Link community detection based on line graphs with a novel link similarity measure

2016 ◽  
Vol 30 (06) ◽  
pp. 1650023 ◽  
Author(s):  
Guishen Wang ◽  
Lan Huang ◽  
Yan Wang ◽  
Wei Pang ◽  
Qin Ma
Keyword(s):  
Community Detection ◽  
Similarity Measure ◽  
Detection Algorithm ◽  
Line Graphs ◽  
The Novel ◽  
On Line ◽  
Community Detection Algorithm ◽  
Relationship Of ◽  
The Relationship ◽  
Link Similarity

Link community gradually unfolds its capacity in complex network research. In this paper, a novel link similarity measure on line graphs is proposed. This measure can be adapted to different types of networks with an adjustable parameter. We prove its value converges to a limit on line graphs with the relationship of the nonneighbor links taken into account. Based on this similarity measure, we propose a novel link community detection algorithm for link clustering on line graphs. The detection algorithm combines the novel link similarity measure with the classic Markov Cluster (MCL) Algorithm and determines the link community partitions by calculating an extended modularity measure. Extensive experiments on two types of complex networks demonstrate the effectiveness, reliability and rationality of our solution in contrast to the other two classical algorithms.

scholarly journals Semisupervised Community Detection by Voltage Drops

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Min Ji ◽  
Dawei Zhang ◽  
Fuding Xie ◽  
Ying Zhang ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Potential Theory ◽  
Community Detection ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Machine Learning Methods ◽  
Voltage Drops ◽  
Discrete Potential Theory ◽  
Community Detection Algorithm ◽  
The Relationship

Many applications show that semisupervised community detection is one of the important topics and has attracted considerable attention in the study of complex network. In this paper, based on notion of voltage drops and discrete potential theory, a simple and fast semisupervised community detection algorithm is proposed. The label propagation through discrete potential transmission is accomplished by using voltage drops. The complexity of the proposal isOV+Efor the sparse network withVvertices andEedges. The obtained voltage value of a vertex can be reflected clearly in the relationship between the vertex and community. The experimental results on four real networks and three benchmarks indicate that the proposed algorithm is effective and flexible. Furthermore, this algorithm is easily applied to graph-based machine learning methods.

Multiresolution community detection in weighted complex networks

2019 ◽  
Vol 30 (02n03) ◽  
pp. 1950016
Author(s):  
Hao Long ◽  
Xiao-Wei Liu
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Network Flow ◽  
Network Science ◽  
Detection Algorithm ◽  
Expansion Strategy ◽  
Community Detection Algorithm ◽  
Dominant Structure ◽  
The Relationship ◽  
Different Levels

The community is the dominant structure that exhibits different features and multifold functions of complex networks from different levels; accordingly, multiresolution community detection is of critical importance in network science. In this paper, inspired by the ideas of the network flow, we propose an intensity-based community detection algorithm, i.e. ICDA, to detect multiresolution communities in weighted networks. First, the edge intensity is defined to quantify the relationship between each pair of connected nodes, and the vertices connected by the edges with higher intensities are denoted as core nodes, while the others are denoted as marginal nodes. Second, by applying the expansion strategy, the algorithm merges the closely connected core nodes as the initial communities and attaches marginal nodes to the nearest initial communities. To guarantee a higher internal density for the ultimate communities, the captured communities are further adjusted according to their densities. Experimental results of real and synthetic networks illustrate that our approach has higher performance and better accuracy. Meanwhile, a multiresolution investigation of some real networks shows that the algorithm can provide hierarchical details of complex networks with different thresholds.

scholarly journals Enhanced Community Detection Based on Cross Time for Higher Visibility in Supply Chain: A Six-Steps Model Framework

2019 ◽  
Vol 9 (1) ◽  
pp. 4509-4513
Keyword(s):  
Supply Chain ◽  
Community Detection ◽  
Detection Algorithm ◽  
Internal Quality ◽  
Supply Chain Network ◽  
Model Framework ◽  
Graph Analytics ◽  
Community Detection Algorithm ◽  
The Impact ◽  
The Relationship

Increasing the visibility in supply chain network had decrease the risk in industries. However, the current Cross-Time approach for temporal community detection algorithm in the visibility has fix number of communities and lack of operation such as split or merge. Therefore, improving temporal community detection algorithm to represent the relationship in supply chain network for higher visibility is significant. This paper proposed six steps model framework that aim: (1) To construct the nodes and vertices for temporal graph representing the relationship in supply chain network; (2) To propose an enhanced temporal community detection algorithm in graph analytics based on Cross-time approach and (3) To evaluate the enhanced temporal community detection algorithm in graph analytics for representing relationship in supply chain network based on external and internal quality analysis. The proposed framework utilizes the Cross-Time approach for enhancing temporal community detection algorithm. The expected result shows that the Enhanced Temporal Community Detection Algorithm based on Cross Time approach for higher visibility in supply chain network is the major finding when implementing this proposed framework. The impact advances industrialization through efficient supply chain in industry leading to urbanization.

scholarly journals The Flag-based Algorithm - A Novel Greedy Method that Optimizes Protein Communities Detection

2011 ◽  
Vol 6 (1) ◽  
pp. 33
Author(s):  
Razvan Bocu ◽  
Sabin Tabirca
Keyword(s):  
Community Detection ◽  
Detection Algorithm ◽  
Biological Data ◽  
Sequential Algorithm ◽  
The Novel ◽  
C Programming Language ◽  
Analysis Process ◽  
Biological Phenomena ◽  
C Programming ◽  
Community Detection Algorithm

Proteins and the networks they determine, called interactome networks, have received attention at an important degree during the last years, because they have been discovered to have an influence on some complex biological phenomena, such as problematic disorders like cancer. This paper presents a contribution that aims to optimize the detection of protein communities through a greedy algorithm that is implemented in the C programming language. The optimization involves a double improvement in relation to protein communities detection, which is accomplished both at the algorithmic and programming level. The resulting implementation’s performance was carefully tested on real biological data and the results acknowledge the relevant speedup that the optimization determines. Moreover, the results are in line with the previous findings that our current research produced, as it reveals and confirms the existence of some important properties of those proteins that participate in the carcinogenesis process. Apart from being particularly useful for research purposes, the novel community detection algorithm also dramatically speeds up the proteomic databases analysis process, as compared to some other sequential community detection approaches, and also to the sequential algorithm of Newman and Girvan.

The role and relationship of mindreading and social attunement in HRI – position statements of interdisciplinary researchers. Workshop HRI'20

2020 ◽  
Author(s):  
Agnieszka Wykowska ◽  
Jairo Pérez-Osorio ◽  
Stefan Kopp
Keyword(s):  
Mental States ◽  
Human Robot Interaction ◽  
Human Interaction ◽  
Artificial Agents ◽  
The Novel ◽  
Robot Interaction ◽  
Novel Coronavirus ◽  
Relationship Of ◽  
The Relationship

This booklet is a collection of the position statements accepted for the HRI’20 conference workshop “Social Cognition for HRI: Exploring the relationship between mindreading and social attunement in human-robot interaction” (Wykowska, Perez-Osorio & Kopp, 2020). Unfortunately, due to the rapid unfolding of the novel coronavirus at the beginning of the present year, the conference and consequently our workshop, were canceled. On the light of these events, we decided to put together the positions statements accepted for the workshop. The contributions collected in these pages highlight the role of attribution of mental states to artificial agents in human-robot interaction, and precisely the quality and presence of social attunement mechanisms that are known to make human interaction smooth, efficient, and robust. These papers also accentuate the importance of the multidisciplinary approach to advance the understanding of the factors and the consequences of social interactions with artificial agents.

Police Records

2019 ◽  
pp. 797-819
Author(s):  
Cristina Vatulescu
Keyword(s):  
Nineteenth Century ◽  
Detective Fiction ◽  
Media History ◽  
Late Nineteenth Century ◽  
The Novel ◽  
Police Records ◽  
History Of ◽  
Late Nineteenth ◽  
Relationship Of ◽  
The Relationship

This chapter approaches police records as a genre that gains from being considered in its relationships with other genres of writing. In particular, we will follow its long-standing relationship to detective fiction, the novel, and biography. Going further, the chapter emphasizes the intermedia character of police records not just in our time but also throughout their existence, indeed from their very origins. This approach opens to a more inclusive media history of police files. We will start with an analysis of the seminal late nineteenth-century French manuals prescribing the writing of a police file, the famous Bertillon-method manuals. We will then track their influence following their adoption nationally and internationally, with particular attention to the politics of their adoption in the colonies. We will also touch briefly on the relationship of early policing to other disciplines, such as anthropology and statistics, before moving to a closer look at its intersections with photography and literature.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Adjustment Strategy ◽  
Topology Information ◽  
Overlapping Community ◽  
Real World Datasets ◽  
Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

Sign in / Sign up

Export Citation Format

Share Document