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Modern Methods for Characterization of Social Networks through Network Models
Published in Natalie M. Scala, James P. Howard, Handbook of Military and Defense Operations Research, 2020
Christine M. Schubert Kabban, Fairul Mohd-Zaid, Richard F. Deckro
An updated survey was recently conducted by Livi and Rizzi (2013) with a focus on Inexact Matching methods that are divided into Graph Edit Distance (GED) based, Graph Kernel-based, and Graph Embedding based techniques. GED based techniques fall in the family of tree searches whereas kernel and embedding based techniques fall in the continuous optimization family where the discrete problem is transformed into the continuous space in order to take advantage of the vast amount of metrics and matching techniques available in that space. Kernel-based techniques transform the graphs onto an induced feature space for evaluation whereas Graph Embedding based techniques transform the graphs to obtain a general vector representation and utilizes the metric space to compute dissimilarities. Livi and Rizzi (2013) suggested that all of the types of methods considered have polynomial complexity, but the computation cost is still dependent on a set of parameters that have to be tuned specifically to the application at hand.
Tree-Walk Kernels for Computer Vision
Published in Olivier Lézoray, Leo Grady, Image Processing and Analysis with Graphs, 2012
We propose to model appearance of images using region adjacency graphs obtained by morphological segmentation. If enough segments are used, i.e., if the image (and hence the objects of interest) is over-segmented, then the segmentation enables us to reduce the dimension of the image while preserving the boundaries of objects. Image dimensionality goes from millions of pixels down to hundreds of segments, with little loss of information. Those segments are naturally embedded in a planar graph structure. Our approach takes into account graph planarity. The goal of this section is to show that one can feed kernel-based learning methods with a kernel measuring appropriate region adjacency graph similarity, called the region adjacency graph kernel, for the purpose of image classification. Note that this kernel was previously referred to as the segmentation graph kernel in the original paper [15].
Graph Representation Learning for Protein Classification
Published in Ranjeet Kumar Rout, Saiyed Umer, Sabha Sheikh, Amrit Lal Sangal, Artificial Intelligence Technologies for Computational Biology, 2023
The main idea of the graph kernel method is to decompose the graph into substructures and the Weisfeiler-Lehman kernel method is one of such approaches [20]. WL-kernel extracts node-level features using the iterative neighbourhood aggregation approach, which enables the model to gather neighbourhood information and accumulate this rich information into a graph level representation.
Discriminant subgraph learning from functional brain sensory data
Published in IISE Transactions, 2022
Lujia Wang, Todd J. Schwedt, Catherine D. Chong, Teresa Wu, Jing Li
The existing graph classification methods fall into two general categories: similarity-based and subgraph-feature-based approaches. Similarity-based approaches learn global similarity between each pair of graphs, which is further used by conventional classification algorithms such as Support Vector Machine (SVM) for classification of the graphs. Global similarity is measured by graph kernels or graph embedding. Schölkopf et al. (2003) introduced a unified account of a family of kernels that are defined via label sequences for handling graph data. Other types of kernels have been proposed to measure graph similarity, such as kernels between vertex and/or edge label histograms (Gärtner et al., 2003), graphlet kernels (Shervashidze et al., 2009), random walk kernels (Sugiyama and Borgwardt, 2015), and Weisfeiler–Lehman graph kernel (Shervashidze et al., 2011). Graph embedding has also been used for similarity-based approaches. Riesen and Bunke (2009) proposed a graph classification system using Lipschitz embedded graphs. One limitation of similarity-based graph classification methods is that the similarity is computed based on the global structure of graphs. However, some sub-structures may not have discriminant power, and therefore, including them in the computation of graph similarity may negatively affect the classification accuracy. This limitation is better addressed by the other category of graph classification methods based on subgraph features.