Lin Pan

Lin Pan
Tianjin University | tju · School of Marine Science and Technology

Ph.D.

About

20
Publications
1,579
Reads
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308
Citations
Citations since 2016
17 Research Items
291 Citations
20162017201820192020202120220102030405060
20162017201820192020202120220102030405060
20162017201820192020202120220102030405060
20162017201820192020202120220102030405060

Publications

Publications (20)
Article
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 community structure. In temporal networks, the identified communities should reflect the current snaps...
Article
Full-text available
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 usually limited. Considering random noises and irregular links, we propose a perturbation-based framew...
Article
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 semantic information of user preference and item features, and can alleviate the sparsity problems...
Article
Temporal community detection is helpful to discover and analyze significant groups or clusters hidden in dynamic networks in the real world. A variety of methods, such as modularity optimization, spectral method, and statistical network model, has been developed from diversified perspectives. Recently, network embedding-based technologies have made...
Article
Network Alignment (NA), which aims to find the nodes that represent the same entity (i.e., anchor nodes) across different networks, is a fundamental problem in many cross-network researches. Recent advances in network embedding have inspired various auspicious approaches for addressing the NA task, and embedding-based NA technology has become the m...
Article
A wide variety of Network Embedding methods focus on the proximity of networks, they learn community-oriented embedding for each node, where the representations are similar if two nodes are closer to each other in the network. Meanwhile, there is another type of structural similarity, i.e., role-based similarity, which is usually complementary and...
Article
Network embedding has been successfully used for a variety of tasks, e.g., node clustering, community detection, link prediction and evolution analysis on complex networks. For a given network, embedding methods are usually designed based on first-order proximity, second-order proximity, community constraints, etc. However, they are incapable of ca...
Article
Role-based network embedding aims to map network into low-dimensional node representations while preserving structural similarities. Adjacency matrix contain both the local and global information of a network, but it cannot directly represent the role of nodes. So it is essential to extract higher-order structural features from adjacency matrix for...
Chapter
Detecting community structure is an important research topic in complex network analysis. How to improve community detection results by using various features in the network is a very challenging problem. The scale-free and attributes of nodes are the two relatively independent aspects of the complex networks in the real world, the former is an inh...
Article
Full-text available
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 analysis. The existing embedding methods usually focus on the local structure of the network by capturi...
Chapter
Many E-commerce platforms allow users to write their opinions towards products, and these reviews contain rich semantic information for users and items. Hence review analysis has been widely used in recommendation systems. However, most existing review-based recommendation methods focus on a single view of reviews and ignore the diversity of users...
Article
Full-text available
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 structures, that is, more links within communities and fewer links between different communities, which ignore...
Article
Full-text available
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 NE are designed by merely using the local, high-order or global proximity to preserve the network s...
Article
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 the communities in dynamic networks. However, most of these methods ignore the varying characteristics...
Conference Paper
Taxis equipped with GPS can record their trajectory and generate huge amounts of data. We can analyzethe behavior of taxi drivers and search for similarities and common characteristics in their working patterns. In this paper, we utilize taxi GPS data collected form Tianjin city to analyze taxi drivers’ working pattern. Firstly, we determined taxis...
Article
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 communication under normal situations. Here, we study the temporal patterns of emergency calls, which...

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