Improved graph clustering

Chen, Yudong; Sanghavi, Sujay; Xu, Hong‐Xi

doi:10.21236/ada596381

Cited by 3 publications

(5 citation statements)

References 27 publications

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“…The convex optimization we consider in this section is SDP. Different formulations of SDP have been shown to yield good community detection performances in Amini and Levina (2014), Chen et al (2012), Cai and Li (2015), Guédon and Vershynin (2016), Montanari and Sen (2015), among others. One illuminating interpretation of SDP is to think of it as a convex relaxation of the maximum likelihood method.…”

Section: Practical Algorithmsmentioning

confidence: 99%

“…For example, starting from a specialized SBM, one can derive SDP by approximating the corresponding likelihood function. See Amini and Levina (2014), Chen et al (2012) and Cai and Li (2015) for the detailed arguments. However, under the NSBM, because of the nodal covariate term, it is not straightforward to generalize the convex relaxation arguments.…”

Section: Practical Algorithmsmentioning

confidence: 99%

“…Spectral clustering has also been studied for the optimal combination of multi‐layer SBMs (Huang et al, 2020). In addition, carefully constructed convex programming has been shown to enjoy provable guarantees for community detection (Amini & Levina, 2014; Cai & Li, 2015; Chen et al, 2012, 2015, Guédon & Vershynin 2016). See also the interesting theoretical works of community detection under the minimax framework (Gao et al, 2015; Zhang & Zhou, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Community detection with nodal information: Likelihood and its variational approximation

Weng

Feng

2022

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Community detection is one of the fundamental problems in the study of network data. Most existing community detection approaches only consider edge information as inputs, and the output could be suboptimal when nodal information is available. In such cases, it is desirable to leverage nodal information for the improvement of community detection accuracy. Towards this goal, we propose a flexible network model incorporating nodal information and develop likelihood‐based inference methods. For the proposed methods, we establish favorable asymptotic properties as well as efficient algorithms for computation. Numerical experiments show the effectiveness of our methods in utilizing nodal information across a variety of simulated and real network data sets.

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Section: Practical Algorithmsmentioning

confidence: 99%

Section: Practical Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Community detection with nodal information: Likelihood and its variational approximation

Weng

Feng

2022

Stat

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“…Many techniques are developed for graph classification such as spectral clustering [11], modularity based clustering [12] and information theory based methods [13], [14] just to mention few. As the graph classification based on the entropy rate maximization has the minimum bias, is employed here.…”

Section: A Point Cloud Classification and Nurbs Fittingmentioning

confidence: 99%

NURBS-based representation of urban environments for mobile robots

Ravari

Taghirad

2016

2016 4th International Conference on Robotics and Mechatronics (ICROM)

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Representation of the surrounding environment is a vital task for a mobile robot. Many applications for mobile robots in urban environments may be considered such as selfdriving cars, delivery drones or assistive robots. In contrast to the conventional methods, in this paper a Non Uniform Rational B-Spline (NURBS) based technique is represented for 3D mapping of the surrounding environment. While in the state of the art techniques, the robot's environment is expressed in a discrete space, the proposed method is mainly developed for representation of environment in a continuous space. Exploiting the information theory, the generated representation has much lower complexity and more compression capability in relation to some state of the art techniques. In addition to representation in a lower dimensional space, the NURBS based representation is invariant against 3D geometric transformations. Furthermore, the NURBS based representation can be employed for obstacle avoidance and navigation. The applicability of the proposed algorithm is investigated in some urban environments through some publicly available data sets. It has been shown by some experiments that the proposed method has better visual representation and much better data compression compared to some state-of-the-art methods.

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“…A number of community detection algorithms have been studied in the literature. This includes spectral clustering and its variants that leverage the eigen structure of the network (Rohe et al, 2011;Lei and Rinaldo, 2015;Sarkar and Bickel, 2015;Sengupta and Chen, 2015;Cao and Chen, 2011), likelihood based methods that maximize the model likelihood (Amini et al, 2013;Zhao et al, 2012;Nowicki and Snijders, 2001), as well as optimization based methods (Chen et al, 2012;Le et al, 2014). Most of these community Community Detection for Large Networks detection algorithms have a high computation complexity.…”

Section: Introductionmentioning

confidence: 99%

Subsampling Based Community Detection for Large Networks

Chakrabarty,

Sengupta,

Chen

2025

STAT SINICA

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Large networks are becoming pervasive in scientific applications. Statistical analysis of such large networks is prohibitive due to exorbitant runtime and high memory requirements. We propose a subsampling based divide-andconquer algorithm, SONNET, for community detection in large networks. The algorithm splits the original network into multiple subnetworks with a common overlap, and carries out detection algorithm for each subnetwork. The results from individual subnetworks are aggregated using a label matching method to get the final community labels. This method saves both memory and computation costs significantly as one needs to store and process only the smaller subnetworks. This method is also parallelizable which makes it even faster.

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Improved graph clustering

Cited by 3 publications

References 27 publications

Community detection with nodal information: Likelihood and its variational approximation

Community detection with nodal information: Likelihood and its variational approximation

NURBS-based representation of urban environments for mobile robots

Subsampling Based Community Detection for Large Networks

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