On Caching for Local Graph Clustering Algorithms

2013 ◽  
pp. 56-67
Author(s):  
René Speck ◽  
Axel-Cyrille Ngonga Ngomo
Keyword(s):  
Clustering Algorithms ◽  
Graph Clustering ◽  
Local Graph

A Practical Comparison of Local Graph Clustering Algorithms

2019 ◽  
Vol 67 (7) ◽  
pp. 64-69
Author(s):  
Rashed Khalil Salem ◽  
Wafaa Tawfik Abdel Moneim ◽  
Mohamed Monir Hassan
Keyword(s):  
Clustering Algorithms ◽  
Graph Clustering ◽  
Local Graph

scholarly journals Local Graph Clustering with Network Lasso

2020 ◽  
pp. 1-1
Author(s):  
Alexander Jung ◽  
Yasmin Sarcheshmehpour
Keyword(s):  
Graph Clustering ◽  
Local Graph

scholarly journals Parallel local graph clustering

2016 ◽  
Vol 9 (12) ◽  
pp. 1041-1052 ◽  
Author(s):  
Julian Shun ◽  
Farbod Roosta-Khorasani ◽  
Kimon Fountoulakis ◽  
Michael W. Mahoney
Keyword(s):  
Graph Clustering ◽  
Local Graph

scholarly journals Entropy-Based Graph Clustering of PPI Networks for Predicting Overlapping Functional Modules of Proteins

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1271
Author(s):  
Hoyeon Jeong ◽  
Yoonbee Kim ◽  
Yi-Sue Jung ◽  
Dae Ryong Kang ◽  
Young-Rae Cho
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Clustering Algorithms ◽  
Graph Clustering ◽  
Functional Modules ◽  
Protein Protein Interactions ◽  
Overlapping Clusters ◽  
Novel Proteins ◽  
Ppi Networks ◽  
Function Modules

Functional modules can be predicted using genome-wide protein–protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task. In particular, the detection of overlapping clusters is necessary because a protein is involved in multiple functions under different conditions. graph entropy (GE) is a novel metric to assess the quality of clusters in a large, complex network. In this study, the unweighted and weighted GE algorithm is evaluated to prove the validity of predicting function modules. To measure clustering accuracy, the clustering results are compared to protein complexes and Gene Ontology (GO) annotations as references. We demonstrate that the GE algorithm is more accurate in overlapping clusters than the other competitive methods. Moreover, we confirm the biological feasibility of the proteins that occur most frequently in the set of identified clusters. Finally, novel proteins for the additional annotation of GO terms are revealed.

Constrained Local Graph Clustering by Colored Random Walk

2019 ◽  
Author(s):  
Yaowei Yan ◽  
Yuchen Bian ◽  
Dongsheng Luo ◽  
Dongwon Lee ◽  
Xiang Zhang
Keyword(s):  
Random Walk ◽  
Graph Clustering ◽  
Local Graph

scholarly journals Metric-Constrained Optimization for Graph Clustering Algorithms

2019 ◽  
Vol 1 (2) ◽  
pp. 333-355 ◽  
Author(s):  
Nate Veldt ◽  
David F. Gleich ◽  
Anthony Wirth ◽  
James Saunderson
Keyword(s):  
Constrained Optimization ◽  
Clustering Algorithms ◽  
Graph Clustering

scholarly journals Novel Algorithms for Graph Clustering Applied to Human Activities

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1089
Author(s):  
Nebojsa Budimirovic ◽  
Nebojsa Bacanin
Keyword(s):  
Human Activities ◽  
Clustering Algorithms ◽  
Weighted Graph ◽  
Graph Clustering ◽  
Recall Rate ◽  
Number Of Clusters ◽  
Starting Point ◽  
Measurement Units ◽  
Novel Algorithms ◽  
Precision Rate

In this paper, a novel algorithm (IBC1) for graph clustering with no prior assumption of the number of clusters is introduced. Furthermore, an additional algorithm (IBC2) for graph clustering when the number of clusters is given beforehand is presented. Additionally, a new measure of evaluation of clustering results is given—the accuracy of formed clusters (T). For the purpose of clustering human activities, the procedure of forming string sequences are presented. String symbols are gained by modeling spatiotemporal signals obtained from inertial measurement units. String sequences provided a starting point for forming the complete weighted graph. Using this graph, the proposed algorithms, as well as other well-known clustering algorithms, are tested. The best results are obtained using novel IBC2 algorithm: T = 96.43%, Rand Index (RI) 0.966, precision rate (P) 0.918, recall rate (R) 0.929 and balanced F-measure (F) 0.923.

scholarly journals Local hypergraph clustering using capacity releasing diffusion

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243485
Author(s):  
Rania Ibrahim ◽  
David F. Gleich
Keyword(s):  
Graph Clustering ◽  
Learning Task ◽  
Higher Order ◽  
Spectral Graph Theory ◽  
Clustering Technique ◽  
Clustering Quality ◽  
Connected Cluster ◽  
Local Graph ◽  
Synthetic Datasets ◽  
Better Than

Local graph clustering is an important machine learning task that aims to find a well-connected cluster near a set of seed nodes. Recent results have revealed that incorporating higher order information significantly enhances the results of graph clustering techniques. The majority of existing research in this area focuses on spectral graph theory-based techniques. However, an alternative perspective on local graph clustering arises from using max-flow and min-cut on the objectives, which offer distinctly different guarantees. For instance, a new method called capacity releasing diffusion (CRD) was recently proposed and shown to preserve local structure around the seeds better than spectral methods. The method was also the first local clustering technique that is not subject to the quadratic Cheeger inequality by assuming a good cluster near the seed nodes. In this paper, we propose a local hypergraph clustering technique called hypergraph CRD (HG-CRD) by extending the CRD process to cluster based on higher order patterns, encoded as hyperedges of a hypergraph. Moreover, we theoretically show that HG-CRD gives results about a quantity called motif conductance, rather than a biased version used in previous experiments. Experimental results on synthetic datasets and real world graphs show that HG-CRD enhances the clustering quality.

On analyzing graphs with motif-paths

2021 ◽  
Vol 14 (6) ◽  
pp. 1111-1123
Author(s):  
Xiaodong Li ◽  
Reynold Cheng ◽  
Kevin Chen-Chuan Chang ◽  
Caihua Shan ◽  
Chenhao Ma ◽  
...  
Keyword(s):  
Link Prediction ◽  
Graph Clustering ◽  
Order Structure ◽  
Graph Analysis ◽  
High Order Structure ◽  
Node Ranking ◽  
Local Graph ◽  
Edge Based ◽  
Graph Motif ◽  
Better Than

Path-based solutions have been shown to be useful for various graph analysis tasks, such as link prediction and graph clustering. However, they are no longer adequate for handling complex and gigantic graphs. Recently, motif-based analysis has attracted a lot of attention. A motif, or a small graph with a few nodes, is often considered as a fundamental unit of a graph. Motif-based analysis captures high-order structure between nodes, and performs better than traditional "edge-based" solutions. In this paper, we study motif-path , which is conceptually a concatenation of one or more motif instances. We examine how motif-paths can be used in three path-based mining tasks, namely link prediction, local graph clustering and node ranking. We further address the situation when two graph nodes are not connected through a motif-path, and develop a novel defragmentation method to enhance it. Experimental results on real graph datasets demonstrate the use of motif-paths and defragmentation techniques improves graph analysis effectiveness.

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