A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study predicts U.S. county COVID-19 growth using demographic and social distancing data.
Social messages classification is a research domain that has attracted the attention of many researchers in these last years. Indeed, the social message is different from ordinary text because it has some special characteristics like its shortness. Then the development of new approaches for the processing of the social…
Analysis of opinion dynamics in social networks plays an important role in today's life. For applications such as predicting users' political preference, it is particularly important to be able to analyze the dynamics of competing opinions. While observing the evolution of polar opinions of a social network's users ove…
A new framework for causal inference in networked settings.
AI monitors social distancing and masks at manufacturing plants.
Stocks of more resilient firms outperformed during the pandemic, reflecting disaster risk.
SLIM model predicts social network polarization using signed links.
In this paper, we introduce a methodology that allows to model behavioral trajectories of users in online social media. First, we illustrate how to leverage the probabilistic framework provided by Hidden Markov Models (HMMs) to represent users by embedding the temporal sequences of actions they performed online. We the…
The evolution of social media users' behavior over time complicates user-level comparison tasks such as verification, classification, clustering, and ranking. As a result, naïve approaches may fail to generalize to new users or even to future observations of previously known users. In this paper, we propose a novel pro…
Computing shortest path distances between nodes lies at the heart of many graph algorithms and applications. Traditional exact methods such as breadth-first-search (BFS) do not scale up to contemporary, rapidly evolving today's massive networks. Therefore, it is required to find approximation methods to enable scalable…
Gromov-Hausdorff distances measure shape difference between the objects representable as compact metric spaces, e.g. point clouds, manifolds, or graphs. Computing any Gromov-Hausdorff distance is equivalent to solving an NP-Hard optimization problem, deeming the notion impractical for applications. In this paper we pro…
We solve the multi-criteria benchmarking problem by formalizing it as a social choice problem and identifying conditions for meaningful rankings.
Proposes a Coulomb-like model for international trade flows, fitting real-world data.
The Social Internet of Things (SIoT), integration of the Internet of Things and Social Networks paradigms, has been introduced to build a network of smart nodes that are capable of establishing social links. In order to deal with misbehaving service provider nodes, service requestor nodes must evaluate their trustworth…
Proposes a new model for predicting future motion of road actors in autonomous vehicles.
I-AID categorizes disaster tweets into useful information types.
Optimizes lockdown strategies to balance economic activities and virus spread.
Model predicts COVID-19 cases with high accuracy using mobility data.
StratLearner learns strategies to prevent misinformation in social networks.
In accessibility tests for digital preservation, over time we experience drifts of localized and labelled content in statistical models of evolving semantics represented as a vector field. This articulates the need to detect, measure, interpret and model outcomes of knowledge dynamics. To this end we employ a high-perf…
Paper proves convergence of Gini index to equilibrium in Wasserstein distance.
We develop a novel methodology based on the marriage between the Bhattacharyya distance, a measure of similarity across distributions of random variables, and the Johnson-Lindenstrauss Lemma, a technique for dimension reduction. The resulting technique is a simple yet powerful tool that allows comparisons between data-…
We introduce a new framework for comparing parametric network families.
New methods cluster and test graphs without vertex correspondence.
Predictive models are being increasingly used to support consequential decision making at the individual level in contexts such as pretrial bail and loan approval. As a result, there is increasing social and legal pressure to provide explanations that help the affected individuals not only to understand why a predictio…
A new method predicts links better across various networks.
Kernel measures similarity of nonlinear causal structures in heterogeneous populations.
Forecasting COVID-19 cases in Senegal using machine learning.
Graph Convolutional Neural Networks (GCNNs) extend classical CNNs to graph data domain, such as brain networks, social networks and 3D point clouds. It is critical to identify an appropriate graph for the subsequent graph convolution. Existing methods manually construct or learn one fixed graph for all the layers of a …
PHASE dataset simulates complex social interactions in physical environments.
The study of networks has received increased attention recently not only from the social sciences and statistics but also from physicists, computer scientists and mathematicians. One of the principal problem in networks is community detection. Many algorithms have been proposed for community finding but most of them do…
We present a method for identifying the coherent structures associated with individual Lagrangian flow trajectories even where only sparse particle trajectory data is available. The method, based on techniques in spectral graph theory, uses the Coherent Structure Coloring vector and associated eigenvectors to analyze t…
Social media enhances or diminishes scientific status, depending on usage.
Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.
Social learning can make financial markets inefficient, but individual learning can fix this.
SINN combines social science and deep learning for predicting opinion dynamics.
Optimizes social interactions for profit, people, and planet using mathematical models.
Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and con…
Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences, which could alleviate the data sparsity issue in collaborative filtering based recommendation. Early approaches relied on utilizing each user's first-order social neighbors' interests for better user …
Model predicts increased social unrest during COVID-19 using social media data.
Enhances traditional MV model for socially responsible investors.
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…
Study uses trajectory embedding to measure place function similarity at fine spatial granularity.
Probabilistic graphical models are graphical representations of probability distributions. Graphical models have applications in many fields including biology, social sciences, linguistic, neuroscience. In this paper, we propose directed acyclic graphs (DAGs) learning via bootstrap aggregating. The proposed procedure i…
Paper proposes a new model for multivariate risk measures using Wasserstein barycenters.
Algorithm improves learning by integrating diverse agents' behaviors.
Relationships between entities in datasets are often of multiple nature, like geographical distance, social relationships, or common interests among people in a social network, for example. This information can naturally be modeled by a set of weighted and undirected graphs that form a global multilayer graph, where th…
Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elem…