Results 271 to 280 of about 8,419,643 (305)

Topical network embedding

Data Mining and Knowledge Discovery, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Min Shi, Yufei Tang, Xingquan Zhu, Jianxun Liu, Haibo He   +4 more
openaire   +3 more sources

PaCEr: Network Embedding From Positional to Structural

The Web Conference
Network embedding plays an important role in a variety of social network applications. Existing network embedding methods, explicitly or implicitly, can be categorized into positional embedding (PE) methods or structural embedding (SE) methods ...
Yuchen Yan, Yongyi Hu, Qinghai Zhou, Lihui Liu, Zhichen Zeng, Yuzhong Chen, Menghai Pan, Huiyuan Chen, Mahashweta Das, Hanghang Tong   +9 more
semanticscholar   +1 more source

LightNE: A Lightweight Graph Processing System for Network Embedding

SIGMOD Conference, 2021
We propose LightNE, a cost-effective, scalable, and high quality network embedding system that scales to graphs with hundreds of billions of edges on a single machine. In contrast to the mainstream belief that distributed architecture and GPUs are needed
J. Qiu, Laxman Dhulipala, Jie Tang, Richard Peng, Chi Wang   +4 more
semanticscholar   +1 more source

Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding

Web Search and Data Mining, 2020
Attributed network embedding is the task to learn a lower dimensional vector representation of the nodes of an attributed network, which can be used further for downstream network mining tasks.
S. Bandyopadhyay, N. Lokesh, Saley Vishal Vivek, M. Murty   +3 more
semanticscholar   +1 more source

PNE: Label Embedding Enhanced Network Embedding

2017
Unsupervised NRL (Network Representation Learning) methods only consider the network structure information, which makes their learned node representations less discriminative. To utilize the label information of the partially labeled network, several semi-supervised NRL methods are proposed.
Weizheng Chen, Xianling Mao, Xiangyu Li, Yan Zhang, Xiaoming Li   +4 more
openaire   +1 more source

Diffusion network embedding

Pattern Recognition, 2019
Abstract In network embedding, random walks play a fundamental role in preserving network structures. However, random walk methods have two limitations. First, they are unstable when either the sampling frequency or the number of node sequences changes.
Yong Shi, Minglong Lei, Hong Yang, Lingfeng Niu   +3 more
openaire   +1 more source

Network Embedding for Community Detection in Attributed Networks

ACM Transactions on Knowledge Discovery from Data, 2020
Community detection aims to partition network nodes into a set of clusters, such that nodes are more densely connected to each other within the same cluster than other clusters.
Heli Sun, Fang He, Jianbin Huang, Yizhou Sun, Y. Li, Chenyu Wang, Liang He, Zhongbin Sun, Xiaolin Jia   +8 more
semanticscholar   +1 more source

Networked embedded automation

Assembly Automation, 2006
PurposeThe aim of this research is to investigate whether a collection of tiny, resource constrained, microcontrollers that communicate with each other over wireless links can perform rigorous automation tasks.Design/methodology/approachWe identify three building blocks that are necessary to obtain large conveyor systems. The operation of each building
Nunzio Hayslip, Shivakumar Sastry, Jon S. Gerhardt   +2 more
openaire   +1 more source

EMBEDDED SENSOR NETWORK

Journal of ISMAC, 2023
Sensors are quite important in the current world. Sensors advance society in a number of areas, including the monitoring of environment as well as human health, safety, and security. Advanced military, agricultural, medical, and disaster management areas frequently employ sensor nodes to streamline monitoring by humans. Due to the fact that sensors are
openaire   +1 more source

Cross-Network Embedding for Multi-Network Alignment

The Web Conference, 2019
Recently, data mining through analyzing the complex structure and diverse relationships on multi-network has attracted much attention in both academia and industry. One crucial prerequisite for this kind of multi-network mining is to map the nodes across
Xiaokai Chu, Xinxin Fan, Di Yao, Zhihua Zhu, Jianhui Huang, Jingping Bi   +5 more
semanticscholar   +1 more source