scholarly journals Mobile Recommendation Based on Link Community Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Kun Deng ◽  
Jianpei Zhang ◽  
Jing Yang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Mobile Networks ◽  
Real World ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Greedy Search ◽  
Recommendation Algorithm

Since traditional mobile recommendation systems have difficulty in acquiring complete and accurate user information in mobile networks, the accuracy of recommendation is not high. In order to solve this problem, this paper proposes a novel mobile recommendation algorithm based on link community detection (MRLD). MRLD executes link label diffusion algorithm and maximal extended modularity (EQ) of greedy search to obtain the link community structure, and overlapping nodes belonging analysis (ONBA) is adopted to adjust the overlapping nodes in order to get the more accurate community structure. MRLD is tested on both synthetic and real-world networks, and the experimental results show that our approach is valid and feasible.

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.

Using functional dependency and node closure to detect communities from the sparse network

2018 ◽  
Vol 32 (03) ◽  
pp. 1850018
Author(s):  
Yang Zhou ◽  
Haifei Miao ◽  
Wei Liu ◽  
Xiaoyun Chen ◽  
Jianjun Cheng
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Time Complexity ◽  
Functional Dependency ◽  
Experimental Results ◽  
Time Consumption ◽  
Database Theory ◽  
Effectiveness And Efficiency

Community structure is one of the most important features of complex networks, a large number of methods have been proposed to extract community structures from networks. However, some of those methods suffer from the high time complexity, and some of them cannot obtain the acceptable results. In this paper, we borrow the idea from the database theory, and propose the concepts of functional dependency (FD) between nodes and node closure first, then we utilize these concepts to extract communities. This method takes both effectiveness and efficiency into consideration, the community detection process can be accomplished with O(m) time consumption. We conducted extensive experiments both on some synthetic networks and on some real-world networks, the experimental results demonstrate that the method can detect communities from a given network successfully.

An algorithm Walktrap-SPM for detecting overlapping community structure

2017 ◽  
Vol 31 (15) ◽  
pp. 1750121 ◽  
Author(s):  
Fang Hu ◽  
Youze Zhu ◽  
Yuan Shi ◽  
Jianchao Cai ◽  
Luogeng Chen ◽  
...  
Keyword(s):  
Community Structure ◽  
Random Walk ◽  
Community Detection ◽  
Real World ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Running Time ◽  
Overlapping Community ◽  
Negative Links ◽  
Novel Algorithm

In this paper, based on Walktrap algorithm with the idea of random walk, and by selecting the neighbor communities, introducing improved signed probabilistic mixture (SPM) model and considering the edges within the community as positive links and the edges between the communities as negative links, a novel algorithm Walktrap-SPM for detecting overlapping community is proposed. This algorithm not only can identify the overlapping communities, but also can greatly increase the objectivity and accuracy of the results. In order to verify the accuracy, the performance of this algorithm is tested on several representative real-world networks and a set of computer-generated networks based on LFR benchmark. The experimental results indicate that this algorithm can identify the communities accurately, and it is more suitable for overlapping community detection. Compared with Walktrap, SPM and LMF algorithms, the presented algorithm can acquire higher values of modularity and NMI. Moreover, this new algorithm has faster running time than SPM and LMF algorithms.

A Collaborative Filtering Recommendation Algorithm Incorporated with Life Cycle

2013 ◽  
Vol 765-767 ◽  
pp. 630-633 ◽  
Author(s):  
Chong Lin Zheng ◽  
Kuang Rong Hao ◽  
Yong Sheng Ding
Keyword(s):  
Life Cycle ◽  
Collaborative Filtering ◽  
Recommendation Systems ◽  
Experimental Results ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Time Information ◽  
Successful Technology

Collaborative filtering recommendation algorithm is the most successful technology for recommendation systems. However, traditional collaborative filtering recommendation algorithm does not consider the change of time information. For this problem,this paper improve the algorithm with two new methods:Predict score incorporated with time information in order to reflect the user interest change; Recommend according to scores by adding the weight information determined by the item life cycle. Experimental results show that the proposed algorithm outperforms the traditional item in accuracy.

Community detection via closure extension

2018 ◽  
Vol 29 (12) ◽  
pp. 1850119
Author(s):  
Jingming Zhang ◽  
Jianjun Cheng ◽  
Xiaosu Feng ◽  
Xiaoyun Chen
Keyword(s):  
Community Structure ◽  
Computational Complexity ◽  
Community Detection ◽  
Network Structure ◽  
Real World ◽  
Prior Information ◽  
State Of The Art ◽  
Local Information ◽  
Second Step ◽  
Novel Method

Identifying community structure in networks plays an important role in understanding the network structure and analyzing the network features. Many state-of-the-art algorithms have been proposed to identify the community structure in networks. In this paper, we propose a novel method based on closure extension; it performs in two steps. The first step uses the similarity closure or correlation closure to find the initial community structure. In the second step, we merge the initial communities using Modularity [Formula: see text]. The proposed method does not need any prior information such as the number or sizes of communities, and it is able to obtain the same resulting communities in multiple runs. Moreover, it is noteworthy that our method has low computational complexity because of considering only local information of network. Some real-world and synthetic graphs are used to test the performance of the proposed method. The results demonstrate that our method can detect deterministic and informative community structure in most cases.

Expert Detection and Recommendation Model With User-Generated Tags in Collaborative Tagging Systems

2020 ◽  
Vol 31 (4) ◽  
pp. 24-45
Author(s):  
Mengmeng Shen ◽  
Jun Wang ◽  
Ou Liu ◽  
Haiying Wang
Keyword(s):  
Collaborative Filtering ◽  
Community Detection ◽  
Real World ◽  
Agglomerative Hierarchical Clustering ◽  
Collaborative Tagging ◽  
Excellent Performance ◽  
Recommendation Algorithm ◽  
Proposed Model ◽  
Target User ◽  
Real World Datasets

Tags generated in collaborative tagging systems (CTSs) may help users describe, categorize, search, discover, and navigate content, whereas the difficulty is how to go beyond the information explosion and obtain experts and the required information quickly and accurately. This paper proposes an expert detection and recommendation (EDAR) model based on semantics of tags; the framework consists of community detection and EDAR. Specifically, this paper firstly mines communities based on an improved agglomerative hierarchical clustering (I-AHC) to cluster tags and then presents a community expert detection (CED) algorithm for identifying community experts, and finally, an expert recommendation algorithm is proposed based the improved collaborative filtering (CF) algorithm to recommend relevant experts for the target user. Experiments are carried out on real world datasets, and the results from data experiments and user evaluations have shown that the proposed model can provide excellent performance compared to the benchmark method.

scholarly journals LIMITED RESOLUTION AND MULTIRESOLUTION METHODS IN COMPLEX NETWORK COMMUNITY DETECTION

2007 ◽  
Vol 07 (03) ◽  
pp. L209-L214 ◽  
Author(s):  
JUSSI M. KUMPULA ◽  
JARI SARAMÄKI ◽  
KIMMO KASKI ◽  
JÁNOS KERTÉSZ
Keyword(s):  
Simulated Annealing ◽  
Community Structure ◽  
Complex Network ◽  
Community Detection ◽  
Real World ◽  
Tuning Parameter ◽  
Challenging Problem ◽  
Resolution Limit ◽  
Network Community ◽  
Multiresolution Methods

Detecting community structure in real-world networks is a challenging problem. Recently, it has been shown that the resolution of methods based on optimizing a modularity measure or a corresponding energy is limited; communities with sizes below some threshold remain unresolved. One possibility to go around this problem is to vary the threshold by using a tuning parameter, and investigate the community structure at variable resolutions. Here, we analyze the resolution limit and multiresolution behavior for two different methods: a q-state Potts method proposed by Reichard and Bornholdt, and a recent multiresolution method by Arenas, Fernández, and Gómez. These methods are studied analytically, and applied to three test networks using simulated annealing.

scholarly journals Incremental Density-Based Link Clustering Algorithm for Community Detection in Dynamic Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Fanrong Meng ◽  
Feng Zhang ◽  
Mu Zhu ◽  
Yan Xing ◽  
Zhixiao Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Topology ◽  
Clustering Algorithm ◽  
Dynamic Networks ◽  
Experimental Results ◽  
Extended Version ◽  
High Quality ◽  
Detection Algorithms

Community detection in complex networks has become a research hotspot in recent years. However, most of the existing community detection algorithms are designed for the static networks; namely, the connections between the nodes are invariable. In this paper, we propose an incremental density-based link clustering algorithm for community detection in dynamic networks, iDBLINK. This algorithm is an extended version of DBLINK which is proposed in our previous work. It can update the local link community structure in the current moment through the change of similarity between the edges at the adjacent moments, which includes the creation, growth, merging, deletion, contraction, and division of link communities. Extensive experimental results demonstrate that iDBLINK not only has a great time efficiency, but also maintains a high quality community detection performance when the network topology is changing.

Detecting communities in networks using a Bayesian nonparametric method

2014 ◽  
Vol 28 (28) ◽  
pp. 1450199
Author(s):  
Shengze Hu ◽  
Zhenwen Wang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Real World ◽  
Detection Method ◽  
Generative Models ◽  
Detection Methods ◽  
Model Parameters ◽  
Nonparametric Method ◽  
Bayesian Nonparametric ◽  
New Community

In the real world, a large amount of systems can be described by networks where nodes represent entities and edges the interconnections between them. Community structure in networks is one of the interesting properties revealed in the study of networks. Many methods have been developed to extract communities from networks using the generative models which give the probability of generating networks based on some assumption about the communities. However, many generative models require setting the number of communities in the network. The methods based on such models are lack of practicality, because the number of communities is unknown before determining the communities. In this paper, the Bayesian nonparametric method is used to develop a new community detection method. First, a generative model is built to give the probability of generating the network and its communities. Next, the model parameters and the number of communities are calculated by fitting the model to the actual network. Finally, the communities in the network can be determined using the model parameters. In the experiments, we apply the proposed method to the synthetic and real-world networks, comparing with some other community detection methods. The experimental results show that the proposed method is efficient to detect communities in networks.

Detecting community structure by belonging intensity analysis of intermediate nodes

2019 ◽  
Vol 30 (11) ◽  
pp. 1950079
Author(s):  
Mengjia Shen ◽  
Dong Lv ◽  
Zhixin Ma
Keyword(s):  
Community Structure ◽  
Communication Networks ◽  
Community Detection ◽  
Real World ◽  
Intensity Analysis ◽  
Human Communication ◽  
Intermediate Node ◽  
Detection Algorithms ◽  
Interactive Behavior

Community structure is a common characteristic of complex networks and community detection is an important methodology to reveal the structure of real-world networks. In recent years, many algorithms have been proposed to detect the high-quality communities in real-world networks. However, these algorithms have shortcomings of performing calculation on the whole network or defining objective function and the number of commonties in advance, which affects the performance and complexity of community detection algorithms. In this paper, a novel algorithm has been proposed to detect communities in networks by belonging intensity analysis of intermediate nodes, named BIAS, which is inspired from the interactive behavior in human communication networks. More specifically, intermediate nodes are middlemen between different groups in social networks. BIAS algorithm defines belonging intensity using local interactions and metrics between nodes, and the belonging intensity of intermediate node in different communities is analyzed to distinguish which community the intermediate node belongs to. The experiments of our algorithm with other state-of-the-art algorithms on synthetic networks and real-world networks have shown that BIAS algorithm has better accuracy and can significantly improve the quality of community detection without prior information.

Sign in / Sign up

Export Citation Format

Share Document