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Graph Neural Networks: A Review of Methods and Applications
- visarga 8y agoGraph network reviews seem all the rage, here's another recent one: "A Comprehensive Survey on Graph Neural Networks " https://arxiv.org/abs/1901.00596 https://arxiv.org/abs/1901.00596
- karimtr 8y agoThere is definitely a lot of interest around the topic. Over 20 graph related papers where accepted for ICLR 2019 - https://openreview.net/group?id=ICLR.cc/2019/Conference https://openreview.net/group?id=ICLR.cc/2019/Conference
- stochastic_monk 8y agoIt’s worth stating that being able to work with hierarchical or graphical data is powerful and has very broad applications. The interest is not purely intellectual.
- macleginn 8y agoAlso this: https://arxiv.org/pdf/1812.04202.pdf https://arxiv.org/pdf/1812.04202.pdf (“Deep learning on graphs: A survey”).
- logancg 8y agoFor more, I highly recommend looking at the accepted papers from the NeurIPS 2018 Relational Representation Learning workshop. [1] I really enjoyed the workshop and I hear workshops tend to represent a (rough) frontier of the subfield. [1] https://r2learning.github.io/ https://r2learning.github.io/
- kaivi 8y agoI'm just curious - why do I primarily see Chinese researchers publishing deep learning stuff on arXiv? Is it subsidized over there? Just look at the publications linked in this thread so far, 2/3 are from Beijing.
- deleted 8y ago[deleted]
- logancg 8y agoThere are a lot of talented ML researchers in China. This is a product of (a) the government and major companies (i.e. BAT) investing heavily in fundamental ML research (b) the population size (c) a long tradition of STEM-focused education in China. So, it's not surprising that would be the case. The interesting questions are if China is uniquely focused on deep learning over other ML techniques, and Chinese research compares in terms of quality. Anecdotally (speaking as a researcher in the field) papers from Chinese institutions seem disproportionately focused on deep learning (whereas, for example, the UK does great work in Bayesian ML and the US does disproprotionately well in NLP). I'm not a deep learning researcher so I can't judge the technical merit, but I was just at NeurIPS in Montreal, and I saw about equal representation of Chinese institutions as South Korean ones. South Korea, with ~1/25 the population, punches way above its weight per capita.
- taneq 8y agoI thought the question was more "why arXiv and not other journals" in which case maybe 'prestigious' Western journal publications just aren't valued as much in China?
- nl 8y agoAlmost all ML research is published on arXiv. In ML (as in most of Comp Sci) conference proceedings (NeurIPS, ICML etc) are where the prestige publishing is.
- lettergram 8y agoPerhaps their papers are disproportionately not accepted to conferences so we see many more on arXiv
- HuShifang 8y agoI can't speak specifically to this topical domain, but generally speaking (assuming the research topic isn't politically sensitive) there are professional incentives to do transnational work -- whether it's publishing in Anglophone journals, organizing international conferences, etc. So probably, it's not that foreign journals are valued less. (Probably, in fact, the contrary.)
- a_bonobo 8y agoAre there any good Python libraries that make working with Graph Neural Networks as easy as working with Keras/Pytorch/fast.ai? All I can find is https://github.com/tkipf/gcn https://github.com/tkipf/gcn, and from the same authors reimplementations in Pytorch https://github.com/tkipf/pygcn https://github.com/tkipf/pygcn and Keras: https://github.com/tkipf/keras-gcn https://github.com/tkipf/keras-gcn many stars for the main repo (1100), but not that much usage?
- logancg 8y agoDeep Graph Library (DGL) [1] came out in November and I've heard good things. It looks easy and intuitive. [1] https://github.com/dmlc/dgl https://github.com/dmlc/dgl