Combining Graph Seriation and Substructures Mining for Graph Recognition

Livi, Lorenzo; Vescovo, Guido Del; Rizzi, Antonello

doi:10.1007/978-3-642-36530-0_7

Cited by 13 publications

(11 citation statements)

References 28 publications

(28 reference statements)

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“…Analogously, the concepts of hitting (average probability that two random walkers are in the same state at the same time) and commute time (average return time to the initial state) in random walks have been used by Qiu and Hancock [57] to characterize graphs for pattern recognition purpose. Other approaches include computation of Ihara coefficients and Laplacian spectrum [43,58]. Finally, various interpretations of the concept of network entropy are studied [21], such as entropy of continuous time quantum walks [4,49], network ensemble entropy [4,11], network transfer entropy [8], von Neumann (quantum) entropy [35,72], and fuzzy entropy [42].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Graphs for Protein Structure Modeling and Recognition of Solubility

Livi¹,

Giuliani²,

Sadeghian³

2016

CBIO

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This paper deals with the relations among structural, topological, and chemical properties of the E.Coli proteome from the vantage point of the solubility/aggregation propensity of proteins. Each E.Coli protein is initially represented according to its known folded 3D shape. This step consists in representing the available E.Coli proteins in terms of graphs. We first analyze those graphs by considering pure topological characterizations, i.e., by analyzing the mass fractal dimension and the distribution underlying both shortest paths and vertex degrees. Results confirm the general architectural principles of proteins. Successively, we focus on the statistical properties of a representation of such graphs in terms of vectors composed of several numerical features, which we extracted from their structural representation. We found that protein size is the main discriminator for the solubility, while however there are other factors that help explaining the solubility degree. We finally analyze such data through a novel one-class classifier, with the aim of discriminating among very and poorly soluble proteins. Results are encouraging and consolidate the potential of pattern recognition techniques when employed to describe complex biological systems.

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Section: Introductionmentioning

confidence: 99%

Characterization of Graphs for Protein Structure Modeling and Recognition of Solubility

Livi¹,

Giuliani²,

Sadeghian³

2016

CBIO

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“…4. In order to merge very similar stable regions, the BSAS clustering algorithm [26,40] is used. The clustering threshold used in this step is the segmentation parameter s fus .…”

Section: Appendix 1: the Image Segmentation Proceduresmentioning

confidence: 99%

“…In the Cluster-based Classifier (CC) a partition f of S tr is generated by means of the BSAS [26,40], a free-clustering algorithm that depends on the scale parameter #, which is the maximum allowed radius for the clusters. The BSAS processes sequentially each pattern in S tr and assigns it to the closest cluster or it generates a new cluster if the distance from the closest cluster is higher than #.…”

Section: Cluster-based Classifiermentioning

confidence: 99%

Granular Computing Techniques for Classification and Semantic Characterization of Structured Data

et al. 2015

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We propose a system able to synthesize automatically a classification model and a set of interpretable decision rules defined over a set of symbols, corresponding to frequent substructures of the input dataset. Given a preprocessing procedure which maps every input element into a fully labeled graph, the system solves the classification problem in the graph domain. The extracted rules are then able to characterize semantically the classes of the problem at hand. The structured data that we consider in this paper are images coming from classification datasets: they represent an effective proving ground for studying the ability of the system to extract interpretable classification rules. For this particular input domain, the preprocessing procedure is based on a flexible segmentation algorithm whose behavior is defined by a set of parameters. The core inference engine uses a parametric graph edit dissimilarity measure. A genetic algorithm is in charge of selecting suitable values for the parameters, in order to synthesize a classification model based on interpretable rules which maximize the generalization capability of the model. Decision rules are defined over a set of information granules in the graph domain, identified by a frequent substructures miner. We compare the system with two other state-of-the-art graph classifiers, evidencing both its main strengths and limits

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“…This set is generated using a stochastic procedure based on Markov Chains [12], [13]. If ||=n, the Markov generation process is entirely described by its transition matrix T, that is, if we are generating strings of length l, the first symbol is chosen with uniform probability on , the next symbol is chosen with conditional probability p(s 2 |s 1 ), that is with a probability that depends only on the last selected symbol.…”

Section: A Problem Definition and Synthetic Datasetsmentioning

confidence: 99%

On the Problem of Modeling Structured Data with the MinSOD Representative

Vescovo¹,

Livi²,

Massimo³

et al. 2014

IJCTE

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Abstract-The possibility to face pattern recognition problems directly on structured domains (e.g., multimedia data, strings, graphs) is fundamental to the effective solution of many interesting applications. In this paper, we deal with a clustering problem defined in the string domain, focusing on the problem of cluster representation in data domains where only a dissimilarity measure can be fixed. To this aim, we adopt the MinSOD (Minimum Sum of Distances) cluster representation technique, which defines the representative as the element of the cluster minimizing the sum of dissimilarities from all the other elements in the considered set. Since the precise computation of the MinSOD have a high computational cost, we propose a suboptimal procedure consisting in computing the representative of the cluster considering only a reduced pool of samples, instead of the whole set of objects in the cluster. We have carried out some tests in order to ascertain the sensitivity of the clustering procedure with respect to the number of samples in the pool used to compute the MinSOD. Results show a good robustness of the proposed procedure. The implementations are available as part of the SPARE library, which is available as an open source project.

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Combining Graph Seriation and Substructures Mining for Graph Recognition

Cited by 13 publications

References 28 publications

Characterization of Graphs for Protein Structure Modeling and Recognition of Solubility

Characterization of Graphs for Protein Structure Modeling and Recognition of Solubility

Granular Computing Techniques for Classification and Semantic Characterization of Structured Data

On the Problem of Modeling Structured Data with the MinSOD Representative

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