Nome da Revista: Neurocomputing
Classificação: B1
Dossiê Temático: Real-time Dynamic Network Learning for Location Inference Modelling and Computing
Prazo: 15/10/2020
Titulação: Sem informação.
User location information contributes to in-depth social network data analytics. Discovering physical locations of users from their online media messages helps us to bridge the online and offline worlds. This also supports many real-life applications like emergency reporting, disaster management, location-based recommendation, location-based advertisement, region-specific topic summarization, and disease outbreak monitoring. For instance, the social distance has played a key role to reduce the Covid19 outbreak. However, location information is not always available because most users may not clearly annotate their locations in user profiles. Recent research trends intend to incorporate multiple types of data including text data, linked data, sensor data, as well as auxiliary insightful feature data. These data generate the linked and dynamic network data, which can be utilized together to learn and infer the user locations in different applications.
However, the existing techniques like recurrent neural network and generative adversarial network are still expensive to train the network models. It is more challenging to handle the dynamics of the networks for particular tasks, particularly when the data distribution and the types of data are not even. Furthermore, the diverse location inference tasks in real applications make the issue being more complex, e.g., next-visit location, event-based location, shopping location, indoor location, web location, etc. As such, novel multi-model dynamic network learning techniques expect to be investigated.
This special issue focuses on emerging techniques and trendy applications of real-time dynamic network learning in fields such as neural network, dynamic network, spatial feature pattern recognition, and active learning.