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Local Structure and High-Order Feature Preserved Network Embedding Based on Non-Negative Matrix Factorization

Network embedding, as an effective method of learning the low-dimensional representations of nodes, has been widely applied to various complex network analysis tasks, such as node classification, community detection, link prediction and evolution …

A Unified Bayesian Model for Generalized Community Detection in Attribute Networks

Identification of community structures and the underlying semantic characteristics of communities are essential tasks in complex network analysis. However, most methods proposed so far are typically only applicable to assortative community …

Hybrid Neural Recommendation with Joint Deep Representation Learning of Ratings and Reviews

Rating-based methods (e.g., collaborative filtering) in recommendation can explicitly model users and items from their rating patterns, nevertheless suffer from the natural data sparsity problem. In other hand, user-generated reviews can provide rich …

Deep Mutual Encode Model for Network Embedding from Structural Identity

Network Embedding (NE) is one of the most popular learning methods in complex networks. It aims at learning the low-dimensional representations of nodes in networks and has been applied in a variety of network analytic tasks. Most existing methods of …

A Novel Evolutionary Clustering via the First-Order Varying Information for Dynamic Networks

Temporal community detection could help us analyze and understand the meaningful substructure hidden within dynamic networks in the real world. Evolutionary clustering, as a popular framework for clustering stream data, has been denoted for mining …

A Perturbation-Based Framework for Link Prediction via Non-Negative Matrix Factorization

Many link prediction methods have been developed to infer unobserved links or predict latent links based on the observed network structure. However, due to network noises and irregular links in real network, the performances of existed methods are …

Autonomous Overlapping Community Detection in Temporal Networks: A Dynamic Bayesian Nonnegative Matrix Factorization Approach

A wide variety of natural or artificial systems can be modeled as time-varying or temporal networks. To understand the structural and functional properties of these time-varying networked systems, it is desirable to detect and analyze the evolving …

Temporal Patterns of Emergency Calls of a Metropolitan City in China

Quantitative understanding of human communication behavior, one of the fundamental human activities, is of great value in many practical problems, ranging from urban planning to emergency management. Most of the recent studies have focused on human …

A Comparative Analysis of Intra-City Human Mobility by Taxi

Quantitative understanding of human movement behaviors would provide helpful insights into the mechanisms of many socioeconomic phenomena. In this paper, we investigate human mobility patterns through analyzing taxi-trace datasets collected from five …

Fuzzy Overlapping Community detection based on local random walk and multidimensional scaling

A fuzzy overlapping community is an important kind of overlapping community in which each node belongs to each community to different extents. It exists in many real networks but how to identify a fuzzy overlapping community is still a challenging …