Results 231 to 240 of about 30,518 (252)
Some of the next articles are maybe not open access.
Deep Attributed Network Embedding Based on the PPMI
2021The attributed network embedding aims to learn the latent low-dimensional representations of nodes, while preserving the neighborhood relationship of nodes in the network topology as well as the similarities of attribute features. In this paper, we propose a deep model based on the positive point-wise mutual information (PPMI) for attributed network ...
Kunjie Dong +4 more
openaire +1 more source
Attributed Heterogeneous Network Embedding for Link Prediction
2021Network embedding aims to embed the network into a low-dimensional vector space wherein the structural characteristic of the network and the attribute information of nodes are preserved as much as possible. Many existing network embedding works focused on the homogeneous or heterogeneous plain networks.
Tingting Wang, Weiwei Yuan, Donghai Guan
openaire +1 more source
Fast Attributed Multiplex Heterogeneous Network Embedding
Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020In recent years, heterogeneous network representation learning has attracted considerable attentions with the consideration of multiple node types. However, most of them ignore the rich set of network attributes (attributed network) and different types of relations (multiplex network), which can hardly recognize the multi-modal contextual signals ...
Zhijun Liu +4 more
openaire +1 more source
A scalable attribute-aware network embedding system
Neurocomputing, 2019Abstract Network embedding, which aims to generate dense, low-dimensional and representative embedding representations for all nodes in the network, is a crucial step for various AI-based tasks related to network analytics, such as node classification, community detection, and link prediction. In addition to network topology, node attributes are also
Weiyi Liu +5 more
openaire +1 more source
Adaptive Attributed Network Embedding for Community Detection
2020Community detection, which discovers densely-connected groups of nodes in networks, is a fundamental task in machine learning and data mining. Compared with plain network, community detection in attributed network presents more challenges. Several recent embedding-based methods have achieved promising community detection performance on some real ...
Mengqing Luo, Hui Yan
openaire +1 more source
A Dual Fusion Model for Attributed Network Embedding
2020Attributed network embedding (ANE) maps nodes in network into the low-dimensional space while preserving proximities of both node attributes and network topology. Existing methods for ANE integrated node attributes and network topology by three fusion strategies: the early fusion (EF), the synchronous fusion (SF) and the late fusion (LF).
Kunjie Dong +3 more
openaire +1 more source
Hyperbolic Embedding of Attributed and Directed Networks
IEEE Transactions on Knowledge and Data Engineering, 2022David W. McDonald, Shan He
openaire +1 more source
Adversarial enhanced attributed network embedding
Knowledge and Information Systems, 2023Lei Chen 0045 +5 more
openaire +1 more source
Learning asymmetric embedding for attributed networks via convolutional neural network
Expert Systems With Applications, 2023Mohammadreza Radmanesh +2 more
exaly
Fusing attributed and topological global-relations for network embedding
Information Sciences, 2021Hui Yu, Junyu Dong, Claudia Plant
exaly