Questions tagged [graphs]
Use for questions related to graph coloring and graph coloring games.
46 questions
0 votes
0 answers
21 views
Need Guidance on Gameplay Video Analysis for Storyline Graph Extractio
I'm a college student working on a project related to storyline graph extraction from gameplay videos and new player position identification in the graph. However, I'm completely clueless about how to ...
0 votes
0 answers
55 views
Is it feasible to solve dynamic graph-level classification without labels?
I already did graph-level classification using heterogeneous hypergraph learning in an ICDM paper last year. However, I now want to extend it for dynamic graphs, i.e. the task is dynamic graph-level ...
- 101
0 votes
0 answers
168 views
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: ...
5 votes
1 answer
640 views
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 ...
- 1,339
1 vote
0 answers
761 views
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 ...
- 111
0 votes
1 answer
224 views
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 ...
1 vote
0 answers
222 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 ...
- 11
1 vote
1 answer
792 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 ...
- 11
0 votes
1 answer
274 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 ...
- 23
1 vote
0 answers
332 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 ...
- 125
1 vote
0 answers
49 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 ...
- 165
2 votes
1 answer
2k 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 ...
- 165
2 votes
2 answers
2k 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 ...
- 21
1 vote
0 answers
77 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
1 answer
521 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 ...
