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[PDF] Top 20 Modularity-based Sparse Soft Graph Clustering

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Modularity-based Sparse Soft Graph Clustering

Modularity-based Sparse Soft Graph Clustering

... Clustering is a central problem in machine learning for which graph-based approaches have proven their efficiency. In this paper, we study a relaxation of the modularity maxi- mization ... Voir le document complet

11

Assessing the Quality of Multilevel Graph Clustering

Assessing the Quality of Multilevel Graph Clustering

... a clustering of a graph (a set partition of its vertices), and then iterate over each subgraph until some stopping condition is ...decided, based on some other criteria to find a best possible ... Voir le document complet

20

Graph-Based Approaches to Clustering Network-Constrained Trajectory Data

Graph-Based Approaches to Clustering Network-Constrained Trajectory Data

... trajectory clustering [6] results in a hierarchy of clusters where the optimal level ...flat clustering can still produce a high number of clusters: the analyst can start with the few, coarse clusters ... Voir le document complet

16

On sparse graph coding for coherent and noncoherent demodulation

On sparse graph coding for coherent and noncoherent demodulation

... the soft receiver. As shown in Fig. 1 for the case of CPFSK based detectors, the approximation is relatively accurate when compared to direct calculation of the mutual information ...considering ... Voir le document complet

7

Graph-Based Approaches to Clustering Network-Constrained Trajectory Data

Graph-Based Approaches to Clustering Network-Constrained Trajectory Data

... similarity graph, we use an implementation of the algorithm described in [7] (the full details of the used implementation as well as the pseudo- codes of its different steps can be found in ...similarity ... Voir le document complet

13

On sparse graph coding for coherent and noncoherent demodulation

On sparse graph coding for coherent and noncoherent demodulation

... the soft receiver. As shown in Fig. 1 for the case of CPFSK based detectors, the approximation is relatively accurate when compared to direct calculation of the mutual information ...considering ... Voir le document complet

6

Scalable Interactive Dynamic Graph Clustering on Multicore CPUs

Scalable Interactive Dynamic Graph Clustering on Multicore CPUs

... Terms—Structural graph clustering, SCAN, anytime clustering, parallel algorithm, multicore CPUs, dynamic ...a graph G = (V, E), where V is a set of vertices and E a set of edges, graph ... Voir le document complet

16

A Streaming Algorithm for Graph Clustering

A Streaming Algorithm for Graph Clustering

... perform graph clustering in the edge streaming ...the graph is presented as a sequence of edges that can be processed strictly ...algorithm based on the modularity function, which is a ... Voir le document complet

13

Spectral redemption in clustering sparse networks

Spectral redemption in clustering sparse networks

... to clustering and community detection in networks. However, for sparse networks the standard versions of these algorithms are suboptimal, in some cases completely failing to detect communities even when ... Voir le document complet

12

A Distributed and Incremental Algorithm for Large-Scale Graph Clustering

A Distributed and Incremental Algorithm for Large-Scale Graph Clustering

... Several graph clustering algorithms have been proposed in the ...examples, modularity-based approaches [23] that represent an optimiza- tion solution of the modularity measure for each ... Voir le document complet

29

Efficient Eigen-updating for Spectral Graph Clustering

Efficient Eigen-updating for Spectral Graph Clustering

... is graph clustering [32, 10], which aims to partition the vertices in a graph into groups or clusters, with dense internal connections and few connections between each ...the clustering issue ... Voir le document complet

28

Topologically Ordered Graph Clustering via Deterministic Annealing

Topologically Ordered Graph Clustering via Deterministic Annealing

... by graph clustering methods [12]: the graph is first clus- tered into a simplified graph and then rendered via standard graph visualization methods ...ordered clustering of the ... Voir le document complet

7

Hierarchical Graph Clustering using Node Pair Sampling

Hierarchical Graph Clustering using Node Pair Sampling

... The results are presented in Table 5 for the graphs considered so far and the graphs of Table 4, selected from the SNAP datasets [Leskovec and Krevl, 2014]. The cost function is normalized by the number of nodes n so as ... Voir le document complet

15

Accelerated spectral clustering using graph filtering of random signals

Accelerated spectral clustering using graph filtering of random signals

... in graph signal processing to propose a faster spectral clustering ...spectral clustering is based on the computation of the first k eigenvectors of the similarity matrix’ Laplacian, whose ... Voir le document complet

6

Template-Based Graph Clustering

Template-Based Graph Clustering

... exact modularity optimization is strongly NP-complete ...for clustering a graph performs greedy modularity ...increases modularity is joined until no potential pair increases ...greedy ... Voir le document complet

16

Modularity-Based Clustering for Network-Constrained Trajectories

Modularity-Based Clustering for Network-Constrained Trajectories

... Work Clustering trajectory data attracted many research in the last few ...apply modularity-optimization graph clustering in the context of trajectory ... Voir le document complet

7

Von Mises-Fisher based (co-)clustering for high-dimensional sparse data : application to text and collaborative filtering data

Von Mises-Fisher based (co-)clustering for high-dimensional sparse data : application to text and collaborative filtering data

... are based on Probabilistic Matrix Factorization (PMF) (Ma et ...social graph, so as to capture the influence between ...social graph through a shared user latent ...user-user graph, then ... Voir le document complet

169

Community detection in networks based on minimum spanning tree and modularity

Community detection in networks based on minimum spanning tree and modularity

... In this paper we propose a novel splitting and merging method for community detection in which a minimum spanning tree (MST) of dissimilarity between nodes in graph is emp[r] ... Voir le document complet

1

Parallel Jaccard and Related Graph Clustering Techniques

Parallel Jaccard and Related Graph Clustering Techniques

... coPaperDBLP graph Notice that using Jaccard and Jaccard-PageRank weights we often obtain a significant improvement of up to 160% in the quality of clustering up to about 32 ... Voir le document complet

10

Hemodynamic estimation based on Consensus Clustering

Hemodynamic estimation based on Consensus Clustering

... hemodynamic estimation were observed depending on the prior parcellation [4]. Moreover, a potential bias can be introduced using the classical GLM that does not enable important fluctuations of the HRF shape throughout ... Voir le document complet

5

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