Results 31 to 40 of about 30,518 (252)

HEAT: Hyperbolic Embedding of Attributed Networks [PDF]

, 2020
15 pages, 4 ...
David W. McDonald, Shan He 0001
openaire   +2 more sources

Nane: A Node2vec Extension for Attributed Network Embedding [PDF]

Interdisciplinary Description of Complex Systems
Traditional network representation learning methods focus solely on the network’s topology, ignoring other sources of information that could improve the learning process.
Sarah Abdulkareem Ahmed Ahmed, Serkan Savaş   +1 more
doaj   +1 more source

Fair Benchmark for Unsupervised Node Representation Learning

Algorithms, 2022
Most machine-learning algorithms assume that instances are independent of each other. This does not hold for networked data. Node representation learning (NRL) aims to learn low-dimensional vectors to represent nodes in a network, such that all ...
Zhihao Guo, Shengyuan Chen, Xiao Huang, Zhiqiang Qian, Chunsing Yu, Yan Xu, Fang Ding   +6 more
doaj   +1 more source

Survey on graph embeddings and their applications to machine learning problems on graphs [PDF]

PeerJ Computer Science, 2021
Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model.
Ilya Makarov, Dmitrii Kiselev, Nikita Nikitinsky, Lovro Subelj   +3 more
doaj   +2 more sources

Outlier Aware Network Embedding for Attributed Networks

Proceedings of the AAAI Conference on Artificial Intelligence, 2019
Attributed network embedding has received much interest from the research community as most of the networks come with some content in each node, which is also known as node attributes. Existing attributed network approaches work well when the network is consistent in structure and attributes, and nodes behave as expected.
Sambaran Bandyopadhyay, Lokesh Nagalapatti, M. Narasimha Murty   +2 more
openaire   +3 more sources

Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding

Data Science and Engineering, 2019
Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years.
Vachik S. Dave, Baichuan Zhang, Pin-Yu Chen, Mohammad Al Hasan   +3 more
doaj   +1 more source

Attributed Network Embedding Using an Improved Weisfeiler-Lehman Schema and a Novel Deep Skip-Gram

IEEE Access, 2023
Attributed Network Embedding (ANE) and the representation of its nodes in a low-dimensional space is a pivotal step in the analysis of real-world networks.
Amr Al-Furas, Mohammed F. Alrahmawy, Abdulaziz Alblwi, Waleed Mohamed Al-Adrousy, Samir Elmougy   +4 more
doaj   +1 more source

Mobile App Start-Up Prediction Based on Federated Learning and Attributed Heterogeneous Network Embedding

Future Internet, 2021
At present, most mobile App start-up prediction algorithms are only trained and predicted based on single-user data. They cannot integrate the data of all users to mine the correlation between users, and cannot alleviate the cold start problem of new ...
Shaoyong Li, Liang Lv, Xiaoya Li, Zhaoyun Ding   +3 more
doaj   +1 more source

Dynamic heterogeneous attributed network embedding

Information Sciences
Wenli Zheng, Feilong Tang, Yitong Song   +2 more
exaly   +2 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