Hello contributors,
I am a computer science undergraduate from Delhi Technological University
(formerly DCE). For quite some time now, I have been pursuing research in
graph embeddings and their applications in social media conjoined with nlp
based features. The work has been towards top conferences like ACL and
NAACL.
A lot of work has come to the fore for deep learning systems for graphs. I
will be very excited to develop some of the essential features in the
MLPACK library during GSOC'19. Following are some possible components that
I have outlined currently. I am also attaching links to the relevant
literature along with them.
a) Graph Convolution Layers:
- Spectral Layer - [1]
- Spatial Layer - [2] and [3]
b) Pooling Layers:
- Mean/Max/Sum Pooling - [4]
c) Graph Flattening Layer (for tasks like graph classification)
d) Sample codes on standard datasets for basic tasks and derivative
implementations like Graph autoencoders.
e) Adding support for node embedding methods like node2vec - [5]
All work will have to be supported by the necessary tests and documentation.
I would love to get inputs on this idea and assistance in structuring the
timeline for the goals. It would give a clearer view of the requirements
and a better assessment of the challenges.
Thanks and regards,
Pradyumn Sinha
[1] https://arxiv.org/pdf/1312.6203.pdf
[2]
https://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs
[3] http://proceedings.mlr.press/v70/gilmer17a/gilmer17a.pdf
[4] https://arxiv.org/pdf/1606.09375.pdf
<https://arxiv.org/pdf/1606.09375.pdf>
[5] https://arxiv.org/abs/1607.00653
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