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Node embedding is one of the most widely adopted techniques in numerous graph analysis tasks, such as node classification. Methods for node embedding can be broadly classified into three categories: proximity matrix factorization approaches, sampling ...

Infectious disease risk prediction plays a vital role in disease control and prevention. Recent studies in machine learning have attempted to incorporate epidemiological knowledge into the learning process to enhance the accuracy and informativeness of ...

Complex network models are helpful to explain the evolution rules of network structures, and also are the foundations of understanding and controlling complex networks. The existing studies (e.g., scale-free model, small-world model) are insufficient to ...

The COVID-19 pandemic has caused the society lockdowns and a large number of deaths in many countries. Potential transmission cluster discovery is to find all suspected users with infections, which is greatly needed to fast discover virus transmission ...

Robustness is one of the most important performance criteria for any rail transit network (RTN), because it helps us enhance the efficiency of RTN. Several studies have addressed the issue of RTN robustness primarily from the perspectives of given rail ...

As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision making by using interaction samples of an agent with its environment and the potentially delayed feedbacks. In contrast to traditional supervised learning that ...

Infectious disease data, recording the numbers of infection cases in different locations and time, is one of the most typical categories of spatio-temporal data and plays an important role in the infectious disease control and ...

In recent years, deep learning achieved significant efficiency on facial expression recognition (FER). While processing, the performance is directly determined by the quality of the dataset. However, blurred images and mislabeling often inevitably exist ...

In offline or online worlds, many social systems can be represented as signed social networks including both positive and negative relationships. Although a variety of studies on signed social networks have been conducted motivated by the great ...

Complex network clustering has been extensively studied in recent years, mostly through optimization approaches. In such approaches, the multi-objective optimization methods have been shown to be capable of overcoming the limitations (e.g., ...

Multi-task learning (MTL) aims to solve multiple related learning tasks simultaneously so that the useful information in one specific task can be utilized by other tasks in order to improve the learning performance of all tasks. Many ...

Predictive spatio-temporal analytics aims to analyze and model the data with both spatial and temporal attributes for future forecasting. Among various models proposed for predictive spatio-temporal analytics, the recurrent neural network (RNN) ...

Current methods for trajectory prediction operate in supervised manners, and therefore require vast quantities of corresponding ground truth data for training. In this paper, we present a novel, label-free algorithm, AutoTrajectory, for trajectory ...

Knowledge graph embedding aims to represent structured entities and relations as continuous and dense low-dimensional vectors. With more and more embedding models being proposed, it has been widely used in many tasks such as semantic search, ...

Understanding the dynamic properties of epidemic spreading on complex social networks is essential to make effective and efficient public health policies for epidemic prevention and control. In recent years, the concept of network embedding has ...

• Empirically prove that grassroots are better choices than elites in the IM problem.

How to select a set of seed users under a limited budget from the social networks to maximize information/influence diffusion is a critical task in the social computing filed, called as influence maximization (IM) problem. Existing ...

• There exist obvious correlations between the POIs-related human mobility and the socioeconomic indicators at city level.

In a city or region, understanding the relationship between human mobility and socioeconomic status is critical to public policies formulation, urban design and marketing strategies development. Based on the available massive geo-...

Healthcare service systems are of profound importance in promoting the public health and wellness of people. This book introduces a data-driven complex systems modeling approach (D2CSM) to systematically understand and improve the essence of healthcare ...

Diffusion speed and scale depend on all kinds of information, not just which users have the most or fewest connections.