Questions tagged [graphs]
Use for questions related to graph coloring and graph coloring games.
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ST-GCN: graph convolution operator in Geometry-Aware Interaction Network (GAIN)
I need help implementing the model in this paper:
They have adopted spatio-temporal graph convolution operator in ST-GCN [section 3.1.2]. I've found there is popular libraries available for GCN: ...
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Clustering of Graph with Binary Vertex Labels
Consider a graph data structure with unweighted edges, where each vertex has been assigned either 0 or 1.
I am wondering if there exists a good way of clustering this graph to detect communities. All ...
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Relevance of Weisfeiler–Lehman Graph Isomorphism Test limitation for Graph Neural Networks
Graph Neural Networks power is limited by the power of Weisfeiler–Lehman Graph Isomorphism algorithm.
Quoting wikipedia:
It has been demonstrated that GNNs cannot be more expressive than the
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How to prove that, for any graph $G$, the tree width of $G$ is $1$, if and only if $G$ is acyclic?
I am studying about CSPs, tree decomposition of a graph and tree width ($\text{TW}(G)$) of a graph - the smallest tree width of a tree decomposition.
I encountered the following problem:
Prove that ...
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Understanding relation between parameter sharing and Message Passing in Graph Neural Networks
Ravanbakhsh has clearly stated the relation between equivariance and parameter-sharing in neural networks. What I'm missing though, is where (and how) this relation becomes clear by considering the ...
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Does iterative deepening depth-first search expand at most twice as many nodes as breadth-first search?
My understanding is that iterative deepening search is roughly equivalent to breadth-first search, except instead of keeping all visited nodes in memory, we regenerate nodes as needed, trading off ...
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Model Suggestion for graph data
I am trying to figure out the right model/algorithm for a graph dataset to develop a machine learning pipeline. I have looked into Graph Neural Network(GNN) but all of the tutorials I found, trained ...
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Best algorithm for the Word Ladder puzzle
What would be the best performing algorithm to solve the Word Ladder problem, in terms of guaranteed finding of the shortest solution in the shortest possible time? Is it BFS, DFS, A*, IDA* or another ...
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What kind of features does each node have as an input graph to a graph neural network?
What kind of features does each node have as an input graph to a graph neural network? For example, we want to do image classification with GNN, what are the features of each pixel? Or if anyone could ...
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How to uniquely associate a directed graph with a feedforward neural network?
I want to write an algorithm that returns a unique directed graph (an adjacency matrix) that represents the structure of a given feedforward neural network (FNN). My idea is to deconstruct the FNN ...
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Why is the Graph Isomorphism Network powerful?
I am reading a paper known as GIN, How powerful are graph neural networks?, Xu et al. 2019
The paper, Lemma 5 and Corollary 6, introduces Graph Isomorphism Network (GIN).
In Lemma 5,
Moreover, any ...
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What is the reason behind using node embeddings?
I was reading Chapter 3 from the following book (here) on graph representation learning. The chapter is about node embeddings.
Question: What is the point of using node embeddings?
Do we use them:
to ...
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What are examples of node 'features' in graph networks?
Context: I was reading Chapter 3 in the following book (here) about graph representation learning. Before I get to node embeddings, I wanted to make sure that I do understand what is meant by the ...
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Can I extend Graph Convolutional Networks to graphs with weighted edges?
I'm researching spatio-temporal forecasting utilising GCN as a side project, and I am wondering if I can extend it by using a graph with weighted edges instead of a simple adjacency matrix with 1's ...
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Is there any geometrical interpretation on overcoming gradient related problems by adjusting/changing loss function?
There are instances in literature where we need to change loss function in order to escape from gradient problems.
Let $L_f$ be a loss function for a model I need to train on. Some times $L_f$ leads ...
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Incorrect node expansion in game board with A* search
I have the following game board below, and we're using A* search to find the optimal path from the agent to the key. There are 8 directions. Up, down, left, right have a cost of 1, and diagonal ...
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How to use unmodified input in neural network?
My question may be a bit hard to explain...
My neural network learns a categorical distribution, which serves as an index. This index will look up the value (= action_mean) in Input 2.
From this ...
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How does a GCN handle new input graphs?
Quick questions to see whether I understand GCNs correctly.
Is it correct that, if I have trained a GCN, it can take arbitrary graphs as input, assuming the feature size is the same?
I can't seem to ...
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1
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What exactly is the eigenspace of a graph (in spectral clustering)?
When we find the eigenvectors of a graph (say in the context of spectral clustering), what exactly is the vector space involved here? Of what vector space (or eigenspace) are we finding the ...
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2
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720
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What is Precision@K for link prediction in graph embedding meaning?
I am trying to re-implement the SDNE algorithm for graph embedding by PyTorch.
I get stuck at some issues about evaluation metric Precision@K.
precision@k is a metric which gives equal weight to the ...
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How to learn how to select a subgraph via reinforcement learning?
I have the following problem.
I am given a graph with a lot (>30000) nodes. Nodes are associated with a low (<10)-dimensional feature vector, and edges are associated with a low (<10)-...
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Applications of polar decomposition in Machine Learning
Assume there exists a new and very efficient algorithm for calculating the polar decomposition of a matrix $A=UP$, where $U$ is a unitary matrix and $P$ is a positive-semidefinite Hermitian matrix. ...
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How can I learn a graph given nodes with features in a supervised fashion?
I have a dataset and want to be able to construct a graph from it in a supervised fashion.
Let's assume I have a dataset with N nodes, each node has e.g. 10 features. Out of these N nodes, I want to ...
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Model for supervised sequence classification task
The Problem
I am currently working on a sequence classification problem I try to solve with machine learning.
The target variable is the current state of a system.
This target variable is following a ...
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How to update edge features in a graph using a loss function?
Given a directed, edge attributed graph G, where the edge attribute is a probability value, and a particular node N (with binary features f1 and f2) in G, the algorithm that I want to implement is as ...
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74
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Suitable deep learning algorithms for spatial / geometric data
I have a task of classifying spatial data from a geographic information system. More precisely, I need a way to filter out unnecessary line segments from the CAD system before loading into the GIS (...
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Can the degree and minimum remaining values heuristics be used in conjunction?
I am currently studying constraint satisfaction problems and have come across two heuristics for variable selection. The minimum remaining values(MRV) heuristic and the degree heuristic.
The MRV ...
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4
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1
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143
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How to solve the problem of variable-sized AST as input for a (convolutional) neural network model?
In my work I have a given source code for a module. From this module I generate an AST, whose size is dependent on the size of the module (e.g. more source code -> bigger AST). I want to train a ...
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1
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What is the difference between graph semi-supervised learning and normal semi-supervised learning?
Whenever I look for papers involving semi-supervised learning, I always find some that talk about graph semi-supervised learning (e.g. A Unified Framework for Data Poisoning Attack to Graph-based Semi-...
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Improving graph decoder network
I have been using a network to generate graphs. The architecture that I have been using is the following:
In this figure, $D_1$ is the signal generator and $D_2$ is the graph topology generator, ...
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Why does the ELBO come to a steady state and the latent space shrinks?
I'm trying to train a VAE using a graph dataset. However, my latent space shrinks epoch by epoch. Meanwhile, my ELBO plot comes to a steady state after a few epochs.
I tried to play around with ...
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3
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Is there an open-source implementation for graph convolution networks for weighted graphs?
Currently, I'm using a Python library, StellarGraph, to implement GCN. And I now have a situation where I have graphs with weighted edges. Unfortunately, StellarGraph doesn't support those graphs
I'm ...
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What are the exact meaning of "lower-order structure" and "higher-order structure" in this paper?
I recently read a paper on community detection in networks. In the paper EdMot: An Edge Enhancement Approach for Motif-aware Community Detection, the authors consider the "lower-order structure" of ...
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Does GraphSage use hard attention?
I was reading the recent paper Graph Representation Learning via Hard and Channel-Wise Attention Networks, where the authors claim that there is no hard attention operator for graph data.
From my ...
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What are the advantages of time-varying graph CNNs compared to fixed graph?
As I wrote in the title, what are the advantages of time-varying graph CNNs compared to fixed graph? For example, in CORA, which is a graph of citation relations of papers frequently used in graph CNN,...
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Is there a neural network method for time-varying directed graphs?
I want to study NN for time-varying directed graphs. However, as this field has developed relatively recently, it is difficult to find new ways. So the question is, is there any NN that can handle ...
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Random graph as input in geometric deep learning on time-varying graph
I want to create a framework that allows GDL to be applied to time-varying graphs.
I came up with the Erdos-renyi model as an example of a time-varying graphs.
GDL for graphs takes node information ...
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2
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400
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What are the differences between network analysis and geometric deep learning on graphs?
Both of them deal with data of graph structure like a network community. Is there a big difference there?
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42
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What are the benefits of using the state information that maintains the graph structure?
When you applying a graph structured data to the graph convolution network, what are the benefits of using the state information that maintains the graph structure?
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2
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Are there neural networks that accept graphs or trees as inputs?
As far I know, the RNN accepts a sequence as input and can produce as a sequence as output.
Are there neural networks that accept graphs or trees as inputs, so that to represent the relationships ...
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36
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Write Constraint Satisfaction Formulation for problem
Given $F_1,F_2,..,F_n$ as set of final exams of subjects taken by students $S_1,..,S_k$ in h slots such that no student takes two exams in a single slot.Here the objective is to maximize the number of ...
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What is the proof that the branch and bound algorithm always finds optimal path in a graph?
I've been studying Branch and Bound's graph algorithm and I hear it always finds the optimal path because it uses previously found solutions to find others
However, I haven't been able to find a ...
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912
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How to solve peg solitaire with a graph search?
Problem
I've been reading research papers on how to solve a peg solitaire using graph search, but all the papers kind of assume you know how to do the reduction(polynomial time conversion) from the ...
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What is geometric deep learning?
What is geometric deep learning (GDL)?
Here are a few sub-questions
How is it different from deep learning?
Why do we need GDL?
What are some applications of GDL?
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How to create meaningful multiple object detection evaluation comparison graph?
I have got multi-class object detector. One model accuracy evaluation of detection consists of: mAP, FP, FN, TP for each class divided to two graphs and looks like this (I've used this repo for ...
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What benefits can be got by applying Graph Convolutional Neural Network instead of ordinary CNN?
What benefits can we got by applying Graph Convolutional Neural Network instead of ordinary CNN? I mean if we can solve a problem by CNN, what is the reason should we convert to Graph Convolutional ...
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39
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How to visualize/interpret text prediction model results?
I am using LSTM model to predict the next xml markup from an input seed.
I have trained my model on 1500 xml files. Each xml file is generated randomly. I am wondering if there is a way to visualize ...
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2
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502
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Neural network for data visualization
At my work, we're currently doing some research into data visualisation for highly interconnected data, basically graphs.
We've been implementing all sorts of different layouts and trying to see which ...
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Machine learning with graph as input and output
In my application, I have inputs and outputs that could be represented as graphs. I have a number of acceptable pairs of input and output graphs. I want to use these to train a model.
I am looking ...
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