Asymmetric graph based zero shot learning

2019 ◽  
Vol 79 (45-46) ◽  
pp. 33689-33710 ◽  
Author(s):  
Yinduo Wang ◽  
Haofeng Zhang ◽  
Zheng Zhang ◽  
Yang Long
Keyword(s):  
Asymmetric Graph

Asymmetric Graph-Guided Multitask Survival Analysis With Self-Paced Learning

2020 ◽  
pp. 1-13 ◽  
Author(s):  
Cheng Liu ◽  
Wenming Cao ◽  
Si Wu ◽  
Wenjun Shen ◽  
Dazhi Jiang ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Asymmetric Graph

scholarly journals An asymmetric graph recommendation algorithm based on user scores and interests

2017 ◽  
Vol 231 ◽  
pp. 012045
Author(s):  
Yi-Wen Zhang ◽  
Ran Wang ◽  
Ting-Wei Wang ◽  
Guang-Da He ◽  
Jun Hu
Keyword(s):  
Recommendation Algorithm ◽  
Asymmetric Graph

scholarly journals Asymmetric graph coloring games

2005 ◽  
Vol 48 (3) ◽  
pp. 169-185 ◽  
Author(s):  
H. A. Kierstead
Keyword(s):  
Graph Coloring ◽  
Asymmetric Graph

Robustness of network coherence in asymmetric unicyclic graphs

2021 ◽  
Author(s):  
Jing Chen ◽  
Ting Jing ◽  
Weigang Sun
Keyword(s):  
Network Size ◽  
Unicyclic Graph ◽  
Symmetric Graph ◽  
Laplacian Spectrum ◽  
Unicyclic Graphs ◽  
Closed Form Solutions ◽  
Asymmetric Structures ◽  
Network Parameters ◽  
Asymmetric Graph ◽  
Better Than

In this paper, we propose a family of unicyclic graphs to study robustness of network coherence quantified by the Laplacian spectrum, which measures the extent of consensus under the noise. We adjust the network parameters to change the structural asymmetries with an aim of studying their effects on the coherence. Using the graph’s structures and matrix theories, we obtain closed-form solutions of the network coherence regarding network parameters and network size. We further show that the coherence of the asymmetric graph is higher than the corresponding symmetric graph and also compare the consensus behaviors for the graphs with different asymmetric structures. It displays that the coherence of the unicyclic graph with one hub is better than the graph with two hubs. Finally, we investigate the effect of degree of hub nodes on the coherence and find that bigger difference of degrees leads to better coherence.

scholarly journals Directed defective asymmetric graph coloring games

2010 ◽  
Vol 158 (4) ◽  
pp. 251-260 ◽  
Author(s):  
Stephan Dominique Andres
Keyword(s):  
Graph Coloring ◽  
Asymmetric Graph

scholarly journals Towards Optimal Discrete Online Hashing with Balanced Similarity

2019 ◽  
Vol 33 ◽  
pp. 8722-8729 ◽  
Author(s):  
Mingbao Lin ◽  
Rongrong Ji ◽  
Hong Liu ◽  
Xiaoshuai Sun ◽  
Yongjian Wu ◽  
...  
Keyword(s):  
Large Scale ◽  
Hash Functions ◽  
Streaming Data ◽  
Unified Framework ◽  
Graph Regularization ◽  
Continuous Space ◽  
Imbalance Problem ◽  
Compact Binary ◽  
Asymmetric Graph ◽  
Online Streaming

When facing large-scale image datasets, online hashing serves as a promising solution for online retrieval and prediction tasks. It encodes the online streaming data into compact binary codes, and simultaneously updates the hash functions to renew codes of the existing dataset. To this end, the existing methods update hash functions solely based on the new data batch, without investigating the correlation between such new data and the existing dataset. In addition, existing works update the hash functions using a relaxation process in its corresponding approximated continuous space. And it remains as an open problem to directly apply discrete optimizations in online hashing. In this paper, we propose a novel supervised online hashing method, termed Balanced Similarity for Online Discrete Hashing (BSODH), to solve the above problems in a unified framework. BSODH employs a well-designed hashing algorithm to preserve the similarity between the streaming data and the existing dataset via an asymmetric graph regularization. We further identify the “data-imbalance” problem brought by the constructed asymmetric graph, which restricts the application of discrete optimization in our problem. Therefore, a novel balanced similarity is further proposed, which uses two equilibrium factors to balance the similar and dissimilar weights and eventually enables the usage of discrete optimizations. Extensive experiments conducted on three widely-used benchmarks demonstrate the advantages of the proposed method over the stateof-the-art methods.

scholarly journals Leontief Meets Markov: Sectoral Vulnerabilities Through Circular Connectivity

2021 ◽  
Author(s):  
Ariel L. Wirkierman ◽  
Monica Bianchi ◽  
Anna Torriero
Keyword(s):  
Graph Laplacian ◽  
Vulnerability Index ◽  
Formal Structure ◽  
Economic Interpretation ◽  
Money Flows ◽  
Local Sector ◽  
Regular Markov Chain ◽  
Asymmetric Graph ◽  
National Economies ◽  
Constituent Elements

AbstractEconomists have been aware of the mapping between an Input-Output (I-O, hereinafter) table and the adjacency matrix of a weighted digraph for several decades (Solow, Econometrica 20(1):29–46, 1952). An I-O table may be interpreted as a network in which edges measure money flows to purchase inputs that go into production, whilst vertices represent economic industries. However, only recently the language and concepts of complex networks (Newman 2010) have been more intensively applied to the study of interindustry relations (McNerney et al. Physica A Stat Mech Appl, 392(24):6427–6441, 2013). The aim of this paper is to study sectoral vulnerabilities in I-O networks, by connecting the formal structure of a closed I-O model (Leontief, Rev Econ Stat, 19(3):109–132, 1937) to the constituent elements of an ergodic, regular Markov chain (Kemeny and Snell 1976) and its chance process specification as a random walk on a graph. We provide an economic interpretation to a local, sector-specific vulnerability index based on mean first passage times, computed by means of the Moore-Penrose inverse of the asymmetric graph Laplacian (Boley et al. Linear Algebra Appl, 435(2):224–242, 2011). Traversing from the most central to the most peripheral sector of the economy in 60 countries between 2005 and 2015, we uncover cross-country salient roles for certain industries, pervasive features of structural change and (dis)similarities between national economies, in terms of their sectoral vulnerabilities.

The world is not an asymmetric graph

Analysis ◽  
2010 ◽  
Vol 71 (1) ◽  
pp. 3-10 ◽  
Author(s):  
D. S. Oderberg
Keyword(s):  
The World ◽  
Asymmetric Graph
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