Results 41 to 50 of about 3,653 (212)
Attributed Network Embedding for Incomplete Attributed Networks
, 2018 Attributed networks are ubiquitous since a network often comes with auxiliary attribute information e.g. a social network with user profiles. Attributed Network Embedding (ANE) has recently attracted considerable attention, which aims to learn unified low dimensional node embeddings while preserving both structural and attribute information.
Hou, Chengbin, He, Shan, Tang, Ke
openaire +2 more sources
Flexible Attributed Network Embedding
CoRR, 2018 Network embedding aims to find a way to encode network by learning an embedding vector for each node in the network. The network often has property information which is highly informative with respect to the node's position and role in the network. Most network embedding methods fail to utilize this information during network representation learning ...
Enya Shen +3 more
openaire +2 more sources
A new attributed graph clustering by using label propagation in complex networks
Journal of King Saud University: Computer and Information Sciences, 2022 The diffusion method is one of the main methods of community detection in complex networks. In this method, the use of the concept that diffusion within the nodes that are members of a community is faster than the diffusion of nodes that are not in the ...
Kamal Berahmand +3 more
doaj +1 more source
Generating Attributed Networks with Communities
PLOS ONE, 2015 In many modern applications data is represented in the form of nodes and their relationships, forming an information network. When nodes are described with a set of attributes we have an attributed network. Nodes and their relationships tend to naturally form into communities or clusters, and discovering these communities is paramount to many ...
Christine Largeron +3 more
openaire +4 more sources
Techniques for the dynamic randomization of network attributes [PDF]
2015 International Carnahan Conference on Security Technology (ICCST), 2015 Critical infrastructure control systems continue to foster predictable communication paths and static configurations that allow easy access to our networked critical infrastructure around the world. This makes them attractive and easy targets for cyber-attack.
Adrian R. Chavez +2 more
openaire +3 more sources
Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding
Future Internet, 2020 Smartphones and applications have become widespread more and more. Thus, using the hardware and software of users’ mobile phones, we can get a large amount of personal data, in which a large part is about the user’s application usage patterns.
Yifei Zhou, Shaoyong Li, Yaping Liu
doaj +1 more source
Attributed network embedding based on self-attention mechanism for recommendation method
Scientific Reports, 2023 Network embedding is a technique used to learn a low-dimensional vector representation for each node in a network. This method has been proven effective in network mining tasks, especially in the area of recommendation systems.
Shuo Wang, Jing Yang, Fanshu Shang
doaj +1 more source
A Network Embedding-Enhanced NMF Method for Finding Communities in Attributed Networks
IEEE Access, 2022 Community detection is an extremely important task for complex network analysis. There still remains a challenge of how to improve the performance of community detection in real-world scenario.
Jinxin Cao +6 more
doaj +1 more source
Frontiers in Physics, 2022 Node classification, as a central task in the graph data analysis, has been studied extensively with network embedding technique for single-layer graph network. However, there are some obstacles when extending the single-layer network embedding technique
Beibei Han +4 more
doaj +1 more source
Sampling from Social Networks with Attributes [PDF]
Proceedings of the 26th International Conference on World Wide Web, 2017 Sampling from large networks represents a fundamental challenge for social network research. In this paper, we explore the sensitivity of different sampling techniques (node sampling, edge sampling, random walk sampling, and snowball sampling) on social networks with attributes.
Wagner, Claudia +4 more
openaire +3 more sources