arXiv research

Paper uses social norms to teach robots human behavior.

Social trust prediction addresses the significant problem of exploring interactions among users in social networks. Naturally, this problem can be formulated in the matrix completion framework, with each entry indicating the trustness or distrustness. However, there are two challenges for the social trust problem: 1) t…

Investors adjust spending based on a social norm, spending less during losses and more during gains.

Study chaotic dynamics in social stratification models leading to thermalization and turbulence.

Algorithm improves learning by integrating diverse agents' behaviors.

New methods solve graph sparsity optimization problems faster.

Stochastic optimization algorithms update models with cheap per-iteration costs sequentially, which makes them amenable for large-scale data analysis. Such algorithms have been widely studied for structured sparse models where the sparsity information is very specific, e.g., convex sparsity-inducing norms or $\ell^0$-n…

The paper introduces a penalized matrix estimation procedure aiming at solutions which are sparse and low-rank at the same time. Such structures arise in the context of social networks or protein interactions where underlying graphs have adjacency matrices which are block-diagonal in the appropriate basis. We introduce…

Framework for online resource allocation using social welfare functions.

A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The social web forms an excellent modern paradigm, where unstructured user generated content is published on a regular basis and in most occasions is freely distributed. The present Ph.D. Thesis deals with the problem …

The ubiquitous proliferation of online social networks has led to the widescale emergence of relational graphs expressing unique patterns in link formation and descriptive user node features. Matrix Factorization and Completion have become popular methods for Link Prediction due to the low rank nature of mutual node fr…

The study applies wealth thermalization hypothesis to social networks and explains inequality.

This paper gives new concentration inequalities for the spectral norm of a wide class of matrix martingales in continuous time. These results extend previously established Freedman and Bernstein inequalities for series of random matrices to the class of continuous time processes. Our analysis relies on a new supermarti…

Contagions such as the spread of popular news stories, or infectious diseases, propagate in cascades over dynamic networks with unobservable topologies. However, "social signals" such as product purchase time, or blog entry timestamps are measurable, and implicitly depend on the underlying topology, making it possible …

We consider the problem of unveiling the implicit network structure of node interactions (such as user interactions in a social network), based only on high-frequency timestamps. Our inference is based on the minimization of the least-squares loss associated with a multivariate Hawkes model, penalized by $\ell_1$ and t…

New study finds targeting based on treatment effects outperforms risk-based targeting in social interventions.

We consider a group of mean-variance investors with mimicking desire such that each investor is willing to penalize deviations of his portfolio composition from compositions of other group members. Penalizing norm constraints are already applied for statistical improvement of Markowitz portfolio procedure in order to c…

PHASE dataset simulates complex social interactions in physical environments.

In this paper, we introduce the first principled adaptive-sampling procedure for learning a convex function in the $L_\infty$ norm, a problem that arises often in the behavioral and social sciences. We present a function-specific measure of complexity and use it to prove that, for each convex function $f_{\star}$, our …

Improved neural model for social recommendation by integrating social and interest networks.

Social media enhances or diminishes scientific status, depending on usage.

Low-rank modeling has a lot of important applications in machine learning, computer vision and social network analysis. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better recovery performance. However, the resultant optimization pro…

Social learning can make financial markets inefficient, but individual learning can fix this.

SINN combines social science and deep learning for predicting opinion dynamics.

Power of network tests degrades when vertices are misaligned.

Optimizes social interactions for profit, people, and planet using mathematical models.

Model predicts increased social unrest during COVID-19 using social media data.

Enhances traditional MV model for socially responsible investors.

We present a novel human-aware navigation approach, where the robot learns to mimic humans to navigate safely in crowds. The presented model, referred to as DeepMoTIon, is trained with pedestrian surveillance data to predict human velocity in the environment. The robot processes LiDAR scans via the trained network to n…

The rise in online social networking has brought about a revolution in social relations. However, its effects on offline interactions and its implications for collective well-being are still not clear and are under-investigated. We study the ecology of online and offline interaction in an evolutionary game framework wh…

Paper proposes PP-GCN for fine-grained social event categorization.

A new method for recommending groups and activities based on geo-social data.

Religious adherence reduces corporate greenwashing behavior.

We show how to incorporate ethical principles into machine learning models.

Secure social recommendation framework using secret sharing.

Predicting event attendance using social influence from social networks.

Model predicts depression and anxiety from social network dynamics.

SBO uses dual voting to build consensus in noisy feedback settings.

The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest. However, accounting for uncertainty in such models, particularly when using diverse, unstructured datasets such as social media, is essential to guarantee t…

Online social networks offer a new way to investigate financial markets' dynamics by enabling the large-scale analysis of investors' collective behavior. We provide empirical evidence that suggests social media and stock markets have a nonlinear causal relationship. We take advantage of an extensive data set composed o…

Paper proposes using word embeddings to detect trolls in social media debates.

New method for faster graph parameter inference from large random Kronecker graphs.

Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance…

We show social events can be accurately predicted, but often undesirably.

Heterogeneous SVO leads to diverse policies in sequential social dilemmas.

Actors in realistic social networks play not one but a number of diverse roles depending on whom they interact with, and a large number of such role-specific interactions collectively determine social communities and their organizations. Methods for analyzing social networks should capture these multi-faceted role-spec…

NAS model improves social recommendation accuracy using neural attention.

Agents learn social skills from each other, improving performance.