Node Classification for Signed Social Networks Using Diffuse Interface Methods

Mercado, Pedro; Bosch, Jessica; Stoll, Martin

doi:10.1007/978-3-030-46150-8_31

Cited by 11 publications

(14 citation statements)

References 47 publications

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“…The variable τ is a hyperparameter but can be interpreted as a pseudo time-step. In more detail following the notation of [20], this leads to…”

Section: Semi-supervised Learning With Phase Field Methods: Allen-cahn Modelmentioning

confidence: 99%

“…Details of the definition of the potential and the fidelity term incorporating the training data are found in [50]. Further extensions of this approach have been suggested in [20,22,[51][52][53][54][55].…”

Section: Semi-supervised Learning With Phase Field Methods: Allen-cahn Modelmentioning

confidence: 99%

“…Graph-based approaches have become a standard tool in many learning tasks (cf. [19][20][21][22][23][24] and the references mentioned therein). The matrix representation of the graph via its Laplacian [25] leads to studying the network using matrix properties.…”

Section: Introductionmentioning

confidence: 99%

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An Empirical Study of Graph-Based Approaches for Semi-supervised Time Series Classification

Alfke¹,

Gondos²,

Peroche³

et al. 2022

Front. Appl. Math. Stat.

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Time series data play an important role in many applications and their analysis reveals crucial information for understanding the underlying processes. Among the many time series learning tasks of great importance, we here focus on semi-supervised learning based on a graph representation of the data. Two main aspects are studied in this paper. Namely, suitable distance measures to evaluate the similarities between different time series, and the choice of learning method to make predictions based on a given number of pre-labeled data points. However, the relationship between the two aspects has never been studied systematically in the context of graph-based learning. We describe four different distance measures, including (Soft) DTW and MPDist, a distance measure based on the Matrix Profile, as well as four successful semi-supervised learning methods, including the recently introduced graph Allen–Cahn method and Graph Convolutional Neural Network method. We provide results for the novel combination of these distance measures with both the Allen-Cahn method and the GCN algorithm for binary semi-supervised learning tasks for various time-series data sets. In our findings we compare the chosen graph-based methods using all distance measures and observe that the results vary strongly with respect to the accuracy. We then observe that no clear best combination to employ in all cases is found. Our study provides a reproducible framework for future work in the direction of semi-supervised learning for time series with a focus on graph representations.

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“…The variable τ is a hyperparameter but can be interpreted as a pseudo time-step. In more detail following the notation of [20], this leads to…”

Section: Semi-supervised Learning With Phase Field Methods: Allen-cahn Modelmentioning

confidence: 99%

Section: Semi-supervised Learning With Phase Field Methods: Allen-cahn Modelmentioning

confidence: 99%

See 1 more Smart Citation

An Empirical Study of Graph-Based Approaches for Semi-supervised Time Series Classification

Alfke¹,

Gondos²,

Peroche³

et al. 2022

Front. Appl. Math. Stat.

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“…These networks are used to model friend and foe type relationships and we refer to [126,178,179,247,267] and the references mentioned therein for an overview of some of the crucial applications. Again techniques such as spectral clustering [199,201,247], semi-supervised learning [197], convolutional networks [72] are available to extract further information from the data. The difficulty for signed networks is that the classical graph Laplacian is not feasible as for example the sum of the weights could be zero resulting in a noninvertible degree matrix.…”

Section: F I G U R E 3 a Simple Multilayer Graphmentioning

confidence: 99%

A literature survey of matrix methods for data science

Stoll

2020

GAMM-Mitteilungen

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Efficient numerical linear algebra is a core ingredient in many applications across almost all scientific and industrial disciplines. With this survey we want to illustrate that numerical linear algebra has played and is playing a crucial role in enabling and improving data science computations with many new developments being fueled by the availability of data and computing resources. We highlight the role of various different factorizations and the power of changing the representation of the data as well as discussing topics such as randomized algorithms, functions of matrices, and high‐dimensional problems. We briefly touch upon the role of techniques from numerical linear algebra used within deep learning.

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“…Graph-based approaches have become a standard tool in many learning tasks (cf. [45,41,34,10,38,14] and the references mentioned therein). The matrix representation of the graph via its Laplacian [23] leads to studying the network using matrix properties.…”

Section: Introductionmentioning

confidence: 99%