Results 11 to 20 of about 27,298 (312)
Between Subgraph Isomorphism and Maximum Common Subgraph [PDF]
Proceedings of the AAAI Conference on Artificial Intelligence, 2017 When a small pattern graph does not occur inside a larger target graph, we can ask how to find "as much of the pattern as possible" inside the target graph. In general, this is known as the maximum common subgraph problem, which is much more computationally challenging in practice than subgraph isomorphism.
Ruth Hoffmann +2 more
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SubRank: Subgraph Embeddings via a Subgraph Proximity Measure [PDF]
, 2020 Representation learning for graph data has gained a lot of attention in recent years. However, state-of-the-art research is focused mostly on node embeddings, with little effort dedicated to the closely related task of computing subgraph embeddings.
Oana Balalau, Sagar Goyal
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In-Memory Caching for Enhancing Subgraph Accessibility
Applied Sciences, 2020 Graphs have been utilized in various fields because of the development of social media and mobile devices. Various studies have also been conducted on caching techniques to reduce input and output costs when processing a large amount of graph data.
Kyoungsoo Bok +4 more
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The t-Graphs over Finitely Generated Groups and the Minkowski Metric
Mathematics, 2022 In this paper, we introduce t-graphs defined on finitely generated groups. We study some general aspects of the t-graphs on two-generator groups, emphasizing establishing necessary conditions for their connectedness.
Gabriela Diaz-Porto +2 more
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Subgraph densities in a surface [PDF]
Combinatorics, Probability and Computing, 2022 AbstractGiven a fixed graphHthat embeds in a surface$\Sigma$, what is the maximum number of copies ofHin ann-vertex graphGthat embeds in$\Sigma$? We show that the answer is$\Theta(n^{f(H)})$, wheref(H) is a graph invariant called the ‘flap-number’ ofH, which is independent of$\Sigma$.
Huynh, T., Joret, G., Wood, D. R.
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Significant subgraph mining for neural network inference with multiple comparisons correction [PDF]
, 2022 We describe how the recently introduced method of significant subgraph mining can be employed as a useful tool in neural network comparison. It is applicable whenever the goal is to compare two sets of unweighted graphs and to determine differences in ...
Gutknecht, Aaron J. +3 more
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Stochastic Subgraph Neighborhood Pooling for Subgraph Classification
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023 Subgraph classification is an emerging field in graph representation learning where the task is to classify a group of nodes (i.e., a subgraph) within a graph. Subgraph classification has applications such as predicting the cellular function of a group of proteins or identifying rare diseases given a collection of phenotypes.
Shweta Ann Jacob +2 more
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Subgraph nomination: query by example subgraph retrieval in networks
Statistics and Computing, 2023 37 pages, 11 ...
Al-Fahad M. Al-Qadhi +3 more
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Knowledge Graph Link Prediction Based on Subgraph Reasoning [PDF]
Jisuanji kexue yu tansuo, 2022 Relationship prediction in knowledge graph aims to identify and infer new relationships from existing data, and provides knowledge services for many downstream tasks.
YU Huilin, CHEN Wei, WANG Qi, GAO Jianwei, WAN Huaiyu
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The Anonymous Subgraph Problem [PDF]
Computers & Operations Research, 2013 In this work we address the Anonymous Subgraph Problem (ASP). The problem asks to decide whether a directed graph contains anonymous subgraphs of a given family. This problem has a number of practical applications and here we describe three of them (Secret Santa Problem, anonymous routing, robust paths) that can be formulated as ASPs.
Andrea Bettinelli +3 more
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