Scalable discovery of best clusters on large graphs

Macropol, Kathy; Singh, Ambuj K.

doi:10.14778/1920841.1920930

Cited by 54 publications

(40 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Detection in research) that searches a dense groups of nodes and its aim is to analyze network to several linked components (communities) in such a way that nodes in each component have high-density connections, while nodes in different components have the lowest density of the proposed methods in this category algorithm SLPA [9], TopGC [10], SVINET [11], MCD [12], CGGC [13], CONCLUDE [14], DSE [15] and SPICi [16] can be cited.…”

Section: Link Based Clustering (Also Known As Communitymentioning

confidence: 99%

Offering a New Approach for Extracting Recurring Conceptual Links from Social Networks

Tabatabaee

2018

IRATJ

View full text Add to dashboard Cite

Massive amounts of data in social networks have made researchers look for ways to display a summary of the information provided and extract knowledge from them. Basic methods in the field used the criteria coming from graph theory, but new approaches are trying to take advantage of the realm of traditional data exploration and linked data. One of these new approaches is the approach called conceptual link approach, which was introduced to describe the social networks. In this approach, using the concept of contextual links, knowledge of the social network through a conceptual view of the dramatic structure is summarized as means of social networking. Conceptual perspective provides a summary of existing knowledge on a social network.

show abstract

Section: Link Based Clustering (Also Known As Communitymentioning

confidence: 99%

Offering a New Approach for Extracting Recurring Conceptual Links from Social Networks

Tabatabaee

2018

IRATJ

View full text Add to dashboard Cite

show abstract

“…Of the proposed methods in this category algorithm SLPA [9], TopGC [10], SVINET [11], MCD [12], CGGC [13], CONCLUDE [14], DSE [15] and SPICi [16] can be cited.…”

Section: ) Linkmentioning

confidence: 99%

D-MFCLMin: A New Algorithm for Extracting Frequent Conceptual Links from Social Networks

Tabatabaee¹

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Massive amounts of data in social networks have made researchers look for ways to display a summary of the information provided and extract knowledge from them. One of the new approaches to describe knowledge of the social network is through a concise structure called conceptual view. In order to build this view, it is first needed to extract conceptual links from the intended network. However, extracting these links for large scale networks is very time consuming. In this paper, a new algorithm for extracting frequent conceptual link from social networks is provided where by introducing the concept of dependency, it is tried to accelerate the process of extracting conceptual links. Although the proposed algorithm will be able to accelerate this process if there are dependencies between data, but the tests carried out on Pokec social network, which lacks dependency between its data, revealed that absence of dependency, increases execution time of extracting conceptual links only up to 15 percent.

show abstract

“…Graph clustering has been extensively studied in recent years [9][10][11][12][13][14][15][16][17][18][19][20][21]. Shiga et al [9] presented a clustering method which integrates numerical vectors with modularity into a spectral relaxation problem.…”

Section: Related Workmentioning

confidence: 99%

“…MLR-MCL [11] is a multilevel graph clustering algorithm using flows to deliver significant improvements in both quality and speed. TopGC [14] is a fast algorithm to probabilistically search large, edge weighted, directed graphs for their best clusters in linear time. BAGC [16] constructs a Bayesian probabilistic model to capture both structural and attribute aspects of graph.…”

Section: Related Workmentioning

confidence: 99%

Integrating Vertex-centric Clustering with Edge-centric Clustering for Meta Path Graph Analysis

Zhou

Liu

Buttler

2015

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

Meta paths are good mechanisms to improve the quality of graph analysis on heterogeneous information networks. This paper presents a meta path graph clustering framework, VEPathCluster, that combines meta path vertex-centric clustering with meta path edge-centric clustering for improving the clustering quality of heterogeneous networks. First, we propose an edge-centric path graph model to capture the meta-path dependencies between pairwise path edges. We model a heterogeneous network containing M types of meta paths as M vertex-centric path graphs and M edge-centric path graphs. Second, we propose a clustering-based multigraph model to capture the fine-grained clustering-based relationships between pairwise vertices and between pairwise path edges. We perform clustering analysis on both a unified vertex-centric path graph and each edge-centric path graph to generate vertex clustering and edge clusterings of the original heterogeneous network respectively. Third, a reinforcement algorithm is provided to tightly integrate vertex-centric clustering and edge-centric clustering by mutually enhancing each other. Finally, an iterative learning strategy is presented to dynamically refine both vertex-centric clustering and edge-centric clustering by continuously learning the contributions and adjusting the weights of different path graphs.

show abstract

Scalable discovery of best clusters on large graphs

Cited by 54 publications

References 29 publications

Offering a New Approach for Extracting Recurring Conceptual Links from Social Networks

Offering a New Approach for Extracting Recurring Conceptual Links from Social Networks

D-MFCLMin: A New Algorithm for Extracting Frequent Conceptual Links from Social Networks

Integrating Vertex-centric Clustering with Edge-centric Clustering for Meta Path Graph Analysis

Contact Info

Product

Resources

About