From data to concepts via wiring diagrams
- URL: http://arxiv.org/abs/2511.20138v1
- Date: Tue, 25 Nov 2025 09:59:56 GMT
- Title: From data to concepts via wiring diagrams
- Authors: Jason Lo, Mohammadnima Jafari,
- Abstract summary: We introduce the notion of a quasi-skeleton wiring diagram graph, and prove that quasi-skeleton wiring diagram graphs correspond to Hasse diagrams.<n>Using this result, we designed algorithms that extract wiring diagrams from sequential data.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A wiring diagram is a labeled directed graph that represents an abstract concept such as a temporal process. In this article, we introduce the notion of a quasi-skeleton wiring diagram graph, and prove that quasi-skeleton wiring diagram graphs correspond to Hasse diagrams. Using this result, we designed algorithms that extract wiring diagrams from sequential data. We used our algorithms in analyzing the behavior of an autonomous agent playing a computer game, and the algorithms correctly identified the winning strategies. We compared the performance of our main algorithm with two other algorithms based on standard clustering techniques (DBSCAN and agglomerative hierarchical), including when some of the data was perturbed. Overall, this article brings together techniques in category theory, graph theory, clustering, reinforcement learning, and data engineering.
Related papers
- Joint Data Inpainting and Graph Learning via Unrolled Neural Networks [1.8999296421549168]
We propose an algorithm to estimate both the underlying graph topology and the missing measurements.
The proposed technique can be used both as a graph learning and a graph signal reconstruction algorithm.
arXiv Detail & Related papers (2024-07-16T06:46:41Z) - Deep Contrastive Graph Learning with Clustering-Oriented Guidance [61.103996105756394]
Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering.
Models estimate an initial graph beforehand to apply GCN.
Deep Contrastive Graph Learning (DCGL) model is proposed for general data clustering.
arXiv Detail & Related papers (2024-02-25T07:03:37Z) - Quantifying analogy of concepts via ologs and wiring diagrams [0.0]
We build on the theory of logs (ologs) created by Spivak and Kent, and define a notion of wiring diagrams.<n>In this article, a wiring diagram is a finite directed labelled graph.<n>The labels correspond to types in an olog; they can also be interpreted as readings of sensors in an autonomous system.
arXiv Detail & Related papers (2024-02-01T21:15:55Z) - CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph
Similarity Learning [65.1042892570989]
We propose a contrastive graph matching network (CGMN) for self-supervised graph similarity learning.
We employ two strategies, namely cross-view interaction and cross-graph interaction, for effective node representation learning.
We transform node representations into graph-level representations via pooling operations for graph similarity computation.
arXiv Detail & Related papers (2022-05-30T13:20:26Z) - Effective and Efficient Graph Learning for Multi-view Clustering [173.8313827799077]
We propose an effective and efficient graph learning model for multi-view clustering.
Our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm.
Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size.
arXiv Detail & Related papers (2021-08-15T13:14:28Z) - Self-supervised Consensus Representation Learning for Attributed Graph [15.729417511103602]
We introduce self-supervised learning mechanism to graph representation learning.
We propose a novel Self-supervised Consensus Representation Learning framework.
Our proposed SCRL method treats graph from two perspectives: topology graph and feature graph.
arXiv Detail & Related papers (2021-08-10T07:53:09Z) - A Unifying Generative Model for Graph Learning Algorithms: Label
Propagation, Graph Convolutions, and Combinations [39.8498896531672]
Semi-supervised learning on graphs is a widely applicable problem in network science and machine learning.
We develop a Markov random field model for the data generation process of node attributes.
We show that label propagation, a linearized graph convolutional network, and their combination can all be derived as conditional expectations.
arXiv Detail & Related papers (2021-01-19T17:07:08Z) - Line Graph Neural Networks for Link Prediction [71.00689542259052]
We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications.
In this formalism, a link prediction problem is converted to a graph classification task.
We propose to seek a radically different and novel path by making use of the line graphs in graph theory.
In particular, each node in a line graph corresponds to a unique edge in the original graph. Therefore, link prediction problems in the original graph can be equivalently solved as a node classification problem in its corresponding line graph, instead of a graph classification task.
arXiv Detail & Related papers (2020-10-20T05:54:31Z) - Wasserstein-based Graph Alignment [56.84964475441094]
We cast a new formulation for the one-to-many graph alignment problem, which aims at matching a node in the smaller graph with one or more nodes in the larger graph.
We show that our method leads to significant improvements with respect to the state-of-the-art algorithms for each of these tasks.
arXiv Detail & Related papers (2020-03-12T22:31:59Z) - Adaptive Graph Auto-Encoder for General Data Clustering [90.8576971748142]
Graph-based clustering plays an important role in the clustering area.
Recent studies about graph convolution neural networks have achieved impressive success on graph type data.
We propose a graph auto-encoder for general data clustering, which constructs the graph adaptively according to the generative perspective of graphs.
arXiv Detail & Related papers (2020-02-20T10:11:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.