Artificial Intelligence Stack Exchange
Artificial Intelligence Stack Exchange

Questions tagged [graphs]

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

In graph theory, graph coloring is a special case of graph labeling; it is an assignment of labels traditionally called "colors" to elements of a graph subject to certain constraints.
Graph Coloring

Graph coloring games are mathematical games related to graph theory. Coloring game problems arose as game-theoretic versions of well-known graph coloring problems.
Graph Coloring Game

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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?
nbro
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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 Neural Network to solve it? Are there any examples…
piratesailor
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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 looking for an open-source implementation for…
port trum
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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 fits best, but, due to the nature of the problem…
tiansivive
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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 for pointers where simple examples of learning…
Suresh
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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 Weisfeiler–Lehman Graph Isomorphism Test. In practice,…
Rexcirus
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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 neural network model which will learn a general…
Michael Kročka
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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 the network at the level of individual nodes and…
hichewness
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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 between the nodes of the graph or tree?
user8426627
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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 proof of why it finds the optimal path. (In fact, most…
Gooby
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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 learn a graph, i.e. an $N \times N$ adjacency…
basti123
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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 heuristic tells you to choose a variable that has the…
calveeen
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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?
unsmoother
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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 peg solitaire to the graph, which I do not, but…
Gooby
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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 phrase 'node features' used numerous times throughout…
Rocky the Owl
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