A Novel Method for Dynamic Community Discovery

2012 ◽  
Author(s):  
Ruixin Ma ◽  
Fancheng Meng
Keyword(s):  
Community Discovery ◽  
Novel Method ◽  
Dynamic Community

scholarly journals A Novel Method for Router-to-AS Mapping Based on Graph Community Discovery

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 87 ◽  
Author(s):  
Hangyu Hu ◽  
Weiyi Liu ◽  
Gaolei Fei ◽  
Song Yang ◽  
Guangmin Hu
Keyword(s):  
Clustering Algorithm ◽  
Autonomous Systems ◽  
Accuracy Rate ◽  
Community Discovery ◽  
Internet Topology ◽  
Progressive Development ◽  
Network Interaction ◽  
Essential Components ◽  
Novel Method ◽  
Time And Space Complexity

The last decades have witnessed the progressive development of research on Internet topology at the router or autonomous systems (AS) level. Routers are essential components of ASes, which dominate their behaviors. It is important to identify the affiliation between routers and ASes because this contributes to a deeper understanding of the topology. However, the existing methods that assign a router to an AS, based on the origin AS of its IP addresses do not make full use of the information during the network interaction procedure. In this paper, we propose a novel method to assign routers to their owners’ AS, based on community discovery. First, we use the initial AS information along with router-pair similarities to construct a weighted router level graph; secondly, with the large amount of graph data (more than 2M nodes and 19M edges) from the CAIDA ITDK project, we propose a fast hierarchy clustering algorithm with time and space complexity, which are both linear for graph community discovery. Finally, router-to-AS mapping is completed, based on these AS communities. Experimental results show that the effectiveness and robustness of the proposed method. Combining with AS communities, our method could have the higher accuracy rate reaching to 82.62% for Routers-to-AS mapping, while the best accuracy of prior works is plateaued at 65.44%.

A Framework for Detecting Weighted Communities in the International Trade Network

2014 ◽  
Vol 513-517 ◽  
pp. 2059-2062
Author(s):  
Lei Ming Yan ◽  
Jin Han
Keyword(s):  
Social Networks ◽  
International Trade ◽  
Social Network ◽  
Network Analysis ◽  
Trade Network ◽  
Open Problems ◽  
Community Discovery ◽  
Detection Algorithms ◽  
Dynamic Community ◽  
International Trade Network

Community discovery is a crucial task in social network analysis, especially in describing the evolution of social networks. Although some works have focused on finding the dynamic community, there are still some open problems need to be conquered, such as analyzing the dynamic and weighted community. In this paper, we propose a framework for analyzing weighted communities and their evolutions via clustering correlated weight vectors to enhance existing community detection algorithms. The International trade network is used to verify our framework. Experiments show that the framework discovers and captures the evolving behaviors with temporal elements and weight values.

scholarly journals Dynamic Community Discovery via Common Subspace Projection

2021 ◽  
Author(s):  
Lanlan Yu ◽  
Ping Li ◽  
Jie Zhang ◽  
Juergen Kurths
Keyword(s):  
Subspace Projection ◽  
Community Discovery ◽  
Dynamic Community

scholarly journals A propagation-based seed-centric local community detection for multilayer environment: The case study of colon adenocarcinoma

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255718
Author(s):  
Ehsan Pournoor ◽  
Zaynab Mousavian ◽  
Abbas Nowzari-Dalini ◽  
Ali Masoudi-Nejad
Keyword(s):  
Local Community ◽  
Single Layer ◽  
Colon Adenocarcinoma ◽  
Functional Enrichment ◽  
Community Discovery ◽  
Local Methods ◽  
Local Community Detection ◽  
Novel Method ◽  
Discovery Algorithms

Regardless of all efforts on community discovery algorithms, it is still an open and challenging subject in network science. Recognizing communities in a multilayer network, where there are several layers (types) of connections, is even more complicated. Here, we concentrated on a specific type of communities called seed-centric local communities in the multilayer environment and developed a novel method based on the information cascade concept, called PLCDM. Our simulations on three datasets (real and artificial) signify that the suggested method outstrips two known earlier seed-centric local methods. Additionally, we compared it with other global multilayer and single-layer methods. Eventually, we applied our method on a biological two-layer network of Colon Adenocarcinoma (COAD), reconstructed from transcriptomic and post-transcriptomic datasets, and assessed the output modules. The functional enrichment consequences infer that the modules of interest hold biomolecules involved in the pathways associated with the carcinogenesis.

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