Data Science Stack Exchange
Data Science Stack Exchange

Questions tagged [gnn]

12 questions
7
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1 answer

What is difference between transductive and inductive in GNN?

It seems in GNN(graph neural network), in transductive situation, we input the whole graph and we mask the label of valid data and predict the label for the valid data. But is seems in inductive situation, we also input the whole graph(but sample…
CoderOnly
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2
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1 answer

How to define similarity between nodes in original graph?

While there has been a lot of talk about defining the similarity between nodes in the embedding space, I don't seem to come across any talking about defining the similarity between nodes in the original, non-embedded graph. Any suggestions as to how…
Student
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2 answers

What is the model architectural difference between transductive GCN and inductive GraphSAGE?

Difference of the model design. It seems the difference is that GraphSAGE sample the data. But what is the difference in model architecture.
CoderOnly
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Preference learning for collaborative scheduling

I am working on a project of integrating the preferences of the workers into a schedule, I mean we won’t only satisfy the systematic constraints but also users preferences as constraints, so we are trying to learn these preferences from historical…
Tchikss
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Graph Clustering algorithms when both nodes *and* edges have features (numerical, categorical and potentially even temporal!)

I'm trying to figure out how much complexity I can get away with and am looking for model recommendations. I have transactional data on hand - the features being customer id, customer balance, transaction amount, transaction date/time, receiver id…
MergeMonster
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1 answer

Trouble Training GNN for Binary Node Classification Task

I am using a GNN to solve a problem in which I have a query target and an undirected graph. My goal is to emit a subset of nodes in the graph (via a node-wise binary prediction) whose features sum to the target query. I figured this would be…
mt_
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Graph Neural Network | How node embeddings are learned from several graphs?

I am reading paper on MEGnet which is a GNN. The objective is that we have several molecules that share same elements such as molecules $C0_2$ and $COOH$ share $C$ and $O$. Now if we learn the node embeddings of the both graphs via representation…
user0193
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When an author says Features are the input to Machine Learning Model what does it mean?

I am reading an article about graph neural network and it is mentioned: In this step, we extract all newly update hidden states and create a final feature vector describing the whole graph. This feature vector can be then used as input to a…
user0193
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0
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Matching nodes in two directed graphs

How to match a node of graph X with the same node in graph G if: Every node has only one feature: text string, and Nodes in different graphs are considered to be equal if: 2.1 Nodes have the same value of text feature, and 2.2 Nodes have…
dokondr
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My model is not learning

I am using the ogb molhiv dataset for graph classification, I imported the data and created the DataLoader following the ogb documentation. The data is composed of 41127 graphs and there are 2 classes. Below is the code to implement the neural…
edak
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2 answers

Same code vastly different accuracies

I am working on a node classification model, My friend implemented a simple 2 layer GCN and got an accuracy of 62%, I implemented the same code and got an accuracy of 50% we are both working on google collab, anyone knows why this is happening.
edak
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