Search By Subject

In a competitive Mobile telecommunications market, the customers want competitive pricing and high quality of service. A customer won't hesitate to change their telecom service provider if he/she does not find what they are looking for. This phenomenon ...

Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks. However, learning on large graphs remains a challenge -- many recently proposed scalable GNN approaches rely on an expensive message-passing ...

The spread of information on Facebook and Twitter is much more efficient than on traditional social media platforms. For word-of-mouth (WOM) marketing, social media have become a rich information source for companies or scholars to design models to ...

We propose a network-aware multi-agent simulation approach to understanding the interlacing connections between herder-farmer communities in open property regimes. Specifically, we model herder-farmer conflicts in agent-based terms whereby individual ...

This paper aims to explore the problems associated in solving the classification of cancer in gene expression data using deep learning model. Our proposed solution for the cancer classification of ribonucleic acid sequencing (RNA-seq) extracted from the ...

With the advent of child-centric content-sharing platforms, such as YouTube Kids, thousands of children, from all age groups are consuming gigabytes of content on a daily basis. With PBS Kids, Disney Jr. and countless others joining in the fray, this ...

Community detection and evolution has been largely studied in the last few years, especially for network systems that are inherently dynamic and undergo different types of changes in their structure and organization in communities. Because of the ...

Fake news and rumors constitute a major problem in social networks recently. Due to the fast information propagation in social networks, it is inefficient to use human labor to detect suspicious news. Automatic rumor detection is thus necessary to ...

How to effectively detect fake news and prevent its diffusion on social media has gained much attention in recent years. However, relatively little focus has been given on exploiting user comments left for posts and latent sentiments therein in ...

As deep learning models are getting popular, upgrading the retrieval-based content recommendation system to the learning-based system is highly demanded. However, efficiency is a critical issue. For article recommendation, an effective neural network ...

This paper studies graph-based recommendation, where an interaction graph is built from historical responses and is leveraged to alleviate data sparsity and cold start problems. We reveal an early summarization problem in previous graph-based models, ...

This paper presents a method of pairwise multi-layer networks for multi-field categorical data, which widely exists with various applications such as web search, recommender systems, social link prediction, and computational advertising. The success of ...

In the increasingly digitalized world, it is of utmost importance for various applications to harness the ability to process, understand, and exploit data collected from the Internet. For instance, in customer-centric applications such as personalized ...

In this talk, I will first discuss deep learning models that can find semantically meaningful representations of words, learn to read documents and answer questions about their content. I will show how we can encode external linguistic knowledge as an ...

The advent of advanced modeling for general machine learning, and in particular computer vision, speech recognition and natural language processing, the applications of AI is enabling classical businesses to reinvent themselves, and new business fields ...

Machine learning (ML) has had a tremendous impact in across the world over the last decade. As we think about ML solving complex tasks, sometimes at super-human levels, it is easy to forget that there is no machine learning without humans in the loop. ...

This tutorial aims to provide the audience with a guided introduction to deep reinforcement learning (DRL) with specially curated application case studies in transportation. The tutorial covers both theory and practice, with more emphasis on the ...

Real-world entities' behaviors, associated with their side information, are often recorded over time as asynchronous event sequences. Such event sequences are the basis of many practical applications, neural spiking train study, earth quack prediction, ...

There have long been connections between statistical mechanics and neural networks, but in recent decades these connections have withered. However, in light of recent failings of statistical learning theory and stochastic optimization theory to describe,...

Time series forecasting is a key ingredient in the automation and optimization of business processes: in retail, deciding which products to order and where to store them depends on the forecasts of future demand in different regions; in cloud computing, ...