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Protein-protein interaction (PPI) networks are dynamic in the real world. That is, at different times and under different conditions, the interaction among proteins may or may not be active. In different dataset, PPI networks might be gathered as static or dynamic networks. For the conversion of static PPI networks to time graphs, i.e., dynamic PPI networks, additional information like gene expression and gene co-expression profiles is used. One of the challenges in system biology is to determine appropriate thresholds for converting static PPI networks to dynamic PPI networks based on active proteins. In the available methods, fixed thresholds are used for all genes. However, the purpose of this study is to determine an adaptive unique threshold for each gene. In this study, the available additional information at different times and conditions and gold-standard protein complexes was employed to determine fitting thresholds. By so doing, the problem is converted into an optimization problem. Thereafter, the problem is solved using the firefly meta-heuristic optimization algorithm. One of the most remarkable aspects of this study is determining the attractiveness function in the firefly algorithm. In this study, attraction is defined as a combination of standard complexes and gene co-expressions. Then, active proteins are specified utilizing the created thresholds. The MCL, ClusterOne, MCODE and Coach algorithms are used for final evaluation. The experimental results about BioGRID dataset and CYC2008 gold-standard protein complexes indicated that the produced dynamic PPI networks by the proposed method have better results than the earlier methods. [ABSTRACT FROM AUTHOR]

In the present paper, a novel graph-based approach to the shape decomposition problem is addressed. The shape is appropriately transformed into a visibility graph enriched with local neighborhood information. A two-step diffusion process is then applied to the visibility graph that efficiently enhances the information provided, thus leading to a more robust and meaningful graph construction. Inspired by the notion of a clique as a strict cluster definition, the dominant sets algorithm is invoked, slightly modified to comport with the specific problem of defining shape parts. The cluster cohesiveness and a node participation vector are two important outputs of the proposed graph partitioning method. Opposed to most of the existing techniques, the final number of the clusters is determined automatically, by estimating the cluster cohesiveness on a random network generation process. Experimental results on several shape databases show the effectiveness of our framework for graph-based shape decomposition. [ABSTRACT FROM AUTHOR]

The problem of scalability of correlation clustering (CC) is addressed by reducing the number of variables involved in the SDP formulation. A nonlinear programming formulation is obtained instead of SDP formulation, which reduces the number of variables. The new formulation is solved through limited memory Broyden Fletcher Goldfarb Shanno method. We demonstrate the potential of the nonlinear formulation on large graph datasets having more than ten thousand vertices and nine million edges. The proposed scalable formulation is experimentally shown not to compromise on quality of the obtained clusters. We compare the scalable formulation results with those of the original CC formulation. We compare the scalable formulation results with the original CC formulation, a constrained spectral clustering method which uses edge labels of the graph and differs only in the way clusters are obtained by defining the cut on the given graph and with a variant of constraint spectral clustering known as self-taught spectral clustering. [ABSTRACT FROM AUTHOR]

n unavoidable problem of most existing skeletonization algorithms for handwriting images is the production of undesired artifacts or pattern distortions. This paper presents a method of identifying these unreliable segments to improve the skeletons of handwriting images. In this method, a novel feature called iteration time is exploited, by which each unreliable segment can be treated as a set of points with exceptional iteration times. First, the iteration time of each skeleton point is calculated, and an undirected graph is built from the skeleton whose edges are weighted by defining a distance measurement between each pair of connected nodes based on iteration time. Then the set of unreliable segments is achieved by a graph clustering algorithm with an effective clustering quality function. Finally, the probability of two jointed reliable segments belonging to a continuous pair is estimated by a best-matched method, and a cubic B-spline interpolation is applied to reconstruct unreliable parts of the skeleton. Experimental results show that the proposed method can detect unreliable segments effectively and produce a skeleton that is closer to the original writing trajectory. [ABSTRACT FROM AUTHOR]