Results 21 to 30 of about 5,903,622 (201)
Random Walk Graph Auto-Encoders With Ensemble Networks in Graph Embedding
IEEE Access, 2023 Recently graph auto-encoders have received increasingly widespread attention as one of the important models in the field of deep learning. Existing graph auto-encoder models only use graph convolutional neural networks (GCNs) as encoders to learn the ...
Chengxin Xie +3 more
doaj +1 more source
Random Features Strengthen Graph Neural Networks [PDF]
SDM, 2020 Graph neural networks (GNNs) are powerful machine learning models for various graph learning tasks. Recently, the limitations of the expressive power of various GNN models have been revealed.
R. Sato, M. Yamada, H. Kashima
semanticscholar +1 more source
Combinatorica, 1988 We introduce a large equivalence class of graph properties, all of which are shared by so-called random graphs. Unlike random graphs, however, it is often relatively easy to verify that a particular family of graphs possesses some property in this class.
Chung, F. R. K. +2 more
openaire +3 more sources
Semi‐random graph process [PDF]
Random Struct. Algorithms, 2018 We introduce and study a novel semi‐random multigraph process, described as follows. The process starts with an empty graph on n vertices. In every round of the process, one vertex v of the graph is picked uniformly at random and independently of all ...
Omri Ben-Eliezer +5 more
semanticscholar +1 more source
Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning [PDF]
The Web Conference, 2022 Recently, graph collaborative filtering methods have been proposed as an effective recommendation approach, which can capture users’ preference over items by modeling the user-item interaction graphs.
Zihan Lin +3 more
semanticscholar +1 more source
Random Graph Isomorphism [PDF]
SIAM Journal on Computing, 1980 Summary: A straightforward linear time canonical labeling algorithm is shown to apply to almost all graphs (i.e. all but \(O(2^{\binom n2})\) of the \(2^{\binom n2})\) graphs on \(n\) vertices). Hence, for almost all graphs \(X\), and graph \(Y\) can be easily tested for isomorphism to \(X\) by an extremly naive linear time algorithm.
Babai, Laszlo +2 more
openaire +2 more sources
Random matrices and random graphs
ESAIM: Proceedings and Surveys, 2023 We collect recent results on random matrices and random graphs. The topics covered are: fluctuations of the empirical measure of random matrices, finite-size effects of algorithms involving random matrices, characteristic polynomial of sparse matrices and Voronoi tesselations of split trees.
Capitaine, Mireille +4 more
openaire +3 more sources
Configuring Random Graph Models with Fixed Degree Sequences [PDF]
SIAM Review, 2016 Random graph null models have found widespread application in diverse research communities analyzing network datasets, including social, information, and economic networks, as well as food webs, protein-protein interactions, and neuronal networks.
B. Fosdick +3 more
semanticscholar +1 more source
The Electronic Journal of Combinatorics, 2009 We study a random even subgraph of a finite graph $G$ with a general edge-weight $p\in(0,1)$. We demonstrate how it may be obtained from a certain random-cluster measure on $G$, and we propose a sampling algorithm based on coupling from the past. A random even subgraph of a planar lattice undergoes a phase transition at the parameter-value ${1\over2 ...
Grimmett, Geoffrey, Janson, Svante
openaire +3 more sources