Time-varying graph representation learning via higher-order skip-gram with negative sampling

Representation learning models for graphs are a successful family of techniques that project nodes into feature spaces that can be exploited by other machine learning algorithms. Since many real-world networks are inherently dynamic, with interactions among nodes changing over time, these techniques can be defined both for static and for time-varying graphs. Here, we show how the skip-gram embedding approach can be generalized to perform implicit tensor factorization on different tensor representations of time-varying graphs. We show that higher-order skip-gram with negative sampling (HOSGNS) is able to disentangle the role of nodes and time, with a small fraction of the number of parameters needed by other approaches. We empirically evaluate our approach using time-resolved face-to-face proximity data, showing that the learned representations outperform state-of-the-art methods when used to solve downstream tasks such as network reconstruction. Good performance on predicting the outcome of dynamical processes such as disease spreading shows the potential of this method to estimate contagion risk, providing early risk awareness based on contact tracing data. Supplementary information: The online version contains supplementary material available at 10.1140/epjds/s13688-022-00344-8

Predicting human mobility through the assimilation of social media traces into mobility models

Predicting human mobility flows at different spatial scales is challenged by the heterogeneity of individual trajectories and the multi-scale nature of transportation networks. As vast amounts of digital traces of human behaviour become available, an opportunity arises to improve mobility models by integrating into them proxy data on mobility collected by a variety of digital platforms and location-aware services. Here we propose a hybrid model of human mobility that integrates a large-scale publicly available dataset from a popular photo-sharing system with the classical gravity model, under a stacked regression procedure. We validate the performance and generalizability of our approach using two ground-truth datasets on air travel and daily commuting in the United States: using two different cross-validation schemes we show that the hybrid model affords enhanced mobility prediction at both spatial scales.Comment: 17 pages, 10 figure

Explainability Methods for Natural Language Processing: Applications to Sentiment Analysis

Sentiment analysis is the process of classifying natural lan-guage sentences as expressing positive or negative sentiments, and it is a crucial task where the explanation of a prediction might arguably be as necessary as the prediction itself. We analysed di fierent explanation techniques, and we applied them to the classification task of Sentiment Analysis. We explored how attention-based techniques can be exploited to extract meaningful sentiment scores with a lower computational cost than existing XAI methods

Anomaly detection in temporal graph data: An iterative tensor decomposition and masking approach

Sensors and Internet-of-Things scenarios promise a wealth of interaction data that can be naturally represented by means of timevarying graphs. This brings forth new challenges for the identification and removal of temporal graph anomalies that entail complex correlations of topological features and activity patterns. Here we present an anomaly detection approach for temporal graph data based on an iterative tensor decomposition and masking procedure. We test this approach using highresolution social network data from wearable sensors and show that it successfully detects anomalies due to sensor wearing time protocols.published_or_final_versio

Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data

Ending poverty in all its forms everywhere is the number one Sustainable Development Goal of the UN 2030 Agenda. To monitor the progress toward such an ambitious target, reliable, up-to-date and fine-grained measurements of socioeconomic indicators are necessary. When it comes to socioeconomic development, novel digital traces can provide a complementary data source to overcome the limits of traditional data collection methods, which are often not regularly updated and lack adequate spatial resolution. In this study, we collect publicly available and anonymous advertising audience estimates from Facebook to predict socioeconomic conditions of urban residents, at a fine spatial granularity, in four large urban areas: Atlanta (USA), Bogotá (Colombia), Santiago (Chile), and Casablanca (Morocco). We find that behavioral attributes inferred from the Facebook marketing platform can accurately map the socioeconomic status of residential areas within cities, and that predictive performance is comparable in both high and low-resource settings. Our work provides additional evidence of the value of social advertising media data to measure human development and it also shows the limitations in generalizing the use of these data to make predictions across countries

RV4JaCa - Runtime Verification for Multi-Agent Systems

This paper presents a Runtime Verification (RV) approach for Multi-Agent Systems (MAS) using the JaCaMo framework. Our objective is to bring a layer of security to the MAS. This layer is capable of controlling events during the execution of the system without needing a specific implementation in the behaviour of each agent to recognise the events. MAS have been used in the context of hybrid intelligence. This use requires communication between software agents and human beings. In some cases, communication takes place via natural language dialogues. However, this kind of communication brings us to a concern related to controlling the flow of dialogue so that agents can prevent any change in the topic of discussion that could impair their reasoning. We demonstrate the implementation of a monitor that aims to control this dialogue flow in a MAS that communicates with the user through natural language to aid decision-making in hospital bed allocation

Explaining Semantic Reasoning Using Argumentation

Multi-Agent Systems (MAS) are popular because they provide a paradigm that naturally meets the current demand to design and implement distributed intelligent systems. When developing a multi-agent application, it is common to use ontologies to provide the domain-specific knowledge and vocabulary necessary for agents to achieve the system goals. In this paper, we propose an approach in which agents can query semantic reasoners and use the received inferences to build explanations for such reasoning. Also, thanks to an internal representation of inference rules used to build explanations, in the form of argumentation schemes, agents are able to reason and make decisions based on the answers from the semantic reasoner. Furthermore, agents can communicate the built explanation to other agents and humans, using computational or natural language representations of arguments. Our approach paves the way towards multi-agent systems able to provide explanations from the reasoning carried out by semantic reasoners

Impact of birth weight and daily weight gain during suckling on the weight gain of weaning piglets.

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