We present a time-dependent approach for learning temporal co-variates explaining the subsequent user sentiments in social networks. In most of the existing approaches, we note that the underlying text classification setting, generally used to model user sentiments, is designed to ingest a user comment generated at time t to predict his corresponding sentiment at the same time. Under such constraint, user sentiments can only be given whenever she or he has generated a comment. Furthermore, the evolving historical sentiments are omitted and no anticipation of subsequent sentiments could be made. To alleviate this limitation, we propose a time-dependent approach that takes advantage of historical user comments to learn temporal co-variates that explain their evolving sentiments. We demonstrate that our approach could be used to predict user sentiments at subsequent times ahead. Experimental results on Tweets data, during the Covid-19 pandemic, illustrate the suitability of our approach.

These days, complex systems yield copious time series data, necessitating understanding co-generation, often assessed through pairwise comparisons. However, this method lacks scalability and temporal dynamics handling. In this paper, we advocate using a temporal graph to capture contiguous effects among multiple time series efficiently. Our two-step approach identifies patterns and temporal influences with low execution time, showcasing its potential in financial system incident prediction.

We investigate the problem of discovering and modeling regime shifts in an ecosystem comprising multiple time series known as co-evolving time series. Regime shifts refer to the changing behaviors exhibited by series at different time intervals. Learning these changing behaviors is a key step toward time series forecasting. While advances have been made, existing methods suffer from one or more of the following shortcomings: (1) failure to take relationships between time series into consideration for discovering regimes in multiple time series; (2) lack of an effective approach that models time-dependent behaviors exhibited by series; (3) difficulties in handling data discontinuities which may be informative. Most of the existing methods are unable to handle all of these three issues in a unified framework. This, therefore, motivates our effort to devise a principled approach for modeling interactions and time-dependency in co-evolving time series. Specifically, we model an ecosystem of multiple time series by summarizing the heavy ensemble of time series into a lighter and more meaningful structure called a mapping grid . By using the mapping grid, our model first learns time series behavioral dependencies through a dynamic network representation, then learns the regime transition mechanism via a full time-dependent Cox regression model. The originality of our approach lies in modeling interactions between time series in regime identification and in modeling time-dependent regime transition probabilities, usually assumed to be static in existing work.

We present a time-dependent approach for learning potential features that may explain the early risk of human self-harm. Rather than only extracting features from text posted by users, as suggested by several approaches, we propose remodeling the user posts into sequential data. We demonstrate that the sequences reflecting the longitudinal grammatical language of users allow the improved performance of classification algorithms in predicting self-harm behavior. The experimental results on the eRisk 2019 data corroborate our claim.

Most existing approaches for electricity load forecasting perform the task based on overall electricity consumption. However, using such a global methodology can affect load forecasting accuracy, as it does not consider the possibility that customers’ consumption behavior may change at any time. Predicting customers’ electricity consumption in the presence of unstable behaviors poses challenges to existing models. In this article, we propose a principled approach capable of handling customers’ changeable electricity consumption. We devise a network-based method that first builds and tracks clusters of customer consumption patterns over time. Then, on the evolving clusters, we develop a framework that exploits long short-term memory recurrent neural network and survival analysis techniques to forecast electricity consumption. Our experiments on real electricity consumption datasets illustrate the suitability of the proposed approach.

As time evolves, communities in a social network may undergo various changes known as critical events. For instance, a community can either split into several other communities, expand into a larger community, shrink to a smaller community, remain stable or merge into another community. Prediction of critical events has attracted increasing attention in the recent literature. Learning the evolution of communities over time is a key step towards predicting the critical events the communities may undergo. This is an important and difficult issue in the study of social networks. In the work to date, there is a lack of formal approaches for modeling and predicting critical events over time. This motivates our effort to design a new statistical method for event prediction in order to make better use of histories of past changes. To this end, this paper proposes a sliding window analysis from which we develop a model that simultaneously exploits an autoregressive model and survival analysis techniques. The autoregressive model is employed here to simulate the evolution of the community structure, whereas the survival analysis techniques allow the prediction of future changes the community may undergo.

In real-world social networks, there is an increasing interest in tracking the evolution of groups of users and detecting the various changes they are liable to undergo. Several approaches have been proposed for this. In studying these approaches, we observed that most of them use a two-stage process. In the first stage, they run an algorithm to identify groups of users at each timestamp. In the second stage, a pairwise comparison based on a similarity measure is employed to track groups of users and detect changes they may undergo. While the majority of existing approaches use a two-stage process, they all run different algorithms to identify communities and rely on different similarity measures to track groups of users over time. Noting that the different approaches may perform differently depending on the dynamic social network under investigation, we decided to make a high level survey of some existing tracking approaches and then do a comparative analysis of some of them. In our analysis, we compared the algorithms in two main situations: (1) when groups of users do not overlap and (2) when the groups are overlapping. The study was done on three different testbeds extracted from the DBLP, Autonomous System (AS) and Yelp datasets.

In real-world social networks, there is increasing interest in tracking the evolution of groups of users. Existing approaches track evolving communities, in a time-sequential way, by comparing communities in terms of nodes using a similarity measure such as the Jaccard or a modified Jaccard measure. The measure allows the use of a one-to-one comparison in order to match communities. However, tracking a given community based on this measure alone may, at the end of its lifespan yield a community that does not share any node with the community initially observed. In this paper we present a novel approach for modeling and detecting the evolution of communities. In our model, we first build a matrix that counts the number of nodes shared between two communities. The individual rows of the obtained matrix are then used to represent nodes shared by a community with all other communities over time. This effectively captures the trace of the communities that should be compared over the period of observation. We then propose a new similarity measure, named mutual transition, for tracking the communities and rules for capturing significant transition events a community can undergo. The proposed approach is general in the sense that it can be applied to different social networks. To demonstrate the suitability of the proposed method, we conducted experiments on real data extracted from the DBLP, Autonomous System and YELP.