Results 41 to 50 of about 170,883 (277)

MSGCN: Multi-Subgraph Based Heterogeneous Graph Convolution Network Embedding

Applied Sciences, 2021
Heterogeneous graph embedding has become a hot topic in network embedding in recent years and has been widely used in lots of practical scenarios.
Junhui Chen, Feihu Huang, Jian Peng
doaj   +1 more source

Attributed Network Embedding for Learning in a Dynamic Environment

, 2018
Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation for each node in the network. The learned embeddings could advance various learning tasks such as node classification, network clustering, and
Chang, Yi, Dani, Harsh, Hu, Xia, Li, Jundong, Liu, Huan, Tang, Jiliang   +5 more
core   +1 more source

Graph Few-shot Learning via Knowledge Transfer

, 2020
Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest recently, which update the representation of each node by aggregating ...
Chawla, Nitesh V., Huang, Junzhou, Jiang, Meng, Li, Zhenhui, Wang, Suhang, Wei, Ying, Yao, Huaxiu, Zhang, Chuxu   +7 more
core   +1 more source

Node Embeddings via Neighbor Embeddings

CoRR
Accepted to Transactions of Machine Learning Research (TMLR)
Jan Niklas Böhm, Marius Keute, Alica Guzmán, Sebastian Damrich, Andrew Draganov, Dmitry Kobak   +5 more
openaire   +3 more sources

Node Replacements in Embedding Normal Form

Journal of Computer and System Sciences, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Konstantin Skodinis, Egon Wanke
openaire   +2 more sources

From Static to Dynamic Node Embeddings

CoRR, 2020
We introduce a general framework for leveraging graph stream data for temporal prediction-based applications. Our proposed framework includes novel methods for learning an appropriate graph time-series representation, modeling and weighting the temporal dependencies, and generalizing existing embedding methods for such data.
Di Jin 0003, Sungchul Kim, Ryan A. Rossi, Danai Koutra   +3 more
openaire   +2 more sources

Layer Information Similarity Concerned Network Embedding

Complexity, 2021
Great achievements have been made in network embedding based on single-layer networks. However, there are a variety of scenarios and systems that can be presented as multiplex networks, which can reveal more interesting patterns hidden in the data ...
Ruili Lu, Pengfei Jiao, Yinghui Wang, Huaming Wu, Xue Chen   +4 more
doaj   +1 more source

SNE: Signed Network Embedding

, 2017
Several network embedding models have been developed for unsigned networks. However, these models based on skip-gram cannot be applied to signed networks because they can only deal with one type of link.
F Heider, J Duchi, L Wu, LVD Maaten, R Collobert, Y Bengio, Y Bengio, Y LeCun, Y Yang   +8 more
core   +1 more source

LATTE: Application Oriented Social Network Embedding

, 2019
In recent years, many research works propose to embed the network structured data into a low-dimensional feature space, where each node is represented as a feature vector.
Bai, Jiyang, Meng, Lin, Zhang, Jiawei
core   +1 more source

Fat Polygonal Partitions with Applications to Visualization and Embeddings [PDF]

, 2013
Let $\mathcal{T}$ be a rooted and weighted tree, where the weight of any node is equal to the sum of the weights of its children. The popular Treemap algorithm visualizes such a tree as a hierarchical partition of a square into rectangles, where the area
de Berg, Mark, Onak, Krzysztof, Sidiropoulos, Anastasios   +2 more
core   +4 more sources