We show that the classification performance of graph convolutional networks (GCNs) is related to the alignment between features, graph, and ground truth, which we quantify using a subspace alignment measure (SAM) corresponding to the Frobenius norm of the matrix of pairwise chordal distances between three subspaces associated with features, graph, and ground truth. The proposed measure is based on the principal angles between subspaces and has both spectral and geometrical interpretations. We showcase the relationship between the SAM and the classification performance through the study of limiting cases of GCNs and systematic randomizations of both features and graph structure applied to a constructive example and several examples of citation networks of different origins. The analysis also reveals the relative importance of the graph and features for classification purposes.

Large metropolitan cities bring together diverse individuals, creating opportunities for cultural and intellectual exchanges, which can ultimately lead to social and economic enrichment. In this work, we present a novel network perspective on the interconnected nature of people and places, allowing us to capture the social diversity of urban locations through the social network and mobility patterns of their visitors. We use a dataset of approximately 37K users and 42K venues in London to build a network of Foursquare places and the parallel Twitter social network of visitors through check-ins. We define four metrics of the social diversity of places which relate to their social brokerage role, their entropy, the homogeneity of their visitors and the amount of serendipitous encounters they are able to induce. This allows us to distinguish between places that bring together strangers versus those which tend to bring together friends, as well as places that attract diverse individuals as opposed to those which attract regulars. We correlate these properties with wellbeing indicators for London neighbourhoods and discover signals of gentrification in deprived areas with high entropy and brokerage, where an influx of more affluent and diverse visitors points to an overall improvement of their rank according to the UK Index of Multiple Deprivation for the area over the five-year census period. Our analysis sheds light on the relationship between the prosperity of people and places, distinguishing between different categories and urban geographies of consequence to the development of urban policy and the next generation of socially-aware location-based applications.

Abstract This research draws on longitudinal network data from an online community to examine patterns of users' behavior and social interaction, and infer the processes underpinning dynamics of system use. The online community represents a prototypical example of a complex evolving social network in which connections between users are established over time by online messages. We study the evolution of a variety of properties since the inception of the system, including how users create, reciprocate, and deepen relationships with one another, variations in users' gregariousness and popularity, reachability and typical distances among users, and the degree of local redundancy in the system. Results indicate that the system is a “small world” characterized by the emergence, in its early stages, of a hub‐dominated structure with heterogeneity in users' behavior. We investigate whether hubs are responsible for holding the system together and facilitating information flow, examine first‐mover advantages underpinning users' ability to rise to system prominence, and uncover gender differences in users' gregariousness, popularity, and local redundancy. We discuss the implications of the results for research on system use and evolving social networks, and for a host of applications, including information diffusion, communities of practice, and the security and robustness of information systems.

In this paper, we present an abstract formal model of decision‐making in a social setting that covers all aspects of the process, from recognition of a potential for cooperation through to joint decision. In a multi‐agent environment, where self‐motivated autonomous agents try to pursue their own goals, a joint decision cannot be taken for granted. In order to decide effectively, agents need the ability to (a) represent and maintain a model of their own mental attitudes, (b) reason about other agents' mental attitudes, and (c) influence other agents' mental states. Social mental shaping is advocated as a general mechanism for attempting to have an impact on agents' mental states in order to increase their cooperativeness towards a joint decision. Our approach is to specify a novel, high‐level architecture for collaborative decision‐making in which the mentalistic notions of belief, desire, goal, intention, preference and commitment play a central role in guiding the individual agent's and the group's decision‐making behaviour. We identify preconditions that must be fulfilled before collaborative decision‐making can commence and prescribe how cooperating agents should behave, in terms of their own decision‐making apparatus and their interactions with others, when the decision‐making process is progressing satisfactorily. The model is formalized through a new, many‐sorted, multi‐modal logic.

This paper presents a framework that captures how the social nature of agents that are situated in a multi‐agent environment impacts upon their individual mental states. Roles and social relationships provide an abstraction upon which we develop the notion of social mental shaping. This allows us to extend the standard Belief‐Desire‐Intention model to account for how common social phenomena (e.g. cooperation, collaborative problem‐solving and negotiation) can be integrated into a unified theoretical perspective that reflects a fully explicated model of the autonomous agent’s mental state.