Dataset Information

Social influence on selection behaviour: Distinguishing local- and global-driven preferential attachment.

ABSTRACT: Social influence drives human selection behaviours when numerous objects competing for limited attentions, which leads to the 'rich get richer' dynamics where popular objects tend to get more attentions. However, evidences have been found that, both the global information of the whole system and the local information among one's friends have significant influence over the one's selection. Consequently, a key question raises that, it is the local information or the global information more determinative for one's selection? Here we compare the local-based influence and global-based influence. We show that, the selection behaviour is mainly driven by the local popularity of the objects while the global popularity plays a supplementary role driving the behaviour only when there is little local information for the user to refer to. Thereby, we propose a network model to describe the mechanism of user-object interaction evolution with social influence, where the users perform either local-driven or global-driven preferential attachments to the objects, i.e., the probability of an objects to be selected by a target user is proportional to either its local popularity or global popularity. The simulation suggests that, about 75% of the attachments should be driven by the local popularity to reproduce the empirical observations. It means that, at least in the studied context where users chose businesses on Yelp, there is a probability of 75% for a user to make a selection according to the local popularity. The proposed model and the numerical findings may shed some light on the study of social influence and evolving social systems.

SUBMITTER: Pan X

PROVIDER: S-EPMC5391099 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:In recent years, with the emergence of relatively inexpensive and accessible virtual reality technologies, it is now possible to deliver compelling and realistic simulations of human-to-human interaction. Neuroimaging studies have shown that, when participants believe they are interacting via a virtual interface with another human agent, they show different patterns of brain activity compared to when they know that their virtual partner is computer-controlled. The suggestion is that users adopt an "intentional stance" by attributing mental states to their virtual partner. However, it remains unclear how beliefs in the agency of a virtual partner influence participants' behaviour and subjective experience of the interaction. We investigated this issue in the context of a cooperative "joint attention" game in which participants interacted via an eye tracker with a virtual onscreen partner, directing each other's eye gaze to different screen locations. Half of the participants were correctly informed that their partner was controlled by a computer algorithm ("Computer" condition). The other half were misled into believing that the virtual character was controlled by a second participant in another room ("Human" condition). Those in the "Human" condition were slower to make eye contact with their partner and more likely to try and guide their partner before they had established mutual eye contact than participants in the "Computer" condition. They also responded more rapidly when their partner was guiding them, although the same effect was also found for a control condition in which they responded to an arrow cue. Results confirm the influence of human agency beliefs on behaviour in this virtual social interaction context. They further suggest that researchers and developers attempting to simulate social interactions should consider the impact of agency beliefs on user experience in other social contexts, and their effect on the achievement of the application's goals.

Project description:BackgroundMany biological networks show some characteristics of scale-free networks. Scale-free networks can evolve through preferential attachment where new nodes are preferentially attached to well connected nodes. In networks which have evolved through preferential attachment older nodes should have a higher average connectivity than younger nodes. Here we have investigated preferential attachment in the context of metabolic networks.ResultsThe connectivities of the enzymes in the metabolic network of Escherichia coli were determined and representatives for these enzymes were located in 11 eukaryotes, 17 archaea and 46 bacteria. E. coli enzymes which have representatives in eukaryotes have a higher average connectivity while enzymes which are represented only in the prokaryotes, and especially the enzymes only present in betagamma-proteobacteria, have lower connectivities than expected by chance. Interestingly, the enzymes which have been proposed as candidates for horizontal gene transfer have a higher average connectivity than the other enzymes. Furthermore, It was found that new edges are added to the highly connected enzymes at a faster rate than to enzymes with low connectivities which is consistent with preferential attachment.ConclusionHere, we have found indications of preferential attachment in the metabolic network of E. coli. A possible biological explanation for preferential attachment growth of metabolic networks is that novel enzymes created through gene duplication maintain some of the compounds involved in the original reaction, throughout its future evolution. In addition, we found that enzymes which are candidates for horizontal gene transfer have a higher average connectivity than other enzymes. This indicates that while new enzymes are attached preferentially to highly connected enzymes, these highly connected enzymes have sometimes been introduced into the E. coli genome by horizontal gene transfer. We speculate that E. coli has adjusted its metabolic network to a changing environment by replacing the relatively central enzymes for better adapted orthologs from other prokaryotic species.

Dataset Information

Social influence on selection behaviour: Distinguishing local- and global-driven preferential attachment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets