Results 211 to 220 of about 2,805,373 (250)
, 2021 Many technological, socio-economic, environmental, biomedical phenomena exhibit an underlying graph structure. Valued graph allows one to incorporate the connections or links among the population units in addition. The links may provide effectively access to the part of population that is the primary target, which is the case for many unconventional ...
Zhang, Li-Chun
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2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008
We present an analytic and geometric view of the sample mean of graphs. The theoretical framework yields efficient subgradient methods for approximating a structural mean and a simple plug-in mechanism to extend existing central clustering algorithms to graphs. Experiments in clustering protein structures show the benefits of the proposed theory.
Brijnesh J. Jain, Klaus Obermayer
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We present an analytic and geometric view of the sample mean of graphs. The theoretical framework yields efficient subgradient methods for approximating a structural mean and a simple plug-in mechanism to extend existing central clustering algorithms to graphs. Experiments in clustering protein structures show the benefits of the proposed theory.
Brijnesh J. Jain, Klaus Obermayer
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Deterministic graph exploration for efficient graph sampling
Social Network Analysis and Mining, 2017Graph sampling is a widely used procedure in social network analysis, has attracted great interest in the scientific community and is considered as a very powerful and useful tool in several domains of network analysis. Apart from initial research in this area, which has proposed simple processes such as the classic Random Walk algorithm, Random Node ...
Nikos Salamanos +2 more
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Unbiased Sampling of Bipartite Graph
2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2011Increasing size of online social networks (OSNs) has given rise to sampling method studies that provide a relatively small but representative sample of large-scale OSNs so that the measurement and analysis burden can be affordable. So far, a number of sampling methods already exist that crawl social graphs.
Jing Wang, Yuchun Guo
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Sampling theory for graph signals
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015We propose a sampling theory for finite-dimensional vectors with a generalized bandwidth restriction, which follows the same paradigm of the classical sampling theory. We use this general result to derive a sampling theorem for bandlimited graph signals in the framework of discrete signal processing on graphs.
Siheng Chen +2 more
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Guided sampling for large graphs
Data Mining and Knowledge Discovery, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Muhammad Irfan Yousuf, Suhyun Kim 0001
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ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
This paper considers the graph signal sampling problem when some of the selected samples are lost or unavailable due to sensor failures or adversarial erasures. We formulate a robust graph signal sampling problem where only a subset of selected samples are received, and the goal is to maximize the worst-case performance.
Basak Güler +3 more
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This paper considers the graph signal sampling problem when some of the selected samples are lost or unavailable due to sensor failures or adversarial erasures. We formulate a robust graph signal sampling problem where only a subset of selected samples are received, and the goal is to maximize the worst-case performance.
Basak Güler +3 more
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Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006
Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.), but several of them become impractical for large graphs. Thus graph sampling is essential.The natural questions to ask are (a) which sampling method to use, (b) how small ...
Jure Leskovec, Christos Faloutsos
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Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.), but several of them become impractical for large graphs. Thus graph sampling is essential.The natural questions to ask are (a) which sampling method to use, (b) how small ...
Jure Leskovec, Christos Faloutsos
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List sampling for large graphs
Intelligent Data Analysis, 2018Real world graphs are massive in size and often prohibitively expensive to analyze. Of the possible solutions, sampling is extracting a representative subgraph from a large graph that faithfully represents the actual graph. The prior research has developed several sampling methods but the samples produced by these methods fail to match important ...
Muhammad Irfan Yousuf, Suhyun Kim 0001
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On Sampling of Bandlimited Graph Signals
2018The signal processing on graphs has been widely used in various fields, including machine learning, classification and network signal processing, in which the sampling of bandlimited graph signals plays an important role. In this paper, we discuss the sampling of bandlimited graph signals based on the theory of function spaces, which is consistent with
Mo Han +3 more
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