Results 71 to 80 of about 116,308 (181)
The authors study the problem of \(r\)-regular graph generation. The considered graphs can posses multiple edges between each pair \((x,y)\) of vertices, so they can be seen as labeled graphs with an integer accompanying each edge. Therefore, the subjective loopless graphs can be seen as flow networks. Two general approaches to generation are discussed.
Ding, Guoli, Chen, Peter
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Semi-supervised learning by constructing query-document heterogeneous information network
Various graph-based algorithms for semi-supervised learning have been proposed in recent literatures. How-ever, although classification on homogeneous networks has been studied for decades, classification on heterogeneous networks has not been explored ...
Yu-feng LIU, Ren-fa LI
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Manifold Regularization Graph Structure Auto-Encoder to Detect Loop Closure for Visual SLAM
Loop closure detection plays a vital role in the visual simultaneous localization and mapping (SLAM) systems. In order to overcome the shortcomings of the artificial design algorithm to extract insufficient features, this paper proposes a graph ...
Zhonghua Wang +3 more
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Approximately strongly regular graphs
We give variants of the Krein bound and the absolute bound for graphs with a spectrum similar to that of a strongly regular graph. In particular, we investigate what we call approximately strongly regular graphs. We apply our results to extremal problems. Among other things, we show the following: (1) Caps in $\mathrm{PG}(n, q)$ for which the number of
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On $k$-Walk-Regular Graphs [PDF]
Considering a connected graph $G$ with diameter $D$, we say that it is $k$-walk-regular, for a given integer $k$ $(0\leq k \leq D)$, if the number of walks of length $\ell$ between any pair of vertices only depends on the distance between them, provided that this distance does not exceed $k$. Thus, for $k=0$, this definition coincides with that of walk-
Dalfó Simó, Cristina +2 more
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Application of Non-Sparse Manifold Regularized Multiple Kernel Classifier
Non-sparse multiple kernel learning is efficient but not directly able to be applied in a semi-supervised scenario; therefore, we extend it to semi-supervised learning by using a manifold regularization.
Tao Yang
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Domain-Invariant Label Propagation With Adaptive Graph Regularization
As an effective machine learning paradigm, domain adaptation (DA) learning aims to enhance the learning performance of the target domain by utilizing other relevant but distinct domain(s) (referred to as the source domain(s)).
Yanning Zhang, Jianwen Tao, Liangda Yan
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Distance-Regular Graphs and Halved Graphs
Let G be a bipartite distance-regular graph with bipartition \(V(G)=X\cup Y\). Let \(V(G')=X\) and, for x and y in X, let x be adjacent to y in G' if and only if x is of distance two from y in G. Then G' is called a halved graph of G, and is distance-regular. This paper discusses whether G' is one of the known, large-diameter, distance-regular graphs.
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TorusE: Knowledge Graph Embedding on a Lie Group
Knowledge graphs are useful for many artificial intelligence (AI) tasks. However, knowledge graphs often have missing facts. To populate the graphs, knowledge graph embedding models have been developed.
Ebisu, Takuma, Ichise, Ryutaro
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Snake: a Stochastic Proximal Gradient Algorithm for Regularized Problems over Large Graphs
A regularized optimization problem over a large unstructured graph is studied, where the regularization term is tied to the graph geometry. Typical regularization examples include the total variation and the Laplacian regularizations over the graph. When
Bianchi, Pascal +2 more
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