scholarly journals Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification

2020 ◽  
Author(s):  
Lavender Y. Jiang ◽  
John Shi ◽  
Mark Cheung ◽  
Oren Wright ◽  
Jose M.F. Moura
Keyword(s):  
Node Classification

scholarly journals Discovering latent node Information by graph attention network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Weiwei Gu ◽  
Fei Gao ◽  
Xiaodan Lou ◽  
Jiang Zhang
Keyword(s):  
Supervised Learning ◽  
Link Prediction ◽  
Learning Algorithm ◽  
Network Visualization ◽  
Graph Structure ◽  
High Quality ◽  
Node Centrality ◽  
Influential Nodes ◽  
Node Classification ◽  
Highly Cited

AbstractIn this paper, we propose graph attention based network representation (GANR) which utilizes the graph attention architecture and takes graph structure as the supervised learning information. Compared with node classification based representations, GANR can be used to learn representation for any given graph. GANR is not only capable of learning high quality node representations that achieve a competitive performance on link prediction, network visualization and node classification but it can also extract meaningful attention weights that can be applied in node centrality measuring task. GANR can identify the leading venture capital investors, discover highly cited papers and find the most influential nodes in Susceptible Infected Recovered Model. We conclude that link structures in graphs are not limited on predicting linkage itself, it is capable of revealing latent node information in an unsupervised way once a appropriate learning algorithm, like GANR, is provided.

scholarly journals CT Demonstration of the 1996 AJCC-UICC Regional Lymph Node Classification for Lung Cancer Staging

Radiographics ◽  
1999 ◽  
Vol 19 (4) ◽  
pp. 899-899 ◽  
Author(s):  
Michel Cymbalista ◽  
Albert Waysberg ◽  
Claude Zacharias ◽  
Yves Ajavon ◽  
Marc Riquet ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lymph Node ◽  
Regional Lymph Node ◽  
Cancer Staging ◽  
Node Classification

scholarly journals Keyword Search over Probabilistic XML Documents Based on Node Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yue Zhao ◽  
Ye Yuan ◽  
Guoren Wang
Keyword(s):  
Keyword Search ◽  
Possible World ◽  
Xml Data ◽  
Fast Learning ◽  
Probabilistic Xml ◽  
Learning Speed ◽  
Xml Document ◽  
Probability Threshold ◽  
Node Classification ◽  
Learning Machine

This paper describes a keyword search measure on probabilistic XML data based on ELM (extreme learning machine). We use this method to carry out keyword search on probabilistic XML data. A probabilistic XML document differs from a traditional XML document to realize keyword search in the consideration of possible world semantics. A probabilistic XML document can be seen as a set of nodes consisting of ordinary nodes and distributional nodes. ELM has good performance in text classification applications. As the typical semistructured data; the label of XML data possesses the function of definition itself. Label and context of the node can be seen as the text data of this node. ELM offers significant advantages such as fast learning speed, ease of implementation, and effective node classification. Set intersection can compute SLCA quickly in the node sets which is classified by using ELM. In this paper, we adopt ELM to classify nodes and compute probability. We propose two algorithms that are based on ELM and probability threshold to improve the overall performance. The experimental results verify the benefits of our methods according to various evaluation metrics.

Sign in / Sign up

Export Citation Format

Share Document