Clustering based on random graph model embedding vertex features

Zanghi, Hugo; Volant, Stevenn; Ambroise, Christophe

doi:10.1016/j.patrec.2010.01.026

Cited by 74 publications

(39 citation statements)

References 18 publications

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“…Thus, we confined ourselves to a well-known artificial dataset, called ZAV dataset. ZAV benchmark dataset proposed by Zanghi et al (2010) is a type of computer-generated dataset. This dataset includes nodes with their community labels, interactions, and integer attributes.…”

Section: Datasets and Resultsmentioning

confidence: 99%

Extracting new ideas from the behavior of social network users

Karimi-Majd¹,

Fathian²

2017

10.5267/j.dsl

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Online social networks (OSNs) provide services targeting multifarious types of users in order to attract and retain them. For this purpose, developing new services according to user preferences has recently been under focused by various researchers. Most of present studies focus only on extracting the behavioral patterns of users, and neglect users' interactions, which is the main part of the social activities in OSNs. To cope with this issue, this paper proposes a new methodology to bring both dimensions of data, the extracted behavioral patterns of users and their social interactions, in order to reach a better analysis. Moreover, the idea provides a basis for considering other dimensions efficiently. In order to evaluate the performance of the methodology, this paper performs a case study, and conducts a set of experiments on the computer-generated datasets. The results indicates the great performance of the methodology.Growing Science Ltd. All rights reserved. 7

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Section: Datasets and Resultsmentioning

confidence: 99%

Extracting new ideas from the behavior of social network users

Karimi-Majd¹,

Fathian²

2017

10.5267/j.dsl

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“…Current dynamic network models (Robins and Pattison, 2001;Xu and Hero, 2013;Xing et al, 2010;Sarkar and Moore, 2005) raise open questions about coherency, flexibility, theoretical properties and computational tractability. Contributions considering edge-specific covariate effects are available in static settings (see e.g., Snijders et al, 2006;Zanghi et al, 2010;Hoff et al, 2002) and developments in the longitudinal framework have been recently explored (Snijders, 2005;Cranmer and Desmarais, 2011;Ward et al, 2013). Such approaches inherit the drawbacks of the dynamic network models they seek to generalize, with only Ward et al (2013) allowing the edge covariate parameters to vary over time via a sequential estimating approach, which does not borrow dynamic information efficiently, and fails to properly propagate uncertainty.…”

Section: Relevant Contributionsmentioning

confidence: 99%

Bayesian dynamic financial networks with time-varying predictors

Durante¹,

Dunson²

2014

Statistics & Probability Letters

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We propose a Bayesian nonparametric model including time-varying predictors in dynamic network inference. The model is applied to infer the dependence structure among financial markets during the global financial crisis, estimating effects of verbal and material cooperation efforts. We interestingly learn contagion effects, with increasing influence of verbal relations during the financial crisis and opposite results during the United States housing bubble.

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“…Zanghi et al [2010] proposed a probabilistic model based on a similar generative process to ours. It can be treated as an instance of GBAGC for unweighted edges and continuous attributes, except that it does not take a Bayesian treatment but treats model parameters as fixed values.…”

Section: Related Workmentioning

confidence: 99%

Gbagc

Wang

et al. 2014

ACM Trans. Knowl. Discov. Data

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Graph clustering, also known as community detection, is a long-standing problem in data mining. In recent years, with the proliferation of rich attribute information available for objects in real-world graphs, how to leverage not only structural but also attribute information for clustering attributed graphs becomes a new challenge. Most existing works took a distance-based approach. They proposed various distance measures to fuse structural and attribute information and then applied standard techniques for graph clustering based on these distance measures. In this article, we take an alternative view and propose a novel Bayesian framework for attributed graph clustering. Our framework provides a general and principled solution to modeling both the structural and the attribute aspects of a graph. It avoids the artificial design of a distance measure in existing methods and, furthermore, can seamlessly handle graphs with different types of edges and vertex attributes. We develop an efficient variational method for graph clustering under this framework and derive two concrete algorithms for clustering unweighted and weighted attributed graphs. Experimental results on large real-world datasets show that our algorithms significantly outperform the state-of-the-art distance-based method, in terms of both effectiveness and efficiency.

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Clustering based on random graph model embedding vertex features

Cited by 74 publications

References 18 publications

Extracting new ideas from the behavior of social network users

Extracting new ideas from the behavior of social network users

Bayesian dynamic financial networks with time-varying predictors

Gbagc

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