Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
Skip to next slide
You can skip to the next slide in
3
Ad
×
Your browser does not support the video tag.
Skip to next slide
Ad
×
Share Slideshare
LinkedIn
Facebook
Twitter
Embed
Size (px)
Show related Slideshows at end
WordPress Shortcode
Link
Share
Email
2024 Trend Updates: What Really Wor...
by Search Engine Jou...
1033581 views
Storytelling For The Web: Integrate...
by Chiara Aliotta
945137 views
Artificial Intelligence, Data and C...
by OECD Directorate ...
865593 views
How to Leverage AI to Boost Employe...
by SocialHRCamp
363144 views
2024 State of Marketing Report – by...
by Marius Sescu
206375 views
Everything You Need To Know About C...
by Expeed Software
226977 views
View on Slideshare
1
of
19
Ad
View on Slideshare
1
of
19
Ad
![Spectral Graph Convolutions (2)
• !" Λ [Hammond et al. 2011]
• $Λ$% & = $Λ&$%
• (′ ∈ ℝ,
• -Λ =
.
/012
Λ − 45 (7: 9 )
• ;9 ...](https://image.slidesharecdn.com/gcn1-181102025619/75/DL-Hacks-Semi-Supervised-Classification-with-Graph-Convolutional-Networks-9-2048.jpg?cb=1668113565)
![GCN
•
•
Karate Club [Brandes et al. 2008]
•
• ( )
•
•
•
12](https://image.slidesharecdn.com/gcn1-181102025619/75/DL-Hacks-Semi-Supervised-Classification-with-Graph-Convolutional-Networks-13-2048.jpg?cb=1668113565)
![•
• Ex. [Zhu et al., 2003]
•
•
• skip-gram
• Ex. DeepWalk [Perozzi et al., 2014]
• random walk
•
13](https://image.slidesharecdn.com/gcn1-181102025619/75/DL-Hacks-Semi-Supervised-Classification-with-Graph-Convolutional-Networks-14-2048.jpg?cb=1668113565)
![GCN
14
[X1 ]
[X2 ]
[X3 ]
[X4 ]
[Z1 ]
[Z2 ]
[Z3 ]
[Z4 ]
4 * K 4 * Q
• 1, 4 Loss ( )
•
2 GCN
Loss](https://image.slidesharecdn.com/gcn1-181102025619/75/DL-Hacks-Semi-Supervised-Classification-with-Graph-Convolutional-Networks-15-2048.jpg?cb=1668113565)
