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# Questions tagged [graphs]

Use for questions related to graph coloring and graph coloring games.

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### How can I prove "If the canonical forms of two graphs are not equivalent, then the graphs are definitively not isomorphic." on WL-test?

WL-Test is used for checking whether two graphs are isomorphic or not. It can make a graph to a canonical form. How can I prove that if canonical forms of two graphs are different, then they are non-...
• 115
1 vote
28 views

### 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 ...
1 vote
32 views

### 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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51 views

### 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 ...
116 views

### 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 ...
• 115
1 vote
17 views

### 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 ...
32 views

### 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 ...
1 vote
66 views

### 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 ...
33 views

### 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 ...
• 3,029
21 views

### Do I need to imagine loss curves of changing shapes in case of GANs?

Loss function, in general, is imagined as a curve in higher dimensional space with weights on input axes and loss on output axes. Suppose we have a neural network and we are training our neural ...
• 3,029
34 views

### 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 ...
• 21
1 vote
41 views

### 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 ...
• 31
1 vote
61 views

### 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 ...
94 views

### 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 ...
306 views

### 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 ...
1 vote
43 views

### 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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1 vote
88 views

### 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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85 views

### 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 ...
• 31
1 vote
52 views

### 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 ...
• 31
1 vote
27 views

### 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 ...
• 11
54 views

### 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 (...
• 121
535 views

### 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 ...
• 1,201
99 views

### 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 ...
65 views

### 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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17 views

### 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, ...
• 141
54 views

### 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 ...
• 141
733 views

### 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 ...
143 views

### 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 ...
192 views

### 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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1 vote
31 views

### 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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200 views

### 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 ...
• 179
28 views

### 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 ...
• 179
1 vote
169 views

### 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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### 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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531 views

### 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 ...
• 350
1 vote
31 views

### 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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151 views

### 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 ...
• 351
486 views

### Using graph searching to solve peg solitaire?

Problem: I've been reading research papers on how to solve a peg solitaire using graph searching, but all the papers kind of assume you know how to do the reduction(polynomial time conversion) from ...
• 351
4k views

### 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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1 vote
83 views

### 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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1k views

### 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 ...
• 155
1 vote
38 views

### 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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476 views

### 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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