN; N=38 egos, 628 alters), and (2) LGBTQ+ Social Networks, Aging, and Policy Study (QSNAPS; Wave 3; N=981 egos, 13,412 alters).

Methods: We use descriptive statistics to identify group differences in closeness, relationship length, and social support across chosen family, family of origin, and friend only relationship ties; and multi-level mixed-effects negative binomial regression models (alter-level characteristics nested within ego outcomes) to assess the impact of percent of chosen family ties within a network on mental health.

Findings: In both datasets, "chosen family" ties represent ~10% of all ties and were predominantly co-identified as “friends”; they were long-term, averaging 15-20 years. Egos in both datasets reported the highest proportions of feeling close to chosen family alters (at least 70% of these ties considered very close). There were no differences in providing instrumental support by relationship type in both datasets. Mixed-effects model results showed non-significant associations between percent of chosen family ties in network with ego distress, social anxiety, and social well-being among QTVAAN respondents. Despite the null effects on mental health outcomes, there are underlying social dynamics of long-term closeness among chosen family ties that warrant further investigation.