Questions tagged [graphs]
For the nodes and links sense of graph; use the visualization tag for the charting sense.
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how to derive this negative sampling approximation for graphs?
I am watching CS224W video (https://www.youtube.com/watch?v=Xv0wRy66Big&list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn&index=8) and have some issue understanding the following approximation:
Does ...
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Train test split in Graph Convolution Network for image classification
I am trying to construct a GCN for image classification where each pixel is a single node in the graph. However I want to train and test the model within the same graph, so I constructed a single ...
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How to Use Graph Learning Libraries to Predict Edges on a Graph where Each Node Has an Embedding?
An undirectional graph $\mathcal{G}$ has the set of nodes $\mathcal{N}$ where each node has an associated unique embedding of $512$ dimensions. Note that the embeddings themselves are fixed, and not ...
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When visualizing graph nodes, should I use apply PCA to node2vec embedding?
I am trying to visualize graph nodes using node2vec embedding.
The node2vec embeddings has lengths of 50~100 dimensions.
I have two plans:
use umap to project node2vec embeddings to 2D space
use PCA ...
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35
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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 ...
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What is degree assortativity coefficient of a complete undirected graph?
Because it computes the correlation coefficient of degrees and the correlation coefficient of constant arrays is not defined, networkx library returns ...
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2
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How to compute edge and node bottleneck coefficients in a weighted directed graph using networkx?
I am trying to analyze a weighted network, and I am focusing on identifying the bottleneck nodes and edges coefficients. I have never done that before on Python and I have the following code:
...
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Questions regarding Relational Graph Convolutional Network for a Fraud Detection problem
I am currently working on a transaction dataset (https://github.com/IBM/TabFormer/tree/main/data/credit_card) and I intend to build a fraud detection engine, but with tabular data transformed into a ...
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Terminology question - closed-form QA graph networks
I'm trying to find graph network research on closed-form sequential question answering, but whenever I include 'question-answering' I of course see cutting edge research into free-form conversational, ...
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Is there a Graph Neural Network that learns from its neighboring labels (and features)?
I built a heterogeneous graph on a citation graph with a Heterogeneous Graph Convolutional Neural Network in PyTorch and DGL. The graph structure looks like this: (author, writes, paper), (paper, ...
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Inductive embedding technique for nodes without features
I am working on a project that involves graph embeddings. I have an homogeneous directed graph where the nodes have no features. The only available information are the edges between nodes. I need to ...
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Are there any advantages on considering images as graphs and use them on Graph Convolutional Networks?
I have seen this encoding of an image as a graph:
The set of the nodes $V$ is the set of pixels. If the image is of size $10\times10$, then we have $10\cdot10=100$ pixels.
Each node has a length 3 ...
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25
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What books are there to learn to implement these graph algorithms?
I saw a post on Reddit (https://www.reddit.com/r/math/comments/ci50d3/visualizing_mathematical_subjects/) that utilizes label propagation, Fruchterman-Reingold algorithm, and edge betweenness ...
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understanding quadratic form in proof of positive definiteness of laplacian matrix
Consider the proof at page 2 found here: https://people.orie.cornell.edu/dpw/orie6334/Fall2016/lecture7.pdf
I cant wrap my head around the 2nd and third line:
\begin{align}
&= \sum_{i \in V}x(i)^2 ...
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NetworkX: Subgraph matching
Trying to match a Query subgraph to a Target graph, where:
Query:
and Target:
As I understand matching in this case should return tuples of matching nodes:
...
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37
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How to add label my stacked bar in R?
How can I add label per sub type?
Is it possible to also add a table below my graph.
With these info?
This is the my current code.
Thank you.
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83
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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:
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how to find out input data, its structure and how to achieve them on graph machine learning model?
I'm a newbie in graph machine learning and apologize if my question is silly. There is a model suggested in some paper for inductive link prediction, I need to use that model on my custom graph but I ...
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What does near-cut (threshold) graph say about original complete weighted graph?
CROSSPOST: https://math.stackexchange.com/q/4588249/117548
ORIGINAL post, preamble
Start from a complete graph with weighted edges (e.g. in $[0,1]$ interval). Continuously increasing threshold $t$ ...
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How to model flow circuit
I have a production circuit containing flows with different content and rate. Some of them are observable, some have covariate observations and some of them are totally not observable but can be ...
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50
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How to cluster components of a graph containing text data?
Suppose that I have a graph that has components like the image below.
Graph nodes contain text data (titles) and the edges data is the similarity (percentage).
I know that each component represents a ...
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66
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What are good Bipartite Graphs Algorithms?
Currently, I have a dataset with pairs .
The idea is to detect any anomaly in these relationships. I was able to just use pandas to do the analysis so far.
|Customer|Agent|Duration|Marks|etc
|C1.......
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How to generate a dynamic network
I need to implement a machine learning method called "OLCPM", this method uses for input a stream graph (dynamic network), the dataset they used is in this format :
...
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143
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Python - stock price for different stocks plotting (without yfinance)
I have a dataset which contains a few stocks in a range of dates.
I searched in internet but most of the solutions gives solutions with yfinance and I'm not ...
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What is purpose of diffusion matrix? What is its role in graph clustering? How do it serve its purpose by equation?
As titled, I am not quite sure what is purpose of Diffusion Matrix, which is support to clustering graph.
From this article (https://arxiv.org/pdf/2006.05582v1.pdf) I am might able to compute the ...
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What kind of object detection model is required?
I have some images, and my task is to build an object detection model for detecting tables and equipment photos in the image. The target would be detecting objects (table and equipment), so I can crop ...
- 111
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93
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METIS requests ~1000 TB of memory for problem
I am attempting to use METIS (configured for 64-bit, with a Python interface) to perform some weighted graph partitioning, but am immediately being hit with a memory allocation failure:
...
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35
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Cause of randomness in AUC score for GNN
I have implemented a GraphSAGE model using dgl for link prediction. On average the auc score of the model is ~0.7 but the score varies a lot for different runs. ...
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Method or tool to simulate weighted graphs with a specified weighted degree sequence
Are you aware of a method or tool to simulate a graph with that has a specified weighted degree sequence? They would be used to generate a distribution of bootstrap replicates.
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98
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How to describe this UMAP connectivity figure
I generated this UMAP connectivity diagram of my research data. How do I interpret/describe this plot regarding the UMAP connectivity?
Is it correct to say that:
As there is a lot of connectivity ...
- 147
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27
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May high closeness centrality in email graph be interpreted as high involvement?
I have a network graph where nodes are email addresses and their connections are emails they send to each other. I have calculated closeness centrality for each node and got couple with very high ...
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479
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Building a graph out of a large text corpus
I'm given a large amount of documents upon which I should perform various kinds of analysis. Since the documents are to be used as a foundation of a final product, I thought about building a graph out ...
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38
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Task of regression on graphs
Which tools are available to extract features from a graph.
After that, I would like to perform regressions on those features.
Initially, I was thinking about using the adjacency matrix of the graph. ...
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How can I store sources, effective dates, and confidence for every property in a knowledge graph?
What I am wanting to do is ensure that every property in a knowledge base comes from at least one source. I would like to ensure that every edge is spawned (or at least explained) by some event, like ...
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4
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How to perform inductive train/test split for GraphSAGE classification
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 ...
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Probability distributions for Directed Cyclic Graphs
Given a directed cyclic graph where vertex A is 'infected', and there are different infection probabilities between each node, what is the best approach towards computing the conditional probability $...
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Algorithms for Vertex or Node Correspondence
Given a graph G, and another graph with the same number of vertices G’, one can define a vertex correspondence function f, from the vertex set of G to the vertex set of G’. The correspondence function ...
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How to unify weights in my dataset
I have a symptom-disease network that consists of four attributes: symptom, disease,
co-occurrence and TF-IDF.
I'm considering the TF-IDF attribute as the weight of my network edges and symptom and ...
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35
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Tableau - Filter Buttons not filtering correctly
I created filter buttons to use as a filter for 6 category's (measures) in Tableau Public (latest version) so when you click on them the values for each display on my "Military Enrollment By ...
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Predicting the amount of new nodes discovered if a known node is extended in a graph
I'm currently working on a problem relating to Discord servers (sort of like a group chat within a social media platform), where I have a program recursively joining servers, looking for invites, and ...
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206
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Graph legend for plot in Base R for class differentiated data gives incorrect representation of actual category
I am new to R. While working on my university assignments, I found that legends for Base R plot do not show correct information, hence I switched to ggplot2 wherever legends were needed.
I observed ...
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Return the gradient and y intercept (m, b) to create two lines to best fit the data
I have been working on this task for a few hours now and have been unsuccessful with getting the target result. I have tried using multiple methods of trying to split the dataset using different ...
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Graph Pattern Matching Library
Assume that in an application, the user gives us a graph and we want to consider it as a pattern and find all occurrences of the pattern in a graph database (like neo4j). If we knew what the pattern ...
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Using MySQL Common Table Expressions to solve the travelling salesman problem
The problem I'm trying to solve is very similar to the travelling salesman problem, where there are many paths between nodes in the database. I've tried to edit my example to fit into this well-known ...
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weakly connected (or disconnected) graph enhancement
I'm trying to model a collection of security alerts into graph representation. Each alert consists of 0 or more objects that represent IP addresses, users, hostnames and so on. I consider two objects ...
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Node embeddings and densely connected graphs
I have a dataset of users and their interests (represented by categories) and I’m trying to embed the graph which results from connecting users if they have a common interest, so I’ll add an edge for ...
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Is there a metric for "cliquiness" for social graphs?
Regarding social network graphs, let us say that I am connected to 10 people, and that each of them are connected to 10 people. At one extreme this means that I have 100 unique $2^{nd}$ degree ...
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Which kind of technique is used for classifying a graph datastructure?
I have a 3D surface from which I extract certain spatial parameters like extremas and their locations (a.k.a coordinates), which then I convert into a bipartite graph with distance between them as ...
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Low-dimensional path representation learning
I have a graph (ex: map) and multiple sequences of ids representing different paths.
A vertex represents a region/area
An edge between 2 vertices : a crossing from a region to another
A graph path (...
