Results 1  10
of
125
Computing the shape of brain network using graph filtration and GromovHaudorff metric
 in 14th Int. Conf. Med. Image Computing Computer Assist. Intervent. (MICCAI
"... Abstract. The difference between networks has been often assessed by the difference of global topological measures such as the clustering coefficient, degree distribution and modularity. In this paper, we introduce a new framework for measuring the network difference using the GromovHausdorff (GH) ..."
Abstract

Cited by 8 (6 self)
 Add to MetaCart
) distance, which is often used in shape analysis. In order to apply the GH distance, we define the shape of the brain network by piecing together the patches of locally connected nearest neighbors using the graph filtration. The shape of the network is then transformed to an algebraic form called the single
Metrics Useful in Network Tomography Studies.
"... Abstract — To facilitate the development of statistical methods geared at analyzing data from networks, it is important to have metrics which define and measure distances between a network’s links, its paths, and also between different networks. This is particularly important in the rapidly growing ..."
Abstract
 Add to MetaCart
area of network tomography which plays a central role in studies of data, communication, and internet traffic. We propose such metrics, outline some of their properties, and motivate them with two very recent applications. The proposed metrics are simple yet have appealing properties. Index Terms—distance
NED: An InterGraph Node Metric Based On Edit Distance
"... ABSTRACT Node similarity is fundamental in graph analytics. However, node similarity between nodes in different graphs (intergraph nodes) has not received enough attention yet. The intergraph node similarity is important in learning a new graph based on the knowledge extracted from an existing gra ..."
Abstract
 Add to MetaCart
graph (transfer learning on graphs) and has applications in biological, communication, and social networks. In this paper, we propose a novel distance function for measuring intergraph node similarity with edit distance, called NED. In NED, two nodes are compared according to their local neighborhood
Learning a DegreeAugmented Distance Metric From a Network
"... In many naturally occurring networks, connected nodes tend to have empirical similarities [1], which is a phenomenon commonly referred to as homophily. It is useful to learn how to relate the network homophily to the measurable features from data. However, due to the inherent structural nature of ne ..."
Abstract
 Add to MetaCart
algorithm that learns a similarity metric and a set of degreebased score functions that together provide a structureaware, distancebased method for link prediction. This method, degree distributional metric learning (DDML) is an extension of structure preserving metric learning (SPML) [4], both of which
TestRetest Reliability of Graph Metrics in Functional Brain Networks: A RestingState fNIRS Study
"... Recent research has demonstrated the feasibility of combining functional nearinfrared spectroscopy (fNIRS) and graph theory approaches to explore the topological attributes of human brain networks. However, the testretest (TRT) reliability of the application of graph metrics to these networks rema ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Recent research has demonstrated the feasibility of combining functional nearinfrared spectroscopy (fNIRS) and graph theory approaches to explore the topological attributes of human brain networks. However, the testretest (TRT) reliability of the application of graph metrics to these networks
Multiview Sequencedata Representation and Nonmetric Distancefunction Learning
, 2005
"... Sequencedata analysis plays a key role in many scientific studies and realworld applications such as bioinformatics, data stream, and sensor networks, where sequence data are processed and their semantics interpreted. In this paper we address two relevant issues: sequencedata representation, and ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
, and representationtosemantics mapping. For representation, since the best one is dependent upon the application being used and even the types of queries, we propose representing sequence data in multiple views. For each representation, we propose methods to construct a valid distance metric to compare sequences
Global Training of Document Processing Systems using Graph Transformer Networks.
 In Proc. of Computer Vision and Pattern Recognition
, 1997
"... We propose a new machine learning paradigm called Graph Transformer Networks that extends the applicability of gradientbased learning algorithms to systems composed of modules that take graphs as inputs and produce graphs as output. Training is performed by computing gradients of a global objective ..."
Abstract

Cited by 24 (5 self)
 Add to MetaCart
We propose a new machine learning paradigm called Graph Transformer Networks that extends the applicability of gradientbased learning algorithms to systems composed of modules that take graphs as inputs and produce graphs as output. Training is performed by computing gradients of a global
On Bounds for Diffusion, Discrepancy and Fill Distance Metrics
"... Criteria for optimally discretizing measurable sets in Euclidean space is a difficult and old problem which relates directly to the problem of good numerical integration rules or finding points of low discrepancy. On the other hand, learning meaningful descriptions of a finite number of given points ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
for suitable measures with discrete support. The diffusion metric is used to learn via normalized graph Laplacian dimension reduction and the discepancy is used to discretize. Dedicated to Alexander Gorban, Andrei Zinovyev and their coorganisers for the wonderful international workshop on large data sets held
11 On Bounds for Diffusion, Discrepancy and Fill Distance Metrics
"... Summary. Criteria for optimally discretizing measurable sets in Euclidean space is a difficult and old problem which relates directly to the problem of good numerical integration rules or finding points of low discrepancy. On the other hand, learning meaningful descriptions of a finite number of giv ..."
Abstract
 Add to MetaCart
distance on X for suitable measures with discrete support. The diffusion metric is used to learn via normalized graph Laplacian dimension reduction and the discepancy is used to discretize.
Optimal Hierarchical Decompositions for Congestion Minimization in Networks
, 2008
"... Hierarchical graph decompositions play an important role in the design of approximation and online algorithms for graph problems. This is mainly due to the fact that the results concerning the approximation of metric spaces by tree metrics (e.g. [10, 11, 14, 16]) depend on hierarchical graph decompo ..."
Abstract

Cited by 58 (1 self)
 Add to MetaCart
decompositions. In this line of work a probability distribution over tree graphs is constructed from a given input graph, in such a way that the tree distances closely resemble the distances in the original graph. This allows it, to solve many problems with a distancebased cost function on trees
Results 1  10
of
125