28
votes
What are graph embedding?
What are graph Embeddings ?
"Graph Embeddings" is a hot area today in machine learning. It basically means finding "latent vector representation" of graphs which captures the topology (in very basic ...
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22
votes
Accepted
What are graph embedding?
Graph embedding learns a mapping from a network to a vector space, while preserving relevant network properties.
Vector spaces are more amenable to data science than graphs. Graphs contain edges and ...
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17
votes
Anconda R version - How to upgrade to 4.0 and later
You need to create a new environment and then you can install R 4.+ in Anaconda. Follow these steps.
conda create --name r4-base
After activating ...
- 281
13
votes
Accepted
In elbow curve how to find the point from where the curve starts to rise?
TL;DR
Use the two functions from below to get the index of the elbow:
elbow_index = find_elbow(data, get_data_radiant(data))
**Edit:** I put all of the code below ...
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9
votes
Accepted
Large Graphs: NetworkX distributed alternative
Good , old and unsolved question! Distributed processing of large graphs as far as I know (speaking as a graph guy) has 2 different approaches, with the knowledge of Big Data frameworks or without it.
...
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9
votes
How to use Scikit-Learn Label Propagation on graph structured data?
Answering my own question here, as I hope it will be useful to some readers.
Scikit-learn is primarily designed to deal with vector structured data. Hence, if you want to perform label propagation/...
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7
votes
Accepted
Use Cases of Neo4J and Spark GraphX
Neo4j and Spark GraphX are meant for solving problem at different level and they are complimentary to each other.
They can be connected by Neo4j's Mazerunner extension:
Mazerunner is a Neo4j ...
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6
votes
Accepted
Visualize graph network with more than 30k edges
Yes There are!
Networkx
I think 20k-30k node-edge would be OK on Networkx, IF YOU HAVE A GOOD MACHINE! Networkx is a great library in Python particularly for Graph Analysis so you have access to ...
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6
votes
Large Graphs: NetworkX distributed alternative
In this present moment, Apache has develop a powerfull API called PySpark. And you can setup Graphframes directly from pyspark command line. Launch from you shell ...
5
votes
What is the difference between affinity matrix eigenvectors and graph Laplacian eigenvectors in the context of spectral clustering?
The concept is the same but you are getting confused by the type of data. Spectral Clustering as Ng et al. explain is about clustering standard data while the Laplacian matrix is a graph derived ...
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5
votes
Accepted
Network analysis classic datasets
What you are looking for can be found in KONECT (the website is down as I'm writing this but it should be fixed soon!). It's almost the most comprehensive data collection for network analysis. But the ...
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5
votes
Finding groups of friends in social network data
Community Detection and Clique Percolation:
This is a community detection problem. Here is a very detailed review article surveying the state of the art.
The Clique Percolation Method is also ...
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5
votes
Accepted
Use cases for graph algorithms and graph data structures in finance and banking
There are many use cases of graph theory in Finance industry and it is a very broad question. As Emre said can be used for Fraud Detection, Risk Modelling, Economic Networks etc.
These below links ...
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4
votes
Accepted
What is a discrimination threshold of binary classifier?
Just to add a bit.
Like it was mentioned before, if you have a classifier (probabilistic) your output is a probability (a number between 0 and 1), ideally you want to say that everything larger than ...
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4
votes
What is a discrimination threshold of binary classifier?
Classifiers often return probabilities of belonging to a class. For example in logistic regression the predicted values are the predicted probability of belonging to the non-reference class or $\text{...
- 205
4
votes
Partitioning Weighted Undirected Graph
So what you need is Modularity score. Speaking as a Graph Clustering guy (my master thesis topic, PhD research and my main research direction during last 2.5 years) I recommend you to go through what ...
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4
votes
Accepted
Learning time of arrival (ETA) from historical location data of vehicle
Based on what I figured out from your problem:
1
You can easily convert your data to a graph using Networkx, igraph or any other tool/library/software. Then what you need is a Shortest Path ...
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4
votes
collaborative filtering using graph and machine learning
I can only speak about Graphs:
Advantages:
Using graphs, you can easily find products bought/rated by users that bought or liked an item, or users that have similar "taste" to another user. From my ...
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4
votes
Accepted
Organize TSNE data into grid
There seem to be a few options, but I found rasterfairy which is very easy to install and use. Has the added bonus of being able to fit to a rectangular grid, but also circular and other arbitrary ...
- 171
4
votes
Accepted
Clustering Multiple Networks
Your question is not clear in a way there are two different Graph Clustering problems. One is having a dataset of different graphs and you would like to cluster similar graphs (in this case each ...
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4
votes
Accepted
Struggling to understand GCNNs (Graph Convolutional Neural Networks)
The name "Graph Convolutional Neural Network" is a bit misleading, as no "traditional" convolutions (like in the context of CNNs) take place at all. You are correct that it doesn't really make sense ...
- 3,870
3
votes
collaborative filtering using graph and machine learning
One advantage of many ML-based recommendation techniques is they allow you to work in a lower-dimensional space. Matrix factorization techniques for example, allow you to view a user or an item in ...
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3
votes
Accepted
Why are HMMs called linear-chain?
It is so called because it classifies the linear sequence, and not because the structure of the graph.
- 173
3
votes
Partitioning Weighted Undirected Graph
Even a simple Internet search reveals numerous papers on graph clustering approaches and algorithms. This paper is most likely the best starting point, as it presents a rather comprehensive overview ...
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3
votes
Creating a Graph from Address Data
Speaking as a Graph/Complex Networks guy I'd recommend Networkx package in Python. This is the main library I used for my master thesis and my research during last 2 years. As long as your graph is ...
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3
votes
Which Graph Properties are Useful for Predictive Analytics?
Using Community detection you can build a recommendation system. The most commonly used algorithm in this field is Blondel Algorithm which u have probably seen in SNAP. Blondel is almost the fastest ...
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3
votes
Accepted
What is a Recurrent Heavy Subgraph?
The term may best be expressed as a Recurrent, Heavy Subgraph. That is, a subgraph which is both Recurrent and Heavy.
Heaviness of a subgraph refers to heavily connected vertices- that is, nodes ...
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3
votes
Graph to display differences (or lack of) in multilevel categorical data
OK, here are my attempts with R & ggplot2
1 Simple stacked histogram
2 Dodged stacked histogram ~ bacteria
3 Dodged histogram ~ Culture
4 Dodged histogram ~ change
5 Grouped by number of ...
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3
votes
Accepted
How do you set sigma for the Gaussian similarity kernel?
Updated Answer
According to a reference paper in Spectral Clustering (von Luxburg) the $\sigma$ is simply set to 1. A further tuning can be applied with some visualization inspection but I did not ...
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Only top scored, non community-wiki answers of a minimum length are eligible