Clustering coefficientGraph clusteringCombinatorial optimizationGraph clustering is an important issue for several applications associated with data analysis in graphs. However, the discovery of groups of highly
Overall, this paper makes the following two contributions: • We propose a graph-based view for a group of patients diagnosed with the same disease, named Weighted Patient Network, an efficient network method to extract underlying relationships among patients. • We propose a new framework for...
theconnectome1that can be modeled and analyzed with the tools of network science and graph theory2. Modeling the brain as a network allows us to explore local as well as distributed properties of brain organization, using both descriptive3and generative modeling approaches4. A hallmark of...
Overall, this paper makes the following two contributions: • We propose a graph-based view for a group of patients diagnosed with the same disease, named Weighted Patient Network, an efficient network method to extract underlying relationships among patients. • We propose a new framework for...
From the bar graph, it is evident that the computational complexities of HMRFO and MRFO are close and reasonable. The effect of the improved method in this paper on the algorithm complexity of MRFO is negligible. The low computational complexity and minimal computation cost make HMRFO suitable...
After obtaining the integrated network, we applied spectral clustering to assign labels to patients since spectral clustering is superior in capturing global structure of a graph. Moreover, we provide a method to estimate the optimal clustering number based on the diffused network. The advantage of ...
10) the number of times a particular neighborhood size prevailed as best performer (using overall Dice overlap coefficients) across the five age groups. There is ample clustering of winning numbers under the size 5 training dataset. Fig. 11 displays the Generalized Dice overlap score versus age-...
A second extension of the overall or global statistics builds on the general property that any statistic taking the form of a ratio of a quadratic form and its sum of squares can be interpreted as a bivariate regression coefficient. Equations (4) and (5) show that Moran's I has this ...
graphKnhas a trivial topology, in terms of complexity (since the average degree is always\(\left\langle k \right\rangle\) = n–1 and most of other metrics, such as average path length, network diameter, graph density, and clustering coefficient are equal to one), we filter the set...
Graph Theory Introduction Multiple kernel clustering (MKC) aims to optimally integrate the consensus information among multiple base kernels to generate a consensus kernel for improving clustering performance. In the last few years, MKC methods have been widely applied into various applications, with bene...