scholarly journals Detecting Topic-oriented Overlapping Community Using Hybrid Hypergraph Model

2016 ◽  
Vol 11 (4) ◽  
pp. 538
Author(s):  
Gui-lan Shen ◽  
Xiao-ping Yang ◽  
Jie Sun
Keyword(s):  
Community Detection ◽  
Detection Method ◽  
Information Networks ◽  
Overlapping Community Detection ◽  
Structure Information ◽  
Hypergraph Model ◽  
Overlapping Community ◽  
New Challenges ◽  
Information Work ◽  
Real World Datasets

A large number of emerging information networks brings new challenges to the overlapping community detection. The meaningful community should be topicoriented. However, the topology-based methods only reflect the strength of connection, but ignore the consistency of the topics. This paper explores a topic-oriented overlapping community detection method for information work. The method utilizes a hybrid hypergraph model to combine the node content and structure information naturally. Two connections for hyperedge pair, including real connection and virtual connection are defined. A novel hyperedge pair similarity measure is proposed by combining linearly extended common neighbors metric for real connection and incremental fitness for virtual connection. Extensive experiments on two real-world datasets validate our proposed method outperforms other baseline algorithms.

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.

scholarly journals A Local Overlapping Community Detection Method in Complex Networks

2012 ◽  
Vol 6-7 ◽  
pp. 985-990
Author(s):  
Yan Peng ◽  
Yan Min Li ◽  
Lan Huang ◽  
Long Ju Wu ◽  
Gui Shen Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Detection Method ◽  
Experimental Results ◽  
Overlapping Community Detection ◽  
Greedy Strategy ◽  
Structure Detection ◽  
Overlapping Community

Community structure detection has great importance in finding the relationships of elements in complex networks. This paper presents a method of simultaneously taking into account the weak community structure definition and community subgraph density, based on the greedy strategy for community expansion. The results are compared with several previous methods on artificial networks and real world networks. And experimental results verify the feasibility and effectiveness of our approach.

scholarly journals Overlapping Community Detection Based on Membership Degree Propagation

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 15
Author(s):  
Rui Gao ◽  
Shoufeng Li ◽  
Xiaohu Shi ◽  
Yanchun Liang ◽  
Dong Xu
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Computational Time ◽  
Membership Degree ◽  
Overlapping Community Detection ◽  
Overlapping Community ◽  
Real World Datasets

A community in a complex network refers to a group of nodes that are densely connected internally but with only sparse connections to the outside. Overlapping community structures are ubiquitous in real-world networks, where each node belongs to at least one community. Therefore, overlapping community detection is an important topic in complex network research. This paper proposes an overlapping community detection algorithm based on membership degree propagation that is driven by both global and local information of the node community. In the method, we introduce a concept of membership degree, which not only stores the label information, but also the degrees of the node belonging to the labels. Then the conventional label propagation process could be extended to membership degree propagation, with the results mapped directly to the overlapping community division. Therefore, it obtains the partition result and overlapping node identification simultaneously and greatly reduces the computational time. The proposed algorithm was applied to a synthetic Lancichinetti–Fortunato–Radicchi (LFR) dataset and nine real-world datasets and compared with other up-to-date algorithms. The experimental results show that our proposed algorithm is effective and outperforms the comparison methods on most datasets. Our proposed method significantly improved the accuracy and speed of the overlapping node prediction. It can also substantially alleviate the computational complexity of community structure detection in general.

Detecting overlapping communities using ensemble-based distributed neighbourhood threshold method in social networks

2021 ◽  
pp. 1-17
Author(s):  
Rajesh Jaiswal ◽  
Sheela Ramanna
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Threshold Method ◽  
Boundary Node ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Starting Point ◽  
Overlapping Community ◽  
Real World Datasets

In this paper, we have proposed a novel overlapping community detection algorithm based on an ensemble approach with a distributed neighbourhood threshold method (EnDNTM). EnDNTM uses pre-partitioned disjoint communities generated by the ensemble mechanism and then analyzes the neighbourhood distribution of boundary nodes in disjoint communities to detect overlapping communities. It is a form of seed-based global method since boundary nodes are considered as seeds and become the starting point for detecting overlapping communities. A threshold value for each boundary node is used as the minimum influence by the neighbours of a node in order to determine its belongingness to any community. The effectiveness of the EnDNTM algorithm has been demonstrated by testing with five synthetic benchmark datasets and fifteen real-world datasets. The performance of the EnDNTM algorithm was compared with seven overlapping community detection algorithms. The F1-score, normalized mutual information ONMI and extended modularity Qo⁢v metrics were used to measure the quality of the detected communities. EnDNTM outperforms comparable algorithms on 4 out of 5 synthetic benchmarks datasets, 11 out of 15 real world datasets and gives comparable results with the remaining datasets. Experiments on various synthetic and real world datasets reveal that for a majority of datasets, the proposed ensemble-based distributed neighbourhood threshold method is able to select the best disjoint clusters produced by a disjoint method from a collection of methods for detecting overlapping communities.

scholarly journals Parallelizing SLPA for Scalable Overlapping Community Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Konstantin Kuzmin ◽  
Mingming Chen ◽  
Boleslaw K. Szymanski
Keyword(s):  
Community Detection ◽  
Linear Time ◽  
Network Size ◽  
Label Propagation ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Multithreaded Programming ◽  
Overlapping Community ◽  
Real World Datasets

Communities in networks are groups of nodes whose connections to the nodes in a community are stronger than with the nodes in the rest of the network. Quite often nodes participate in multiple communities; that is, communities can overlap. In this paper, we first analyze what other researchers have done to utilize high performance computing to perform efficient community detection in social, biological, and other networks. We note that detection of overlapping communities is more computationally intensive than disjoint community detection, and the former presents new challenges that algorithm designers have to face. Moreover, the efficiency of many existing algorithms grows superlinearly with the network size making them unsuitable to process large datasets. We use the Speaker-Listener Label Propagation Algorithm (SLPA) as the basis for our parallel overlapping community detection implementation. SLPA provides near linear time overlapping community detection and is well suited for parallelization. We explore the benefits of a multithreaded programming paradigm and show that it yields a significant performance gain over sequential execution while preserving the high quality of community detection. The algorithm was tested on four real-world datasets with up to 5.5 million nodes and 170 million edges. In order to assess the quality of community detection, at least 4 different metrics were used for each of the datasets.

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