77 questions
Best practices
0
votes
1
replies
87
views
How to design similarity search for mechanical products
I have a website which shows different products about machines and its different parts. There are 10000 of product pages, and want to build a functionality which shows similar product pages and ...
- 165
0
votes
1
answer
60
views
Random split of edges in torch_geometric
In this link prediction example, here is how they split the data:
transform = T.Compose([
T.NormalizeFeatures(),
T.ToDevice(device),
T.RandomLinkSplit(num_val=0.05, num_test=0.1, ...
- 2,786
0
votes
0
answers
68
views
GraphMAE self-supervised learning: node attribute (sampled points) reconstruction works in minimal script but fails in full pipeline
I am experimenting with a GraphMAE self-supervised architecture using PyTorch + DGL.
In my task, each graph node represents a CAD entity, and one node attribute stores sampled points (coordinates + ...
- 1
3
votes
1
answer
74
views
How to decode a tfrecord based on the available meta data
I have a three tfrecords (train,test valid) from the a deepmind github repo (https://github.com/google-deepmind/deepmind-research/tree/master/meshgraphnets) that I am trying to decode (at the minute ...
1
vote
1
answer
99
views
GNN Link Prediction (GraphSAGE/PyG) - Validation AUC Consistently Below 0.5 Despite Overfitting Control
I'm working on a task dependency prediction problem using Graph Neural Networks with PyTorch Geometric. The goal is to predict directed precedence links (A -> B) between tasks within specific sets (...
- 101
0
votes
1
answer
92
views
How can I non-parametrically generate new geometry with machine learning?
Say, I have a few .stl files and want to non-parametrically generate a new geometry that is "interpolated" between the datset. For that, I plan on using neural networks that follows the ...
- 11
3
votes
1
answer
593
views
Link prediction using edge features/attributes in PyTorch geometric
I have a custom homogeneous graph dataset with undirected edges, where each edge has 22 features and each node has 2 features. I would like to perform link prediction on this dataset using the edge ...
- 31
0
votes
0
answers
59
views
What model can I use to get a dynamic path from one point to another as an output?
I am working on a drone and want to make it autonomous. As a first step, I decided to make a PyTorch algorithm that can output a path from one point to another. To do that, two random points are ...
0
votes
0
answers
49
views
PyTorch & DGL Matrix Multiplication Error: "mat1 and mat2 shapes cannot be multiplied (1x4581 and 1x4581)"
My code to train the ML model is as follows:
import dgl
from dgl.nn import GraphConv
import torch
import torch.nn as nn
import torch.nn.functional as F
class GCN(nn.Module):
def __init__(self, ...
0
votes
1
answer
127
views
Batch computation of similarity matrices of different sizes (Pytorch)
I am computing node features of dimension D for a B different graphs where the graph i has N_i nodes, hence I have a batch representation as a tensor of dimension (N_1 + ... + N_B)xD. I want to ...
0
votes
0
answers
270
views
ValueError: Total of weights must be finite
I am trying to create a classification algorithm using Graph Neural Networks (GNNs) in Python, but I am encountering an error that I can't resolve.
I have already tried removing NaN values from my ...
1
vote
1
answer
145
views
PyG graph autoencoder loss is frozen, possible Data object assembly issue
I'm trying to use Graph Autoencoder on a custom PyG Data object, but when I attempt to train it, the loss, AUC and AP do not change. The exact same autoencoder works when using PyTorch Geometric's ...
- 11
0
votes
0
answers
53
views
Graph convolutional networks does not produce the optimal results with the first run. Need to retrain the model from first run
I do not understand why I need to retrain the model starting with the model produced with the first run to improve the performance. Why increasing the number of epochs does not improve the results ...
- 13
1
vote
0
answers
102
views
Integrated Gradients implementation for 2D input in PyTorch
I am trying to implement the Integrated Gradient calculation (described in this article) for a GNN I am working with. Specifically, I am using Eq3 from the paper
My input for the network is a NxM ...
0
votes
0
answers
420
views
Edge Classification using Graph Neural Networks
I am working on a fraudulent transactions detection in SWIFT network using GGN.My graph has banking codes (SWIFT BIC codes) as nodes and the edges represent transactions. I also added an attribute ...
