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Density-Friendly Graph Decomposition [PDF]
ACM Transactions on Knowledge Discovery From Data, 2019 Decomposing a graph into a hierarchical structure via k -core analysis is a standard operation in any modern graph-mining toolkit. k -core decomposition is a simple and efficient method that allows to analyze a graph beyond its mere degree distribution.
Nikolaj Tatti
exaly +6 more sources
Accurate assembly of transcripts through phase-preserving graph decomposition. [PDF]
Nat Biotechnol, 2017 We introduce Scallop, an accurate reference-based transcript assembler that improves reconstruction of multi-exon and lowly expressed transcripts. Scallop preserves long-range phasing paths extracted from reads, while producing a parsimonious set of ...
Shao M, Kingsford C.
europepmc +2 more sources
Toeplitz graph decomposition [PDF]
Transactions on Combinatorics, 2012 Let $n,t_1,...,t_k$ be distinct positive integers. A Toeplitz graph $G=(V, E)$ denoted by $T_n$ is a graph, where $V ={1,...,n}$ and $E= {(i,j) : |i-j| in {t_1,...,t_k}}$.In this paper, we present some results on decomposition of Toeplitz graphs.
Samira Hossein Ghorban
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Nonintrusive Power Load Decomposition Based on Adaptive Graph Convolutional Neural Network [PDF]
SensorsTo fully exploit the correlation between the operating states of appliances, an adaptive graph convolutional neural network (AChebNet) for nonintrusive power load decomposition is proposed.
Pinzhang Zhao +3 more
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GSD: An R package for graph signal decomposition
SoftwareXGraph signals residing on the vertices of a graph have recently gained prominence in research of various fields, including neural networks, social networks, traffic patterns, and sensors.
Hyeonglae Cho, Hee-Seok Oh, Donghoh Kim
doaj +3 more sources
SparsePool: A Graph Pooling Framework via Sparse Representation for Graph Classification [PDF]
SensorsGraph neural networks (GNNs) have achieved great success in graph classification, with graph pooling methods being widely adopted for related tasks. Existing approaches typically rely on node ranking or clustering to coarsen graphs, but often fail to ...
Zehan Li +4 more
doaj +2 more sources
Graph decomposition techniques for solving combinatorial optimization problems with variational quantum algorithms [PDF]
Quantum Information Processing, 2023 The quantum approximate optimization algorithm (QAOA) has the potential to approximately solve complex combinatorial optimization problems in polynomial time. However, current noisy quantum devices cannot solve large problems due to hardware constraints.
Moises Ponce +6 more
semanticscholar +1 more source
I/O efficient ECC graph decomposition via graph reduction
VLDB Journal, 2016 Lu Qin, Wenjie Zhang, Lijun Chang
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Graph Decompositions and Factorizing Permutations [PDF]
Discrete Mathematics & Theoretical Computer Science, 2002 A factorizing permutation of a given graph is simply a permutation of the vertices in which all decomposition sets appear to be factors. Such a concept seems to play a central role in recent papers dealing with graph decomposition. It is applied here
Christian Capelle +2 more
doaj +2 more sources
High-Order Pooling for Graph Neural Networks with Tensor Decomposition [PDF]
Neural Information Processing Systems, 2022 Graph Neural Networks (GNNs) are attracting growing attention due to their effectiveness and flexibility in modeling a variety of graph-structured data. Exiting GNN architectures usually adopt simple pooling operations (eg.
Chenqing Hua +2 more
semanticscholar +1 more source