Learning error-correcting graph matching with a multiclass neural network

Martineau, Maxime; Raveaux, Romain; Conte, Donatello; Venturini, Gilles

doi:10.1016/j.patrec.2018.03.031

Cited by 13 publications

(4 citation statements)

References 21 publications

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“…On the other hand, deletion costs are implicitly set to a specific value (that is to say 0) in the GM problem. Many learning methods aim at learning edit costs (Serratosa, 2020;Martineau et al, 2020) or matching similarities (Zanfir and Sminchisescu, 2018;Caetano et al, 2007). Learned matching similarities may include implicitly deletion and insertion costs.…”

Section: Discussionmentioning

confidence: 99%

On the unification of the graph edit distance and graph matching problems

Raveaux

2021

Pattern Recognition Letters

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Error-tolerant graph matching gathers an important family of problems. These problems aim at finding correspondences between two graphs while integrating an error model. In the Graph Edit Distance (GED) problem, the insertion/deletion of edges/nodes from one graph to another is explicitly expressed by the error model. At the opposite, the problem commonly referred to as "graph matching" does not explicitly express such operations. For decades, these two problems have split the research community in two separated parts. It resulted in the design of different solvers for the two problems. In this paper, we propose a unification of both problems thanks to a single model. We give the proof that the two problems are equivalent under a reformulation of the error models. This unification makes possible the use on both problems of existing solving methods from the two communities.

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Section: Discussionmentioning

confidence: 99%

On the unification of the graph edit distance and graph matching problems

Raveaux

2021

Pattern Recognition Letters

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“…Ref. [16] This study proposes an efficient method for solving the graph matching issue in a classification setting. Ref.…”

Section: Rq2: How To Perform Multiclass Classification Using Unlabele...mentioning

confidence: 99%

Towards the Detection of Fake News on Social Networks Contributing to the Improvement of Trust and Transparency in Recommendation Systems: Trends and Challenges

2022

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In the age of the digital revolution and the widespread usage of social networks, the modalities of information consumption and production were disrupted by the shift to instantaneous transmission. Sometimes the scoop and exclusivity are just for a few minutes. Information spreads like wildfire throughout the world, with little regard for context or critical thought, resulting in the proliferation of fake news. As a result, it is preferable to have a system that allows consumers to obtain balanced news information. Some researchers attempted to detect false and authentic news using tagged data and had some success. Online social groups propagate digital false news or fake news material in the form of shares, reshares, and repostings. This work aims to detect fake news forms dispatched on social networks to enhance the quality of trust and transparency in the social network recommendation system. It provides an overview of traditional techniques used to detect fake news and modern approaches used for multiclassification using unlabeled data. Many researchers are focusing on detecting fake news, but fewer works highlight this detection’s role in improving the quality of trust in social network recommendation systems. In this research paper, we take an improved approach to assisting users in deciding which information to read by alerting them about the degree of inaccuracy of the news items they are seeing and recommending the many types of fake news that the material represents.

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“…The comparison is restricted to the five datasets considered in this work, with a dash (-) indicating that a given dataset has not been tested in the literature on the corresponding model. Competitors span a variety of approaches for graph classification, including classifiers working on the top of pure graph matching similarities [72], [74], kernel methods [77], [80] and several embedding techniques [75], [76], including GrC-based [5], [6], [33] and neural ones [78], [79]. As regards subsampling-based implementations, in Table 1 are reported the performances obtained at different subsampling rates in the form of min-max range.…”

Section: Comparison Against Current Approachesmentioning

confidence: 99%

An Enhanced Filtering-Based Information Granulation Procedure for Graph Embedding and Classification

Martino

2021

IEEE Access

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Granular Computing is a powerful information processing paradigm for synthesizing advanced pattern recognition systems in non-conventional domains. In this paper, a novel procedure for the automatic synthesis of suitable information granules is proposed. The procedure leverages a joint sensitivity-vs-specificity score that accounts the meaningfulness of candidate information granules for each class considered in the classification problem at hand. Only statistically relevant granules are retained for a graph embedding procedure towards a geometric space, in which standard classification systems can be used without alterations. Performance tests have been carried out by considering open access datasets of fully labelled graphs with arbitrarily complex nodes and/or edges attributes that, by definition, must rely on inexact graph matching procedures to quantify dissimilarities. Two variants of the procedure are investigated: a standard variant, which aims at automatically finding suitable information granules for solving the classification problem as a whole, and a class-specific metric learning variant, in which the optimization procedure is performed in a class-aware fashion. In the latter case, each class will have its own set of information granules, along with the corresponding parameters defining distinct instances of the dissimilarity measure. Computational results show that the proposed algorithm is able to outperform the vast majority of current approaches for graph classification, while at the same time returning a grey-box model, interpretable by field-experts.

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Learning error-correcting graph matching with a multiclass neural network

Cited by 13 publications

References 21 publications

On the unification of the graph edit distance and graph matching problems

On the unification of the graph edit distance and graph matching problems

Towards the Detection of Fake News on Social Networks Contributing to the Improvement of Trust and Transparency in Recommendation Systems: Trends and Challenges

An Enhanced Filtering-Based Information Granulation Procedure for Graph Embedding and Classification

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