From Co-Location Patterns to an Informal Social Network of Gig Economy Workers

Pilatti, G., Candia, C., Montini, A., & Pinheiro, F. L. (2023). From Co-Location Patterns to an Informal Social Network of Gig Economy Workers. Applied Network Science, 8, 1-15. [77]. https://doi.org/10.21203/rs.3.rs-2742628/v1, https://doi.org/10.1007/s41109-023-00603-1---GP, AM, and FLP are very grateful for the suggestions given by the audience and peer review of the Complex Networks and Their Applications XI conference, in which we were able to clarify some points and enrich the research. The authors are thankful to the food delivery platform for sharing the data for this study. The findings, interpretations, and conclusions expressed by the authors in this work do not necessarily reflect the views of the food delivery platform. FLP acknowledges the financial support provided by FCT Portugal under the project UIDB/04152/2020 – Centro de Investigação em Gestão de Informação (MagIC).The labor market has transformed with the advent of the gig economy, characterized by short-term and flexible work arrangements facilitated by online platforms. As this trend becomes increasingly prevalent, it presents unique opportunities and challenges. In this manuscript, we comprehensively characterize the social networks of gig economy workers in each of the 15 cities studied. Our analysis reveals a scaling relationship between networks and the city population. In particular, we note the high level of modularity of the networks, and we argue that it results from the natural specialization of couriers along different areas of the cities. Furthermore, we show that degree and betweenness centrality is positively correlated with income but not with tenure. Our findings shed new light on the social organization of the gig economy workers and provide valuable insights for the management and design of gig economy platforms.publishersversionepub_ahead_of_prin

A neural network model for semi-supervised review aspect identification

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

A study of neighbour selection strategies for POI recommendation in LBSNs

Location-based Recommender Systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of liked-minded people, so called neighbors, for prediction. Thus, an adequate selection of such neighbors becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbors in the context of a collaborative filtering based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighborhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from Location-based Social Networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbors based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area as well as to recommender systems developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.Fil: Rios, Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Personalized DP-SGD using Sampling Mechanisms

Personalized privacy becomes critical in deep learning for Trustworthy AI. While Differentially Private Stochastic Gradient Descent (DP-SGD) is widely used in deep learning methods supporting privacy, it provides the same level of privacy to all individuals, which may lead to overprotection and low utility. In practice, different users may require different privacy levels, and the model can be improved by using more information about the users with lower privacy requirements. There are also recent works on differential privacy of individuals when using DP-SGD, but they are mostly about individual privacy accounting and do not focus on satisfying different privacy levels. We thus extend DP-SGD to support a recent privacy notion called ($\Phi$,$\Delta$)-Personalized Differential Privacy (($\Phi$,$\Delta$)-PDP), which extends an existing PDP concept called $\Phi$-PDP. Our algorithm uses a multi-round personalized sampling mechanism and embeds it within the DP-SGD iterations. Experiments on real datasets show that our algorithm outperforms DP-SGD and simple combinations of DP-SGD with existing PDP mechanisms in terms of model performance and efficiency due to its embedded sampling mechanism.Comment: 10 pages, 5 figure

EC3: Combining Clustering and Classification for Ensemble Learning

Classification and clustering algorithms have been proved to be successful individually in different contexts. Both of them have their own advantages and limitations. For instance, although classification algorithms are more powerful than clustering methods in predicting class labels of objects, they do not perform well when there is a lack of sufficient manually labeled reliable data. On the other hand, although clustering algorithms do not produce label information for objects, they provide supplementary constraints (e.g., if two objects are clustered together, it is more likely that the same label is assigned to both of them) that one can leverage for label prediction of a set of unknown objects. Therefore, systematic utilization of both these types of algorithms together can lead to better prediction performance. In this paper, We propose a novel algorithm, called EC3 that merges classification and clustering together in order to support both binary and multi-class classification. EC3 is based on a principled combination of multiple classification and multiple clustering methods using an optimization function. We theoretically show the convexity and optimality of the problem and solve it by block coordinate descent method. We additionally propose iEC3, a variant of EC3 that handles imbalanced training data. We perform an extensive experimental analysis by comparing EC3 and iEC3 with 14 baseline methods (7 well-known standalone classifiers, 5 ensemble classifiers, and 2 existing methods that merge classification and clustering) on 13 standard benchmark datasets. We show that our methods outperform other baselines for every single dataset, achieving at most 10% higher AUC. Moreover our methods are faster (1.21 times faster than the best baseline), more resilient to noise and class imbalance than the best baseline method.Comment: 14 pages, 7 figures, 11 table