Learning feature representation for graphs
This FYP aimed to implement and experiment novel frameworks to learn the feature representations for graphs. In order to be used for downstream tasks, numerous algorithms require the input graphs to be represented as fixed-length feature vectors. Graph2vec developed in 2017 was reported to achieve s...
Saved in:
Main Author: | Luong, Quynh Kha |
---|---|
Other Authors: | Chen Lihui |
Format: | Final Year Project |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/77558 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Graph contrastive learning
by: Tran, Nguyen Manh Thien
Published: (2023) -
Structured sparse representations for supervised and unsupervised learning
by: Zeng, Yijie
Published: (2020) -
Robust deep learning on graphs using neural PDEs
by: Gui, Pengzhe
Published: (2023) -
Machine learning techniques for program representation and comprehension with applications to mobile security
by: Narayanan, Annamalai
Published: (2017) -
Full stack development of online lifelong learning platform featuring peer assessment
by: Goh, Lee Hua
Published: (2023)