Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative ltering methods. However, most existing models assume that social e ects from friend users are static and under the forms of constant weights or xed constraints. To relax this strong assumption, in this paper, we propose dual graph a ention networks to collaboratively learn representations for two-fold social e ects, where one is modeled by a user-speci c a ention weight and the other is modeled by a dynamic and context-aware a ention weight. We also extend the social e ects in user domain to item domain, so that information from related items can be leveraged to further alleviate the data sparsity problem. Furthermore, considering that di erent social e ects in two domains could interact with each other and jointly inuence users' preferences for items, we propose a new policy-based fusion strategy based on contextual multi-armed bandit to weigh interactions of various social e ects. Experiments on one benchmark dataset and a commercial dataset verify the e cacy of the key components in our model. e results show that our model achieves great improvement for recommendation accuracy compared with other state-of-the-art social recommendation methods.

Animal societies are shaped both by social processes and by the physical environment in which social interactions take place. While many studies take the observed patterns of inter-individual interactions as products and proxies of pure social processes, or as links between resource availability and social structure, the role of the physical configuration of habitat features in shaping the social system of group-living animals remains largely overlooked. We hypothesise that by shaping the decisions about when and where to move, physical features of the environment will impact which individuals more frequently encounter one another and in doing so the overall social structure and social organization of populations. We first discuss how the spatial arrangement of habitat components (i.e. habitat configuration) can shape animal movements using empirical cases in the literature. Then, we draw from the empirical literature to discuss how movement patterns of individuals mediate the patterns of social interactions and social organization and highlight the role of network-based approaches in identifying, evaluating and partitioning the effects of habitat configuration on animal social structure or organization. We illustrate the combination of these mechanisms using a simple simulation. Finally, we discuss the implications of habitat configuration in shaping the ecology and evolution of animal societies and offer a framework for future studies. We highlight future directions for studies in animal societies that are increasingly important in widely human-modified landscapes, in particular the implications of habitat-driven social structure in evolution. Significance statement There is now clear evidence that simple processes can generate apparent complex patterns of social structure. However, while studies such as those on collective behaviour and social networks have been focused on processes involving individual decisionmaking, broader patterns of social structure and social organization can also be shaped by factors that have more fundamental impacts on the movements of animals. One set of those factors is related to the amount and spatial arrangement of both biotic and abiotic components of the habitat in which animals live. Examples include the configuration formed by habitat patches connected through corridors, by the presence of hard boundaries between habitat types or by the uneven distribution of resources, mates and competitors across space. In this contribution, we highlight the potential effects of these, which are becoming increasingly important as studies start being able to track populations spanning larger landscapes.

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1.The social decisions that individuals make, in terms of where to move, who to interact with and how frequently, scale up to generate social structure. Such structure has profound consequences: individuals each have a unique social environment, social interactions can amplify or dampen individual differences at the population level, and population-level ecological and evolutionary processes can be governed by higher-level ‘emergent properties’ of animal societies.2.Here we review how explicitly accounting for social structure in animal populations has generated new hypotheses and has revised existing predictions in ecology and evolution. That is, we synthesize the insights gained by applying ‘network-thinking’ rather than the utility of applying social network analysis as a methodological tool. 3.We start with what has been learned about the generative mechanisms that underpin social structure. We then outline the major implications that social structure has been found to have on population processes, on how selection operates and organisms can evolve, and on co-evolutionary dynamics between social structure and population processes. Finally, we highlight areas for which there is clear evidence that accounting for social structure will refine current thinking, but where examples remain scarce.4.Applying ‘network thinking’ in biology presents not only new challenges, but also many opportunities to advance different areas of research. Addressing the question of how social structure changes the biological relationships linking individuals to populations, and populations to processes, is revealing commonalities across scientific disciplines. In doing so, animal social networks can bridge otherwise disparate research topics and, in the future, we hope will allow for more unified theories in biology.