Multilayer networks are a powerful paradigm to model complex systems, where various relations might occur among the same set of entities. Despite the keen interest in a variety of problems, algorithms, and analysis methods in this type of network, the problem of extracting dense subgraphs has remained largely unexplored. As a first step in this direction, we study the problem of core decomposition of a multilayer network. Unlike the single-layer counterpart in which cores are all nested into one another, in the multilayer context no total order exists among multilayer cores: they form a lattice whose size is exponential in the number of layers. In this setting we devise three algorithms which differ in the way they visit the core lattice and in their pruning techniques. We assess time and space efficiency of the three algorithms on a large variety of real-world multilayer networks. We then study the problem of extracting only the inner-most cores, i.e., the cores that are not dominated by any other core in terms of their index on all the layers. As inner-most cores are orders of magnitude less than all the cores, it is desirable to develop algorithms that effectively exploit the maximality property and extract inner-most cores directly, without first computing a complete decomposition. Moreover, we showcase an application of the multilayer core-decomposition tool to the problem of densest-subgraph extraction from multilayer networks. We introduce a definition of multilayer densest subgraph that trades-off between high density and number of layers in which the high density holds, and show how multilayer core decomposition can be exploited to approximate this problem with quality guarantees. We also exploit multilayer core decomposition to speed-up the extraction of frequent cross-graph quasi-cliques and to generalize the community-search problem to the multilayer setting. Core Decomposition in Multilayer Networks: Theory, Algorithms, and Applications

Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities of analysis pipelines. In this review, we provide both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics. We start from the recent achievements of deep learning in the bioinformatics field, pointing out the problems which are suitable to use deep learning. After that, we introduce deep learning in an easy-to-understand fashion, from shallow neural networks to legendary convolutional neural networks, legendary recurrent neural networks, graph neural networks, generative adversarial networks, variational autoencoder, and the most recent state-of-the-art architectures. After that, we provide eight examples, covering five bioinformatics research directions and all the four kinds of data type, with the implementation written in Tensorflow and Keras. Finally, we discuss the common issues, such as overfitting and interpretability, that users will encounter when adopting deep learning methods and provide corresponding suggestions. The implementations are freely available at \url{https://github.com/lykaust15/Deep_learning_examples}. Deep learning in bioinformatics: introduction, application, and perspective in big data era

Fake news detection is a critical yet challenging problem in Natural Language Processing (NLP). The rapid rise of social networking platforms has not only yielded a vast increase in information accessibility but has also accelerated the spread of fake news. Given the massive amount of Web content, automatic fake news detection is a practical NLP problem required by all online content providers. This paper presents a survey on fake news detection. Our survey introduces the challenges of automatic fake news detection. We systematically review the datasets and NLP solutions that have been developed for this task. We also discuss the limits of these datasets and problem formulations, our insights, and recommended solutions. A Survey on Natural Language Processing for Fake News Detection

Graph-structured data arise naturally in many different application domains. By representing data as graphs, we can capture entities (i.e., nodes) as well as their relationships (i.e., edges) with each other. Many useful insights can be derived from graph-structured data as demonstrated by an ever-growing body of work focused on graph mining. However, in the real-world, graphs can be both large – with many complex patterns – and noisy which can pose a problem for effective graph mining. An effective way to deal with this issue is to incorporate ‘attention’ into graph mining solutions. An attention mechanism allows a method to focus on task-relevant parts of the graph, helping it to make better decisions. In this work, we conduct a comprehensive and focused survey of the literature on the emerging field of graph attention models. We introduce three intuitive taxonomies to group existing work. These are based on problem setting (type of input and output), the type of attention mechanism used, and the task (e.g., graph classification, link prediction, etc.). We motivate our taxonomies through detailed examples and use each to survey competing approaches from a unique standpoint. Finally, we highlight several challenges in the area and discuss promising directions for future work. Attention Models in Graphs: A Survey

The ancient mind/body problem continues to be one of deepest mysteries of science and of the human spirit. Despite major advances in many fields, there is still no plausible link between subjective experience (qualia) and its realization in the body. This paper outlines some of the elements of a rigorous science of mind (SoM) – key ideas include scientific realism of mind, agnostic mysterianism, careful attention to language, and a focus on concrete (touchstone) questions and results. Towards a Science of Mind

Currently, knowledge discovery in databases is an essential step to identify valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfil restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently, there is a growing interest from the data mining research community concerning monotonic predictive models. This paper aims to present an overview about the literature in the field, analyzing existing techniques and proposing a taxonomy of the algorithms based on the type of model generated. For each method, we review the quality metrics considered in the evaluation and the different data sets and monotonic problems used in the analysis. In this way, this paper serves as an overview of the research about monotonic classification in specialized literature and can be used as a functional guide of the field. Monotonic classification: an overview on algorithms, performance measures and data sets

This paper discusses technology challenges and opportunities to embrace artificial intelligence (AI) era in the design of wireless networks. We aim to provide readers with motivation and general methodology for adoption of AI in the context of next-generation networks. First, we discuss the rise of network intelligence and then, we introduce a brief overview of AI with machine learning (ML) and their relationship to self-organization designs. Finally, we discuss design of intelligent agent and it’s functions to enable knowledge-driven wireless networks with AI. Knowledge-Driven Wireless Networks with Artificial Intelligence: Design, Challenges and Opportunities

We show that in a broad class of probabilistic random measures one may identify only three that are rescaled versions of themselves when restricted to a subspace. These are Poisson, binomial and negative binomial random measures. We provide some simple examples of possible applications of such measures. Throwing Stones and Collecting Bones: Looking for Poisson-like Random Measures

Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems for which the development of conventional engineering solutions is challenged by modelling or algorithmic deficiencies. This tutorial-style paper starts by addressing the questions of why and when such techniques can be useful. It then provides a high-level introduction to the basics of supervised and unsupervised learning with a focus on probabilistic models. For both supervised and unsupervised learning, exemplifying applications to communication networks are discussed by distinguishing tasks carried out at the edge and at the cloud segments of the network at different layers of the protocol stack. A Very Brief Introduction to Machine Learning With Applications to Communication Systems

Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible. Not only does this dramatically speed up and improve the design of machine learning pipelines or neural architectures, it also allows us to replace hand-engineered algorithms with novel approaches learned in a data-driven way. In this chapter, we provide an overview of the state of the art in this fascinating and continuously evolving field. Meta-Learning: A Survey