Happiness is assortative in online social networks

Social networks tend to disproportionally favor connections between individuals with either similar or dissimilar characteristics. This propensity, referred to as assortative mixing or homophily, is expressed as the correlation between attribute values of nearest neighbour vertices in a graph. Recent results indicate that beyond demographic features such as age, sex and race, even psychological states such as "loneliness" can be assortative in a social network. In spite of the increasing societal importance of online social networks it is unknown whether assortative mixing of psychological states takes place in situations where social ties are mediated solely by online networking services in the absence of physical contact. Here, we show that general happiness or Subjective Well-Being (SWB) of Twitter users, as measured from a 6 month record of their individual tweets, is indeed assortative across the Twitter social network. To our knowledge this is the first result that shows assortative mixing in online networks at the level of SWB. Our results imply that online social networks may be equally subject to the social mechanisms that cause assortative mixing in real social networks and that such assortative mixing takes place at the level of SWB. Given the increasing prevalence of online social networks, their propensity to connect users with similar levels of SWB may be an important instrument in better understanding how both positive and negative sentiments spread through online social ties. Future research may focus on how event-specific mood states can propagate and influence user behavior in "real life".Comment: 17 pages, 9 figure

A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation

Recommending a ranked list of interesting venues to users based on their preferences has become a key functionality in Location-Based Social Networks (LBSNs) such as Yelp and Gowalla. Bayesian Personalised Ranking (BPR) is a popular pairwise recommendation technique that is used to generate the ranked list of venues of interest to a user, by leveraging the user's implicit feedback such as their check-ins as instances of positive feedback, while randomly sampling other venues as negative instances. To alleviate the sparsity that affects the usefulness of recommendations by BPR for users with few check-ins, various approaches have been proposed in the literature to incorporate additional sources of information such as the social links between users, the textual content of comments, as well as the geographical location of the venues. However, such approaches can only readily leverage one source of additional information for negative sampling. Instead, we propose a novel Personalised Ranking Framework with Multiple sampling Criteria (PRFMC) that leverages both geographical influence and social correlation to enhance the effectiveness of BPR. In particular, we apply a multi-centre Gaussian model and a power-law distribution method, to capture geographical influence and social correlation when sampling negative venues, respectively. Finally, we conduct comprehensive experiments using three large-scale datasets from the Yelp, Gowalla and Brightkite LBSNs. The experimental results demonstrate the effectiveness of fusing both geographical influence and social correlation in our proposed PRFMC framework and its superiority in comparison to BPR-based and other similar ranking approaches. Indeed, our PRFMC approach attains a 37% improvement in MRR over a recently proposed approach that identifies negative venues only from social links

Optimal percolation on multiplex networks

Optimal percolation is the problem of finding the minimal set of nodes such that if the members of this set are removed from a network, the network is fragmented into non-extensive disconnected clusters. The solution of the optimal percolation problem has direct applicability in strategies of immunization in disease spreading processes, and influence maximization for certain classes of opinion dynamical models. In this paper, we consider the problem of optimal percolation on multiplex networks. The multiplex scenario serves to realistically model various technological, biological, and social networks. We find that the multilayer nature of these systems, and more precisely multiplex characteristics such as edge overlap and interlayer degree-degree correlation, profoundly changes the properties of the set of nodes identified as the solution of the optimal percolation problem.Comment: 7 pages, 5 figures + appendi

The Impact of the Activity of Industrial Engineering Researchers in Various Scientific-Citation Networks on Improving their Scientific Authority Status

This study analyzes the link between Mendeley indexes of scientific-citation networks and Scopus, taking into account the beneficial influence of researchers' actions in social networks on scientometric indices of works indexed in databases like Google scholar and WoS. In this basic/descriptive study, we use the Altmetrics approach to describe Iranian researchers’ activities in industrial engineering in scientific-citation networks. In this study, researchers whose activities are recorded with Iranian affiliation in scientific-citation networks have been briefly named Iranian researchers. The corpus of the study included the works of 160 Iranian researchers in the field of industrial engineering, indexed in the Scopus in the period 2000-2019. To test the likely correlation between the measures of social networks (SN) activities with scientometric ones, simple and multiple correlation tests were carried out by Excel and SPSS software. The correlation between the number of times a document was read, the number of citations, and the measures in the Mendeley, Scopus, We of Science (WoS), and Google Scholar (GS) was very high. However, the correlation between the number of readers in the Mendeley and co-authorship in Scopus was low. There was a strong correlation between the number of citations in Mendeley and that in other databases. The correlation between the authors' H-index in the Mendeley database and other databases is positive and significant, stronger in Scopus and WoS than Google Scholar. It was finally concluded that researchers’ activities in social networks attract more readers, increase the number of citations and thus increase the H-index score in databases. Therefore, they need to be more active in social networks to increase their H-index score and promote academic publications.https://dorl.net/dor/ 20.1001.1.20088302.2022.20.1.14.