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1. Cooperation and the social brain hypothesis in primate social networks

   https://doi.org/10.3389/fcpxs.2023.1344094  

   MacLaren, Neil G; Meng, Lingqi; Collier, Melissa; Masuda, Naoki (January 2024, Frontiers in Complex Systems)

   Min, Byungjoon (Ed.)

   The social brain hypothesis posits that species with larger brains tend to have greater social complexity. Various lines of empirical evidence have supported the social brain hypothesis, including evidence from the structure of social networks. Cooperation is a key component of group living, particularly among primates, and theoretical research has shown that particular structures of social networks foster cooperation more easily than others. Therefore, we hypothesized that species with a relatively large brain size tend to form social networks that better enable cooperation. In the present study, we combine data on brain size and social networks with theory on the evolution of cooperation on networks to test this hypothesis in non-human primates. We have found a positive effect of brain size on cooperation in social networks even after controlling for the effect of other structural properties of networks that are known to promote cooperation. 

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2. Trust in Social Relations

   https://doi.org/10.1146/annurev-soc-082120-082850  

   Schilke, Oliver; Reimann, Martin; Cook, Karen S. (July 2021, Annual Review of Sociology)

   null (Ed.)

   Trust is key to understanding the dynamics of social relations, to the extent that it is often viewed as the glue that holds society together. We review the mounting sociological literature to help answer what trust is and where it comes from. To this end, we identify two research streams—on particularized trust and generalized trust, respectively—and propose an integrative framework that bridges these lines of research while also enhancing conceptual precision. This framework provides the springboard for identifying several important avenues for future research, including new investigations into the radius of trust, the intermediate form of categorical trust, and the interrelationships between different forms of trust. This article also calls for more scholarship focusing on the consequences (versus antecedents) of trust, addressing more fully the trustee side of the relation, and employing new empirical methods. Such novel approaches will ensure that trust research will continue to provide important insights into the functioning of modern society in the years to come. Expected final online publication date for the Annual Review of Sociology, Volume 47 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

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3. Who Puts the "Social" in "Social Computing"?: Using A Neurodiversity Framing to Review Social Computing Research

   https://doi.org/10.1145/3711106  

   Baillargeon, Philip; Yoon, Jina; Zhang, Amy (May 2025, Proceedings of the ACM on Human-Computer Interaction)

   Human-Computer Interaction (HCI) and Computer Supported Collaborative Work (CSCW) have a longstanding tradition of interrogating the values that underlie systems in order to create novel and accessible experiences. In this work, we use a neurodiversity framing to examine how people with ways of thinking, speaking, and being that differ from normative assumptions are perceived by researchers seeking to study and design social computing systems for neurodivergent people. From a critical analysis of 84 publications systematically gathered across a decade of social computing research, we determine that research into social computing with neurodiverse participants is largely medicalized, adheres to historical stereotypes of neurodivergent children and their families, and is insensitive to the wide spectrum of neurodivergent people that are potential users of social technologies. When social computing systems designed for neurodivergent people rely upon a conception of disability that restricts expression for the sake of preserving existing norms surrounding social experience, the result is often simplistic and restrictive systems that prevent users from being social in a way that feels natural and enjoyable. We argue that a neurodiversity perspective informed by critical disability theory allows us to engage with alternative forms of sociality as meaningful and desirable rather than a deficit to be compensated for. We conclude by identifying opportunities for researchers to collaborate with neurodivergent users and their communities, including the creation of spectrum-conscious social systems and the embedding of double empathy into systems for more equitable design. 

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4. Moving beyond social categories by incorporating context in social psychological theory

   https://doi.org/10.1038/s44159-022-00079-3  

   Cikara, Mina; Martinez, Joel E.; Lewis, Neil A. (September 2022, Nature Reviews Psychology)
5. Representation Learning in Heterogeneous Professional Social Networks with Ambiguous Social Connections.

   Shi, Baoxu; Yang, Jaewon; Weninger, Tim; He, Qi (January 2019, IEEE Conference on Big Data)

   Network representations have been shown to improve performance within a variety of tasks, including classification, clustering, and link prediction. However, most models either focus on moderate-sized, homogeneous networks or require a significant amount of auxiliary input to be provided by the user. Moreover, few works have studied network representations in real-world heterogeneous social networks with ambiguous social connections and are often incomplete. In the present work, we investigate the problem of learning low-dimensional node representations in heterogeneous professional social networks (HPSNs), which are incomplete and have ambiguous social connections. We present a general heterogeneous network representation learning model called Star2Vec that learns entity and person embeddings jointly using a social connection strength-aware biased random walk combined with a node-structure expansion function. Experiments on LinkedIn's Economic Graph and publicly available snapshots of Facebook's network show that Star2Vec outperforms existing methods on members' industry and social circle classification, skill and title clustering, and member-entity link predictions. We also conducted large-scale case studies to demonstrate practical applications of the Star2Vec embeddings trained on LinkedIn's Economic Graph such as next career move, alternative career suggestions, and general entity similarity searches. 

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