WhatsApp, a very popular cross-platform messaging platform with more than one and a half billion users, is the preferred medium for communication in several countries. One of the interesting features in the platform is called Groups (WG), which is an encrypted end-to-end virtual room created by a group of individuals, where only the group members can send and see the messages. The goodness of privacy and security offered by WG have attracted the attention of families, friends, businesses, and organizations. However, in several cases, those WG's have been used as a powerful tool for spreading dangerous content or fake news. In this work, we propose a new method for measuring the level of engagement in conversations and users from WhatsApp groups, by using temporal interaction networks and without reading the messages. Our framework creates an ensemble of networks that represent the temporal evolution of the conversation every 10 minutes. In this way, we use network measurements to build an Engagement Index (EI) for fractions of the conversations. Our results in five real-world WGs data indicate that the EI is able to identify different type of conversations, and users' behaviors according to each category, as well as anomalies on users' engagement.
@cite_18: Given social networks over time, how can we measure network activities across different timesteps with a limited number of metrics? We propose two classes of dynamic metrics for assessing temporal evolution patterns of agents in terms of persistency and emergence. For each class of dynamic metrics, we implement it using three different temporal aggregation models ranging from the most commonly used Average Aggregation Model to more the complex models such as the Exponential Aggregation Model. We argue that the problem of measuring temporal patterns can be formulated using Recency and Primacy effect, which is a concept used to characterize human cognitive processes. Experimental results show that the way metrics model Recency--Primacy effect is closely related to their abilities to measure temporal patterns. Furthermore, our results indicate that future network agent activities can be predicted based on history information using dynamic metrics. By conducting multiple experiments, we are also able to find an optimal length of history information that is most relevant to future activities. This optimal length is highly consistent within a dataset and can be used as an intrinsic metric to evaluate a dynamic social network.
@cite_14: Network theory has established itself as an appropriate tool for complex systems analysis and pattern recognition. In the context of spatiotemporal data analysis, correlation networks are used in the vast majority of works. However, the Pearson correlation coefficient captures only linear relationships and does not correctly capture recurrent events. This missed information is essential for temporal pattern recognition. In this work, we propose a chronological network construction process that is capable of capturing various events. Similar to the previous methods, we divide the area of study into grid cells and represent them by nodes. In our approach, links are established if two consecutive events occur in two different nodes. Our method is computationally efficient, adaptable to different time windows and can be applied to any spatiotemporal data set. As a proof-of-concept, we evaluated the proposed approach by constructing chronological networks from the MODIS dataset for fire events in the Amazon basin. We explore two data analytic approaches: one static and another temporal. The results show some activity patterns on the fire events and a displacement phenomenon over the year. The validity of the analyses in this application indicates that our data modeling approach is very promising for spatio-temporal data mining.
@cite_6: Most networked systems of scientific interest are characterized by temporal links, meaning the network’s structure changes over time. Link temporality has been shown to hinder many dynamical processes, from information spreading to accessibility, by disrupting network paths. Considering the ubiquity of temporal networks in nature, we ask: Are there any advantages of the networks’ temporality? We use an analytical framework to show that temporal networks can, compared to their static counterparts, reach controllability faster, demand orders of magnitude less control energy, and have control trajectories, that are considerably more compact than those characterizing static networks. Thus, temporality ensures a degree of flexibility that would be unattainable in static networks, enhancing our ability to control them.
@cite_0: The time variation of contacts in a networked system may fundamentally alter the properties of spreading processes and affect the condition for large-scale propagation, as encoded in the epidemic threshold. Despite the great interest in the problem for the physics, applied mathematics, computer science, and epidemiology communities, a full theoretical understanding is still missing and currently limited to the cases where the timescale separation holds between spreading and network dynamics or to specific temporal network models. We consider a Markov chain description of the susceptible-infectious-susceptible process on an arbitrary temporal network. By adopting a multilayer perspective, we develop a general analytical derivation of the epidemic threshold in terms of the spectral radius of a matrix that encodes both network structure and disease dynamics. The accuracy of the approach is confirmed on a set of temporal models and empirical networks and against numerical results. In addition, we explore how the threshold changes when varying the overall time of observation of the temporal network, so as to provide insights on the optimal time window for data collection of empirical temporal networked systems. Our framework is of both fundamental and practical interest, as it offers novel understanding of the interplay between temporal networks and spreading dynamics.
@cite_19: Infectious diseases can be considered to spread over social networks of people or animals. Mainly owing tothedevelopment ofdatarecordingand analysistechniques, an increasing amountofsocial contactdata with time stampshas beencollectedinthelast decade. Suchtemporal data capture thedynamics ofsocial networksonatimescalerelevanttoepidemicspreadingandcanpotentiallyleadtobetterwaystoanalyze, forecast, and prevent epidemics. However, they also call for extended analysis tools for network epidemiology, which has, to date, mostly viewed networks as static entities. We review recent results of network epidemiology for such temporal network data and discuss future developments.
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@cite_11: The birth and decline of disciplines are critical to science and society. How do scientific disciplines emerge? No quantitative model to date allows us to validate competing theories on the different roles of endogenous processes, such as social collaborations, and exogenous events, such as scientific discoveries. Here we propose an agent-based model in which the evolution of disciplines is guided mainly by social interactions among agents representing scientists. Disciplines emerge from splitting and merging of social communities in a collaboration network. We find that this social model can account for a number of stylized facts about the relationships between disciplines, scholars, and publications. These results provide strong quantitative support for the key role of social interactions in shaping the dynamics of science. While several “science of science” theories exist, this is the first account for the emergence of disciplines that is validated on the basis of empirical data.
It is increasingly accepted that many natural and social systems are better described as temporal or dynamic networks @cite_6 , with links that exist only at certain times. Some examples are: the temporal and dynamical user's behavior in social networks @cite_18 ; the temporal evolution of disciplines guided by scientists social interaction @cite_11 ; calculating the analytical epidemic threshold @cite_0 or controlling the spreading of epidemics or information @cite_19 @cite_2 ; and, understanding the spatiotemporal evolution of wildfires according to the community identification @cite_14 .