Intro to graph neural networks (ML Tech Talks)

[@TheStargazer1221]

Changed the literature, still incredibly humble. Great representation of a scientist.

[@victor-iyi]

Wow, I used to fear Graph Neural Networks thinking it was some sort of monster. But this presentation has changed everything for me. Excellent job Petar! Thank you, thank you so much!

[@SirajFlorida]

It's taken me a while to discover your lectures, but I can't thank you enough for creating and posting them. Thank you.

[@giovannibianco5996]

Great video Petar; now I understood everything and I will never ever have any kind of fear towards the gat. Now I am friend with the gat. We hang around often and apply leaky relu to beers in a bars. When we cross the streets he always reminds me to pay attention to the other edges and it is also very computationally efficient. Love it!

[@MMUnubi]

next level stuff right here

[@Amapramaadhy]

Really great content and presentation. The analogy between convolutional NN and GNN is one of the best I have heard. Petar should do more lectures

[@jingzhitay6736]

Thank you for this introduction! This might be the last GNN overview that I need to watch :)

[@WickedEssi]

Great explanation. Very calm and precise. Was a pleasure to listen to.

[@muhammadharris4470]

Thanks petar. Really love this intro to GNN been hearing about them for a while. needs got to know the actual graph computations and matrices with the context of ML

[@danielkorzekwa]

Great talk, excellent starting point to Graph Neural Networks. Presentation first + hands on tutorial.

[@fredquesnel1855]

Thanks for the great tutorial! Straight to the point, easy to understand, with an exercice that is easy to follow!

[@nikolayfx]

Thanks Petar for presenting GNN

[@Sangel67rus]

The brilliant explanations! Thank you, Petar!

[@dori8118]

Thanks for video i was in love with knowledge graphs, i am trying to back to it some day.

[@rogiervdw]

Marvellous explanation, thank you. Typo on 17:47 sum over j \in N_i ?

[@jimlbeaver]

Thanks...great stuff. I really appreciate you taking a slow and deliberate approach to this.

[@ihmond]

Thank you for your sample code! Most of models I found are written by pytorch. So, this keras model can be my basic reference.

[@NoNTr1v1aL]

Absolutely amazing video!

[@peterkonig9537]

Very clear presentation. It nicely combines concepts and exercises.

[@LouisChiaki]

Glad that Google improve the ETA of my home city - Taichung! The traffic there was really bad and it must be really difficult for the model 😂 .

[@MMUnubi]

lol

[@nastaranmarzban1419]

Hi, hope you're doing well, i have a problem, when i use "Spektral datasets.citation.load_data" I receive an error "Spektral datasets.citation has no attribute 'load_data' " Would anyone help me with this problem? Tkanks🙏

[@AlexMuresan]

So, at the time of this comment (spektral.__version__ == 1.0.8), loading the cora dataset would be something like this: cora_dataset = spektral.datasets.citation.Citation(name='cora') test_mask = cora_dataset.mask_te train_mask = cora_dataset.mask_tr val_mask = cora_dataset.mask_va graph = cora_dataset.graphs[0] # zero since it's just one graph inside, there could be multiple for other datasets features = graph.x adj = graph.a labels = graph.y Hope this is helpful!

[@phillibob55]

@@AlexMuresan Thankyou s much man!

[@DefendIntelligence]

Thank you it was really interesting

[@frankl1]

Thanks for this intro to GNN, I enjoyed it a lot

[@mytelevisionisdead]

Clearly explained! even more impressive given the information density of the content..!

[@phillibob55]

Those getting the error at load_data(), to quote @Alex Muresan's comment: So, at the time of this comment (spektral._version_ == 1.0.8), loading the cora dataset would be something like this: cora_dataset = spektral.datasets.citation.Citation(name='cora') test_mask = cora_dataset.mask_te train_mask = cora_dataset.mask_tr val_mask = cora_dataset.mask_va graph = cora_dataset.graphs[0] # zero since it's just one graph inside, there could be multiple for other datasets features = graph.x adj = graph.a labels = graph.y Hope this is helpful!

[@ayanansari4463]

it keeps returning /usr/local/lib/python3.7/dist-packages/scipy/sparse/_index.py:126: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient. self._set_arrayXarray(i, j, x) not sure it this is right?

[@phillibob55]

@@ayanansari4463 it'll give this warning, but it'll still work.

[@cybervigilante]

Consider graphs on our level - and even people are graphs. They exist only as nodes in a higher level network. But the edges of the higher level do not connect directly to any node in the lower level graph, otherwise you just have a lower level graph. The edges exert a Bias. Biases are common in nature - hormone biases, electrical biases, thermal biases, etc. However, there is a counter-bias feedback from the lower level graph, which can be any organism or complex structure, which can cause some higher level edges to either disconnect or connect in a benign or malign fashion, changing the bias. We provide the feedback. This explains very many things.

[@giorgigona]

Where can I see the presentation slides?

[@carltonchu1]

I just saw you on our DeepMind internal talks , then KZbin recommended this video to my personal account ?

[@mohajeramir]

This is so awesome. Excellent presenter

[@stephanembatchou5300]

Excellent content. Thank You!

[@nabeelhasan6593]

This is a very good series

[@toandaominh1997]

Thanks for the video. You bring useful knowledge

[@AliMohammedBakhietIssa]

Many Thanks for your efforts :)

[@payamkhorramshahi5726]

Very transparent tutorial ! Thank you

[@sleeping4cat]

Waiting eagerly for a custom Tensorflow Library on GNN!!

[@margheritamaraschini3958]

Great presentation. If it can be useful, I may have found some small typos: - "toward a simple update rule" A~=A~+I should be A~=A+I. Also, in one of the instances W should be transpose (W) - "GCN" The subscript of the sum I think it's the other way around

[@apaarsadhwani]

Thanks, that was useful!

[@Yuri-xm9rx]

Wonderful tutorial

[@masudcseku]

Thanks Petar, very comprehensive tutorial! It will be great if you can make a tutorial on GAT ;)

[@michielim]

This was so so useful - thank you!

[@randerson1184]

I'm going to get a TON of use out of these! Thanks!

[@ExperimentalAIML]

Good explanation

[@squarehead6c1]

Great tutorial!

[@turalsadik81]

Where can I find notebook of the colab exercise?

[@turalsadik81]

anybody?

[@timfaverjon3597]

I, thank you for the video, can I find the colab somewhere ?

[@dennisash7221]

I am trying to follow the example but I get the following error: AttributeError: module 'spektral.datasets.citation' has no attribute 'load_data' Anyone know why this is happening, I can only see load_binary in the attributes list.

[@sanketjoshi8387]

Did you fix the issue?

[@dennisash7221]

@@sanketjoshi8387 I have not found out what the issue is. It might be something to do with some upgrades to Python, NP or Spektral ... I am hoping someone can help

[@satyabansahoo1862]

@@dennisash7221 check the version of spektral, he is using its 0.6.2 so try using that

[@DanielBoyles]

# this should do it in Spektral Version 1.0.6 # I've used the same variable names, but haven't gone through the rest of the colab tutorial as yet from spektral.datasets.citation import Cora dataset = Cora() graph = dataset[0] adj, features, labels = graph.a, graph.x, graph.y train_mask, val_mask, test_mask = dataset.mask_tr, dataset.mask_va, dataset.mask_te

[@dennisash7221]

@@DanielBoyles awesome it seems to work, I will try to run the rest of the NB later but looks like this did the trick.

[@sachinvithubone4278]

Thanks for video. I think GNN can be used more in health care system.

[@innovationscode9909]

Thanks. Great stuff. I really LOVE ML

[@Max-eo6vx]

Thank you Peter. Would you share the code or notebook?

[@cetrusbr]

Fantastic Lecture! Thanks Petar, congrats for the amazing job!

[@miladto]

Thank you for this great Presentation. Can you please share the Colab?

[@ghensao4027]

Typo in 17:35 should iterate j over neighborhood of node N_i

[@thefastreviewer]

Is it possible to share the Colab file as well?

[@fahemhamou6170]

تحياتي الخالصة thank you

[@ernestocontreras-torres9188]

Great material!

[@brunoalvisio]

Thank you for the great intro! Qq: In the equation for GCN is the bias being omitted just for clarity?

[@39srini]

Very good useful video

[@_Intake__Gourab]

Hello, I am doing image classification using gcn, but I failed to understand how to use image data in a gcn model. I need some help!

[@philtoa334]

Nice.

[@BrendanW-c9l]

Please correct me if I'm wrong, Petar, but in the tutorial, it looks like during training we are including the full graph (including test nodes) in the node-pooling step? This looks like information leakage--is there some reason I'm missing why it's considered allowed here?

[@petarvelickovic6033]

This is correct, and it is only allowed under the "transductive" learning regime. In this regime, you're given a static graph, and you need to 'spread labels' to all other nodes. Conversely, in 'inductive' learning you are not allowed access to test nodes at training time. Naturally, the transductive regime is much easier, as you can use a lot of methods that exploit the properties of the graph structure provided. In inductive learning, instead, your method needs to in principle be capable of generalising to arbitrary, unseen, structures at test time.

[@rahulseetharaman4525]

Sir, could you please explain the part where the mask is divided by the mean ?

[@twitteranalyticsbyad3969]

Changing Cake to Pie, Nice move :D You can only understand if you have seen Jure Leskovec's lectures.

[@ThanhPham-xz2yo]

thanks for sharing!

[@RAZZKIRAN]

can we apply on text classifcation problems like sentiment analysis, online hate classifcations?

[@MrWater2]

incredible good!!!

[@pushkinarora5800]

Its a Binge watch!! epic!!

[@phaZZi6461]

thanks a lot!

[@slkslk7841]

What are Inductive problems?

[@halilibrahimakgun7569]

Can you share colab notebook

[@l.g.7694]

Really nice presentation! A question regarding the colab: Anyone else having the problem that the validation accuracy stays at around 13%?

[@l.g.7694]

This ... is unfortunate. I made a typo (mask = tf.reduce_mean(mask) instead of mask /= tf.reduce_mean(mask)) which I literally noticed after hitting send. Now it works.

[@vasylcf]

Thanks!

[@sunaryaseo]

A nice tutorial, now I am thinking about how to implement GNN for signal processing such as classification/prediction problems. How do I design the graph, nodes, and edges?

[@vibrationalmodes2729]

Strong last name dude (just started video, was my first impression 😂)

[@mohammadforutan955]

very useful

[@张凌峰-c2j]

Could I ask why mask should be divided by mean? Thanks

[@AvinashRanganath]

I think it is to prevent the model from overfitting to nodes with a larger number of edges.

[@werewolf_13]

Hey insightful lesson! Can anyone give me an idea on how to prepare a dataset for GNN? especially for recommendation systems

[@jtrtsay]

Love from Taichung city, Taiwan 🇹🇼

[@ScriptureFirst]

A lovely city in an island nation 🇹🇼

[@quickpresent8987]

Is anyone write the colab code following this video, I just get an error for the 'matmul

[@phillibob55]

Is anyone else getting accuracies higher than 1? (I know something's wrong but I can't figure it out)

[@taruneswar9036]

🙏🙏

[@wibulord926]

your source code pls

[@iskrabesamrtna]

inferring soft adjacency -- what does that even mean?

[@desrucca]

Total nodes = 2708 nodes Train = 140 nodes Valid = 500 nodes Test = 1000 nodes Where did the remaining 1068 nodes gone?

[@petarvelickovic6033]

They're still there -- their labels are just not assumed used for anything (training or eval) in this particular node split.

[@asedaradioshowpodcast]

27:35

[@cia05rf]

Great video, doesn't work with spektral 1.2.0. To save downgrading this can be used: ``` cora = spektral.datasets.citation.Cora() train_mask = cora.mask_tr val_mask = cora.mask_va test_mask = cora.mask_te graph = cora.read()[0] adj = cora.a features = graph.x labels = graph.y ```

[@muhannadobeidat]

Thanks for posting this. It's a time saver!

[@jackholloway7516]

1st

[@phillibob55]

If anyone gets the "TypeError: sparse matrix length is ambiguous; use getnnz() or shape[0]" error at the matmul, use adj.todense() while calling the train_cora() method.

[@oladipupoadekoya1559]

Hello sir, Please can i have your email sir. i need you to explain how to represent my optimisation problem in GNN

[@bdegraf]

Is there a link to the Colab code? I see references to it but not finding it.