For a Neural Machine Translation (NMT) task, my input data has relational information. This relation could be modelled using a graphical structure. So one approach could be to use Graph Neural Network (GNN) and use a Graph2Seq model. But I can't find a good generational model for GNN. Instead, I want to use Transformer. But then the challenge is how can I embed structural information there? Is there any open source artefact for Relational Transformer that I can use out …

I'm facing an interesting problem involving medical images. We are set out to test an hypothesis if certain objects in an image affect the diagnosis of a patient. I would love to hear any comments regarding my pipeline but this is my current approach: Segment the image in order to obtain the object's regions. This would be done using off-the-shelf resnet and labeled data obtained from the manual annotation of the images in hand. Now, that I have the segmented …

From page 3 of this paper Knowledge Graph Embeddings and Explainable AI, they mentioned as below: Note that knowledge graph embeddings are different from Graph Neural Networks (GNNs). KG embedding models are in general shallow and linear models and should be distinguished from GNNs [78], which are neural networks that take relational structures as inputs However, it's still vague to me. It seems that we can get embeddings from both of them. What are the difference? How should we choose …

I have an issue with a model that I'm working on, I cannot show you the model architecture because it's basically confidential research. The model includes Graph convolutional networks and Transformer encoders combined to process and classify both sequences and graphs, I have dropout and layernorm and skip connections everywhere. Optimizer : RAdam with a reducelronplateau scheduler The issue is that the model learns pretty fast in a natural progression in the first 10 to 15 epochs but tends to …

I am trying to predict arrythmia using GCNN but the problem i am facing is that the data is in tabular format screenshot attached below. Upon reading i found out that there needs to nodes and edges for it to work on GCNN. So, how can i convert into the graph data?

I am trying to understand how GCNs work. For example, the well known GraphSAGE algorithm considers a graph $G$ with node features $x_i$ of dimension $n$. Then it propagates the node features over the graph by message passing. In a basic implementation of GraphSAGE, message passing is implemented by first averaging over the neighbour features, which is then passed through a linear transformation $W_2$. Finally, the original node features are added multiplied by another linear transformation $W_1$: $$ x_i' = …

I'm using PytorchGeometric to train a graph convolutional network for regression over nodes problem (the graph models physical phenomena in the network of sensors; the network of sensors is actually the network of measurements distributed across the power grid (powers, currents, voltages), and the goal of the GNN is to predict some unmeasured variables in the graph.). In the training dataset there graphs with different topologies (i.e. different edge_index tensors), and each of which has input and label tensors, which …

I am working on a document classifier that can perform the classification based on the document structure as well. My plan is to get the word embedding as well as the word coordinates and somehow combine the two features and pass it through a Graph Convolutional Network (GCN) to generate a graph embedding which I can then use to train a classifier. I was referencing this paper and they do data extraction by first getting the text embedding and image …

Let's say I have a network that consists of a single weakly connected component. From various papers I've seen that if you want to use inductive GNNs like GraphSAGE, it is advisable to split your train/test data into two separate graphs or components. Since I've seen that there are different approaches for node classification and link prediction tasks, I am specifically interested in node classification tasks, possible multiclass classification. So the train/test split graphs would need to ensure some sort …

As I understand, we use graph embedding to make a euclidean representation of non-euclidean structure - graph. Does it mean that conceptually we just take a step back to, may be, more complex, but still grid processing?

I am reading article on Graph Neural Network (GNN) and it is mentioned: The memory stores the states of all the nodes, acting as a compressed representation of the node’s past interactions. Why is the hidden state or node's memory considered compressed representation of the node's interaction? Isnt compressed representation is what auto-encoders yield?

Helow everyone. I am facing a problem on x axis and y axis. when i got the output then the output plot look like as,, The problem is output plot do not show the x-axis label and y axis label. Please help me in this regards while if i put the same code on jupitor notebook it shows the x-axis label and y axis label.

Little background on Work : I am working with ontologies and for my usecase I have to apply random walk on the ontology nodes/entities. In order to do the same I have written one function - that given a node it will output all its immediate neighbour nodes. Actual Problem : Recently I came across an ontology which along with normal nodes in the graph also has "union of other nodes" as an entity. But while going over the triple's …

I've developed a graph neural network using PyTorch Geometric. My model looks like: class GCN(torch.nn.Module): def __init__(self): super().__init__() self.conv1 = GCNConv(3, 32) self.conv2 = GCNConv(32, 64) self.conv3 = GCNConv(64, 32) self.fc1 = Linear(32, 10) def forward(self, data): x, edge_index, edge_attr = data.x, data.edge_index, data.edge_attr x = F.elu(self.conv1(x, edge_index)) x = F.elu(self.conv2(x, edge_index)) x = F.elu(self.conv3(x, edge_index)) x = F.elu(self.fc1(x)) return x I've generated a train and validation dataset, with the following learning curves: I do not know what kind of …

This is my first post here, just a brief presentation: my name is Gianmarco, I’m Medicinal Chemistry undergraduate student who is preparing his dissertation, my idea would be to create a classifier that can distinguish anticancer drugs as active or inactive and distinguish those active in three classes, describing the molecules as a graph. My supervisor suggested me to use the Random Forest classifier, to do this I need to convert my graph into a vector trying to keep as …

I have only just started to look into graph neural networks and I am a little confused on the node embedding process. Here is my understanding, please let me know if i misunderstood: Given unlabelled data, we try to construct a graph $G=(V,E)$ and generate a mask that contains important features of each node such as shape, size, intensity etc. Then let's say the aim is to try and classify the nodes. So, this is where we use node embedding, …

I am new to graph neural network (GNN). Without knowing a graph in advance, how can we possibly form an adjacency matrix? Assume there are 3 nodes (vertices): A, B, & C. There are could be many possible graphs with these 3 nodes such as: (A -- B -- C), (A -- B, A -- C), etc. many thanks

The main derivation of the probabilistic model in Node2Vec goes as follows (paper available on ArXiv: https://arxiv.org/pdf/1607.00653.pdf): We formulate feature learning in networks as a maximum likelihood optimization problem. Let $G=(V, E)$ be a given network. Our analysis is general and applies to any (un)directed, (un)weighted network. Let $f: V \rightarrow \mathbb{R}^{d}$ be the mapping function from nodes to feature representaions we aim to learn for a downstream prediction task. Here $d$ is a parameter specifying the number of dimensions …

I started to dig into GNNs for the first time and I have trouble understanding its advantages over NLP inspired embedding methods like DeepWalk and node2vec. Do GNNs only shine with node features? Or can they handle IDs/giant one-hot vectors as well? Does the usual input for GNNs only consist of a vector of handcrafted features? Are GNNs used directly for tasks like link prediction or they are just embedding generators for other models?

I have recently started studying GNN's. I have covered GCN and GraphSage so far. But I am confused regarding the process when testing occurs. Now suppose in the graph above I am using the nodes as train and test set as shown in the figure. Suppose I am using the GraphSage model for a supervised node-classification task , now during training I am providing the sub-graph with blue nodes and the weights(parameters) gets calculated using the neighbourhood information of the …