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We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning.As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.
Our work presents a foundation for assessing the impact of homophilic and heterophilic behaviour on minorities in social networks.
Social networks are comprised of individuals with a variety of attributes, such as race, age, educational background, or gender.
IBM Research is tackling some of AI's greatest challenges.
Our scientists and engineers focus on fundamental scientific breakthroughs to help guide the advancement of AI.
We devise a social network model with tunable homophily and group sizes, and demonstrate how the degree ranking of nodes from the minority group in a network is a function of (i) relative group sizes and (ii) the presence or absence of homophilic behaviour.
We provide analytical insights on how the ranking of the minority can be improved to ensure the representativeness of the group and correct for potential biases.
It is thus fundamental to understand the effect of group sizes and the different mechanisms of tie formation on the ranking of minorities in social networks.
In this study, we focus on two main mechanisms for the formation of ties: homophily, and systematically study how relative size differences between groups in social networks, with various levels of homophily, impact the ranking of nodes in synthetic and real-world networks.
In recent years, models have been proposed that consider homophily.
We build on these models by systematically exploring the parameter range for homophily and group size differences and offer analytical and empirical evidence on the emergent properties of networks and the ranking of groups.