In the research area of community detection which aims at detecting some highly cohesive vertex subsets in social network, there mainly exist some problems, such as the algorithms with comparatively excellent quality ...
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ISBN:
(纸本)9781479986965
In the research area of community detection which aims at detecting some highly cohesive vertex subsets in social network, there mainly exist some problems, such as the algorithms with comparatively excellent quality of the final partitioning usually have high time complexity and some other fast algorithms often result in low quality of partitioning or other disadvantages. Nowadays, the increasing demands for community detection in large-scale social networks necessitate the use of distributed and scalable methods to detect communities in an effective and efficient manner. label propagation algorithm (LPA), whose time complexity is O(m) on a network with m edges, is a near linear time algorithm to detect community effectively. Besides, owing to having good scalability, the parallel version of LPA (DLPA) is suitable for community detection in large-scale social networks. However, DLPA synchronously updates the vertices labels, which usually brings about label oscillations and results in low quality of partitioning. In this paper, we analyze the drawbacks of DLPA and propose a novel method C-DLPA, which combines DLPA with the notion of maximal cliques and at the same time utilizes a new updating mechanism that updating each node' label by probability of its adjacent nodes, to make final partitioning become more accurate and to avoid oscillations effectively. The experimental results show that C-DLPA has better performance is not only low time cost by as much to avoid oscillations but its community detection accuracy compared with DLPA.
Real world MANETs often exhibit an inherent community structure in their topological connectivity and in the evolution of the topology over time. Such temporal community structure of MANETs has been shown to be extrem...
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ISBN:
(纸本)9781479960361
Real world MANETs often exhibit an inherent community structure in their topological connectivity and in the evolution of the topology over time. Such temporal community structure of MANETs has been shown to be extremely useful in improving the performance of routing and content-based routing in MANETs [1]-[4]. However, detecting temporal communities in a completely distributed and real time manner is a hard problem, and it is often performed offline with knowledge of the full network topology over time. We propose CLAN, a distributed and real-time protocol for detecting temporal communities in MANETs. CLAN is an adaptation of the label propagation algorithm [5] to distributed and time-varying graphs that MANETs are. A key novel component of CLAN is local rules for community rediscovery as the network evolves. CLAN also uses a weighted version of the network topology where the weights are defined using a novel notion of social entropy to promote stability of communities. Extensive simulation results demonstrate that CLAN is quick to converge, incurs minimal overhead and is as effective as centralized approaches to temporal community detection. We also demonstrate how the temporal community structure can be used by designing a hierarchical routing protocol that achieves the delivery ratio of the OLSR [6] routing protocol at a fraction of the overhead.
How communities form can depend on the geospatial location of people within a social network. Here, we investigated the implementation of the label propagation algorithm (LPA) and labelRankT community detection algori...
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ISBN:
(纸本)9780769550121
How communities form can depend on the geospatial location of people within a social network. Here, we investigated the implementation of the label propagation algorithm (LPA) and labelRankT community detection algorithm in Gephi, a graph visualization tool. We researched extending these community detection algorithms to incorporate the geospatial distance between nodes in a network as a limiting factor for the automatic detection of community formation.
Background: Many large-scale studies analyzed high-throughput genomic data to identify altered pathways essential to the development and progression of specific types of cancer. However, no previous study has been ext...
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Background: Many large-scale studies analyzed high-throughput genomic data to identify altered pathways essential to the development and progression of specific types of cancer. However, no previous study has been extended to provide a comprehensive analysis of pathways disrupted by copy number alterations across different human cancers. Towards this goal, we propose a network-based method to integrate copy number alteration data with human protein-protein interaction networks and pathway databases to identify pathways that are commonly disrupted in many different types of cancer. Results: We applied our approach to a data set of 2,172 cancer patients across 16 different types of cancers, and discovered a set of commonly disrupted pathways, which are likely essential for tumor formation in majority of the cancers. We also identified pathways that are only disrupted in specific cancer types, providing molecular markers for different human cancers. Analysis with independent microarray gene expression datasets confirms that the commonly disrupted pathways can be used to identify patient subgroups with significantly different survival outcomes. We also provide a network view of disrupted pathways to explain how copy number alterations affect pathways that regulate cell growth, cycle, and differentiation for tumorigenesis. Conclusions: In this work, we demonstrated that the network-based integrative analysis can help to identify pathways disrupted by copy number alterations across 16 types of human cancers, which are not readily identifiable by conventional overrepresentation-based and other pathway-based methods. All the results and source code are available at http://***/NetPathID/.
How communities form can depend on the geospatial location of people within a social network. Here, we investigated the implementation of the label propagation algorithm (LPA) and labelRankT community detection algori...
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ISBN:
(纸本)9781479903016
How communities form can depend on the geospatial location of people within a social network. Here, we investigated the implementation of the label propagation algorithm (LPA) and labelRankT community detection algorithm in Gephi, a graph visualization tool. We researched extending these community detection algorithms to incorporate the geospatial distance between nodes in a network as a limiting factor for the automatic detection of community formation.
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