Sentence examples for methods for graph from inspiring English sources

His current research interests are on machine learning and combinatorial methods for Graph analysis (including community detection, graph clustering and embeddings, influence maximization), Text mining including Graph of Words, word embeddings with applications to web advertising and marketing, event detection and summarization.

Discriminative Subgraphs (Conf, Ratio, Gtest, HSIC): Supervised feature selection methods for graph classification based upon confidence [12], frequency ratio [16, 17, 18], G test score [37], and HSIC [20], respectively.

The most used methods for graph drawing are force-directed methods [ 17- 19].

The methods presented here are just a selection of the available methods for graph inference.

Existing methods for graph comparison can be categorized into the following three major types: distance-based, alignment-based and kernel-based methods.

This review summarizes current literature on graph kernel and graph alignment methods for graph comparison strategies, as well as module detection approaches including seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods.

As a result, it is of importance to develop drawing methods for graphs whose nodes are of different sizes and shapes, in order to meet the need of real-world applications.

Motivated by the fact that some data in real-world applications are naturally represented by means of graphs, while compressing and converting them to vectorial representations would definitely lose structural information, kernel methods for graphs have been extensively studied for a decade [28].

Additionally, some methods for graph-theoretical analysis are provided.

gSSC: A semi-supervised feature selection method for graph classification based upon both labeled and unlabeled graphs.

Another method for graph database indexing is Closure-Tree (CTree) (He and Singh, 2006).