Eigensolution of symmetric frames using graph factorization

2004 ◽  
Vol 20 (12) ◽  
pp. 889-910 ◽  
Author(s):  
A. Kaveh ◽  
B. Salimbahrami
Keyword(s):  
Graph Factorization

scholarly journals Improved queue-size scaling for input-queued switches via graph factorization

2020 ◽  
Vol 52 (3) ◽  
pp. 798-824
Author(s):  
Jiaming Xu ◽  
Yuan Zhong
Keyword(s):  
Independent Interest ◽  
Queue Size ◽  
Scheduling Policies ◽  
New Class ◽  
Size Scaling ◽  
Size Scales ◽  
Graph Factorization

AbstractThis paper studies the scaling of the expected total queue size in an $n\times n$ input-queued switch, as a function of both the load $\rho$ and the system scale n. We provide a new class of scheduling policies under which the expected total queue size scales as $O\big( n(1-\rho)^{-4/3} \log \big(\!\max\big\{\frac{1}{1-\rho}, n\big\}\big)\big)$, over all n and $\rho<1$, when the arrival rates are uniform. This improves on the best previously known scalings in two regimes: $O\big(n^{1.5}(1-\rho)^{-1} \log \frac{1}{1-\rho}\big)$ when $\Omega\big(n^{-1.5}\big) \le 1-\rho \le O\big(n^{-1}\big)$ and $O\big(\frac{n\log n}{(1-\rho)^2}\big)$ when $1-\rho \geq \Omega(n^{-1})$. A key ingredient in our method is a tight characterization of the largest k-factor of a random bipartite multigraph, which may be of independent interest.

Cartesian graph factorization at logarithmic cost per edge

1992 ◽  
Vol 2 (4) ◽  
pp. 331-349 ◽  
Author(s):  
F. Aurenhammer ◽  
J. Hagauer ◽  
W. Imrich
Keyword(s):  
Graph Factorization

FACTORED ARC CONSISTENCY FOR RETE MATCH

1993 ◽  
Vol 02 (02) ◽  
pp. 277-306
Author(s):  
MARK W. PERLIN
Keyword(s):  
Working Memory ◽  
Constraint Satisfaction ◽  
Production Systems ◽  
Constraint Satisfaction Problems ◽  
Unified Framework ◽  
Call Graph ◽  
Quadratic Dependence ◽  
Arc Consistency ◽  
Common Framework ◽  
Graph Factorization

RETE match for production systems and Arc Consistency (AC) filtering are two efficient AI algorithms that are designed for particular constraint satisfaction problems (CSP). Interestingly, it is possible to integrate the two within a common framework, and provide RETE with the lookahead advantages of AC. Unfortunately, the resulting quadratic dependence of AC on working memory (WM) size precludes the application of AC to RETE, since RETE responds to incremental changes in WM. Recent results in AC graph factorization, however, reduce the quadratic dependence to just linear for certain classes of problems, including RETE. In this paper, we present RETE and AC within the unified framework of Call-Graph Caching (CGC) evaluation. We describe factored arc consistency (FAC) and its use in an integrated FAC/RETE algorithm that provides RETE match with AC lookahead. We discuss our implementations, promising initial empirical results, and explore FAC/RETE’s applicability conditions and extensions. We conclude that incorporating factored AC into RETE match is an interesting and potentially useful application of arc consistency methods to the RETE CSP.

Graph factorization and theorems of the Nordhaus—Gaddum class

1984 ◽  
Vol 15 (2) ◽  
pp. 109-120 ◽  
Author(s):  
R. J. Cook
Keyword(s):  
Graph Factorization

scholarly journals Robust Graph Factorization for Multivariate Electricity Consumption Series Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kaihong Zheng ◽  
Honghao Liang ◽  
Lukun Zeng ◽  
Xiaowei Chen ◽  
Sheng Li ◽  
...  
Keyword(s):  
Multivariate Time Series ◽  
Electricity Consumption ◽  
Electricity Production ◽  
Series Data ◽  
The Past ◽  
Time Period ◽  
Factorization Model ◽  
Spectral Embedding ◽  
Similarity Graph ◽  
Graph Factorization

Multivariate electricity consumption series clustering can reflect trends of power consumption changes in the past time period, which can provide reliable guidance for electricity production. However, there are some abnormal series in the past multivariate electricity consumption series data, while outliers will affect the discovery of electricity consumption trends in different time periods. To address this problem, we propose a robust graph factorization model for multivariate electricity consumption clustering (RGF-MEC), which performs graph factorization and outlier discovery simultaneously. RGF-MEC first obtains a similarity graph by calculating distance among multivariate electricity consumption series data and then performs robust matrix factorization on the similarity graph. Meanwhile, the similarity graph is decomposed into a class-related embedding and a spectral embedding, where the class-related embedding directly reveals the final clustering results. Experimental results on realistic multivariate time-series datasets and multivariate electricity consumption series datasets demonstrate effectiveness of the proposed RGF-MEC model.

scholarly journals Distributed large-scale natural graph factorization

2013 ◽  
Author(s):  
Amr Ahmed ◽  
Nino Shervashidze ◽  
Shravan Narayanamurthy ◽  
Vanja Josifovski ◽  
Alexander J. Smola
Keyword(s):  
Large Scale ◽  
Graph Factorization

scholarly journals A learning based framework for diverse biomolecule relationship prediction in molecular association network

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Zhen-Hao Guo ◽  
Zhu-Hong You ◽  
De-Shuang Huang ◽  
Hai-Cheng Yi ◽  
Zhan-Heng Chen ◽  
...  
Keyword(s):  
Random Forest ◽  
Prediction Model ◽  
Computational Models ◽  
Cross Validation ◽  
Random Forest Classifier ◽  
Regulatory Mechanisms ◽  
Association Network ◽  
Great Hope ◽  
Fold Cross Validation ◽  
Graph Factorization

AbstractAbundant life activities are maintained by various biomolecule relationships in human cells. However, many previous computational models only focus on isolated objects, without considering that cell is a complete entity with ample functions. Inspired by holism, we constructed a Molecular Associations Network (MAN) including 9 kinds of relationships among 5 types of biomolecules, and a prediction model called MAN-GF. More specifically, biomolecules can be represented as vectors by the algorithm called biomarker2vec which combines 2 kinds of information involved the attribute learned by k-mer, etc and the behavior learned by Graph Factorization (GF). Then, Random Forest classifier is applied for training, validation and test. MAN-GF obtained a substantial performance with AUC of 0.9647 and AUPR of 0.9521 under 5-fold Cross-validation. The results imply that MAN-GF with an overall perspective can act as ancillary for practice. Besides, it holds great hope to provide a new insight to elucidate the regulatory mechanisms.

Graph factorization: A new mode of application of a vertex-alternation scheme

1993 ◽  
Vol 46 (4) ◽  
pp. 519-533 ◽  
Author(s):  
Asok K. Mukherjee ◽  
Debes K. Das
Keyword(s):  
Mode Of Application ◽  
Graph Factorization
Sign in / Sign up

Export Citation Format

Share Document