GraphSAGE | Representation learning on large graphs | Machine Learning library
kandi X-RAY | GraphSAGE Summary
kandi X-RAY | GraphSAGE Summary
This directory contains code necessary to run the GraphSage algorithm. GraphSage can be viewed as a stochastic generalization of graph convolutions, and it is especially useful for massive, dynamic graphs that contain rich feature information. See our paper for details on the algorithm.
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Top functions reviewed by kandi - BETA
- Train the graph
- Return the next batch feed
- Create a feed dictionary
- Run random walk
- Make the label vector for a node
- Build the graph
- Sample from inputs
- Summarize loss
- Aggregate the given samples
- Calculate an incremental embedding dictionary
- Creates a feed dictionary from batch edges
- Load data from a JSON file
- Connects the model
- Runs the regression
- Run random walks
- Get the class labels for the given ids
- Calculate the loss between two inputs
- Compute the Hessian loss
- Calculate the skipgram loss
- Build an incremental embedding dictionary
GraphSAGE Key Features
GraphSAGE Examples and Code Snippets
Lines of Code : 140License : Permissive (Apache-2.0)
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To classify nodes, graph neural network performs message passing
discussed in :ref:`guide-message-passing` to utilize the node’s own
features, but also its neighboring node and edge features. Message
passing can be repeated multiple rounds to incorp
为了对节点进行分类，图神经网络执行了 :ref:`guide_cn-message-passing`
中介绍的消息传递机制，利用节点自身的特征和其邻节点及边的特征来计算节点的隐藏表示。
消息传递可以重复多轮，以利用更大范围的邻居信息。
编写神经网络模型
# 构建一个2层的GNN模型
import dgl.nn as dglnn
import torch.nn as nn
import torch.nn.functional as F
class SAGE(nn.Module):
d
One can use GPU-based neighborhood sampling with DGL data loaders via:
* Put the graph onto GPU.
* Put the ``train_nid`` onto GPU.
* Set ``device`` argument to a GPU device.
* Set ``num_workers`` argument to 0, because CUDA does not allow multip import argparse
import time
from contextlib import contextmanager
import numpy as np
import sklearn.linear_model as lm
import sklearn.metrics as skm
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim import argparse
import socket
import time
from contextlib import contextmanager
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import tqdm
import dgl
import dgl.nn.pytorch as d import argparse
import math
import time
import traceback
import numpy as np
import torch as th
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import tqdm
from torch.nn.parallel im Community Discussions
Trending Discussions on GraphSAGE
QUESTION
I am trying to run a link prediction using HinSAGE in the stellargraph python package.
I have a network of people and products, with edges from person to person (KNOWs) and person to products (BOUGHT). Both people and products got a property vector attached, albeit a different one from each type (Persons vector is 1024 products is 200). I am trying to create a link prediction algorithm from person to product based on all the information in the network. The reason for me for using HinSAGE is the option for inductive learning.
I have the code below, and I thought I was doing it similar to the examples
https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/hinsage-link-prediction.html https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/graphsage-link-prediction.html
but I keep getting "nan" as my output predictions, anyone got a suggestion to what I can try?
...ANSWER
Answered 2021-Nov-04 at 14:43So I found the problem, might be useful for others. If there is any node containing missing data, the thing will just produce NAs. Especially dangerous if you create your graph by joining pandas dataframes, I had a typo in one file that was integrated and led to the problem.
QUESTION
If my nodes look like:
...ANSWER
Answered 2021-Nov-28 at 19:04If you want to apply to run GraphSAGE on the string type attributes, you need to apply one hot encoding or some other technique to transform them into a number of a list of numbers. The property type cannot be a mix of various data types, it has to be consistent across all properties. AFAIK, this is valid for any library that includes GraphSAGE, not just Neo4j GDS.
Probably you can skip the id property as it doesn't bring in any additional information. For the city, name, and last name you can use either one hot encoding or word embeddings to include those properties in GraphSAGE, the decision is yours.
QUESTION
I am working on Neo4j graph embeddings. When I try to create embeddings using graphsage algorithm, the process takes a too long time. I see 4 parallel processes working with 100% CPU usage from htop result. Is there a way to use more cores? (I have 48 available cores). Also, I use community edition. And, my Cypher query is shown as below;
...ANSWER
Answered 2021-Sep-27 at 15:03Community edition of GDS only supports 4 cores. If you want to use more than 4 cores, you will need to purchase the enterprise GDS license.
QUESTION
I am trying to create dataset of multiple networkx graphs in json format. My goal is use to this dataset with labels for supervised training of my network. Networkx can dump json format for single graph. However, I am not sure how to handle multiple graphs in the same json file.
The GraphSage documentation claims that the example at https://github.com/williamleif/GraphSAGE/tree/master/example_data has multiple graphs.
The example_data subdirectory contains a small example of the protein-protein interaction data, which includes 3 training graphs + one validation graph and one test graph.
However, when I import the example-data toy-ppi-G.json into python I am not able to differentiate between different graphs or there is just a single graph. The data from json has following keys:
...ANSWER
Answered 2021-Feb-18 at 09:10The overall idea is that you can represent multiple graphs as disjoint components of one big graph. That's what they do in the GraphSAGE repository and you can do it as well.
You can store your multiple graphs in the same json file: as long as there are no edges between two distinct graphs, the GNN will "see" them as separate as well.
I explain this in the documentation of my library for GNNs, here's a representation of the adjacency and node features matrices, where colors represent distinct graphs:
So to answer your question, simply enumerate your nodes sequentially across graphs and add them to the json file. Keep the same indices to add edges.
QUESTION
I'm trying to build my GraphSAGE model using Keras but I get the following error:
...ANSWER
Answered 2020-Mar-30 at 04:21the exact problem/solution would depend on what version of stellargraph you're using, but if it's not a problem for you to use the latest version (0.11.0 at time of writing), I've made some adjustments to make it work:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install GraphSAGE
You can use GraphSAGE like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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