Semi-supervised community detection using label propagation

2014 ◽  
Vol 28 (29) ◽  
pp. 1450208 ◽  
Author(s):  
Dong Liu ◽  
Hong-Yu Bai ◽  
Hui-Jia Li ◽  
Wen-Jun Wang
Keyword(s):  
Community Detection ◽  
Real World ◽  
Prior Information ◽  
Label Propagation ◽  
Background Information ◽  
Network Nodes ◽  
Detection Approach ◽  
Real World Applications ◽  
Topology Information ◽  
Almost All

Almost all existing approaches for community detection only make use of the network topology information, which completely ignore the background information of the network. However, in many real world applications, we may know some prior information that could be useful in detecting the community structures. Specifically, the true community assignments of certain nodes are known in advance. In this paper, a novel semi-supervised community detection approach is proposed based on label propagation, which can utilize prior information to guide the discovery process of community structure. Our algorithm can propagate the labels from the labeled nodes to the whole network nodes. The algorithm is evaluated on several artificial and real-world networks and shows that it is highly effective in recovering communities.

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 LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 497
Author(s):  
Huan Li ◽  
Ruisheng Zhang ◽  
Zhili Zhao ◽  
Xin Liu
Keyword(s):  
Community Detection ◽  
Real World ◽  
Label Propagation ◽  
Node Importance ◽  
Propagation Algorithm ◽  
Synthetic Networks

Community detection is of great significance in understanding the structure of the network. Label propagation algorithm (LPA) is a classical and effective method, but it has the problems of randomness and instability. An improved label propagation algorithm named LPA-MNI is proposed in this study by combining the modularity function and node importance with the original LPA. LPA-MNI first identify the initial communities according to the value of modularity. Subsequently, the label propagation is used to cluster the remaining nodes that have not been assigned to initial communities. Meanwhile, node importance is used to improve the node order of label updating and the mechanism of label selecting when multiple labels are contained by the maximum number of nodes. Extensive experiments are performed on twelve real-world networks and eight groups of synthetic networks, and the results show that LPA-MNI has better accuracy, higher modularity, and more reasonable community numbers when compared with other six algorithms. In addition, LPA-MNI is shown to be more robust than the traditional LPA algorithm.

An improved label propagation algorithm based on node intimacy for community detection in networks

2018 ◽  
Vol 32 (25) ◽  
pp. 1850279 ◽  
Author(s):  
Hanzhang Kong ◽  
Qinma Kang ◽  
Chao Liu ◽  
Wenquan Li ◽  
Hong He ◽  
...  
Keyword(s):  
Community Detection ◽  
Label Propagation ◽  
Propagation Process ◽  
Node Importance ◽  
Propagation Algorithm ◽  
Efficiency And Effectiveness ◽  
Specific Order ◽  
Topology Information ◽  
The Stability ◽  
Local Topology

Community detection in complex network analysis is a quite challenging problem spanning many applications in various disciplines such as biology, physics and social network. A large number of methods have been developed for this problem, among which the label propagation algorithm (LPA) has attracted much attention because of its advantages of nearly-linear running time and easy implementation. Nevertheless, the random updating order and tie-breaking strategy in LPA make the algorithm unstable and may even lead to the formation of a monster community. In this paper, an improved LPA called LPA-INTIM is proposed for solving the community detection problem. Firstly, an intimacy matrix is constructed using local topology information for measuring the intimacy between nodes. And then, the node importance is calculated to ensure that nodes are updated in a specific order. Finally, the label influence is evaluated for updating node label during the label propagation process. In addition, we introduce a novel tightness function to improve the stability of the proposed algorithm. By the comparison with the methods presented in the literatures, experimental results on real-world and synthetic networks show the efficiency and effectiveness of our proposed algorithm.

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.

scholarly journals NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 260 ◽  
Author(s):  
Bingyang Huang ◽  
Chaokun Wang ◽  
Binbin Wang
Keyword(s):  
Community Detection ◽  
Real World ◽  
State Of The Art ◽  
Label Propagation ◽  
Detection Methods ◽  
Overlapping Communities ◽  
Propagation Algorithm ◽  
Pruning Strategy ◽  
Node Similarity ◽  
Attributed Networks

With the enrichment of the entity information in the real world, many networks with attributed nodes are proposed and studied widely. Community detection in these attributed networks is an essential task that aims to find groups where the intra-nodes are much more densely connected than the inter-nodes. However, many existing community detection methods in attributed networks do not distinguish overlapping communities from non-overlapping communities when designing algorithms. In this paper, we propose a novel and accurate algorithm called Node-similarity-based Multi-Label Propagation Algorithm (NMLPA) for detecting overlapping communities in attributed networks. NMLPA first calculates the similarity between nodes and then propagates multiple labels based on the network structure and the node similarity. Moreover, NMLPA uses a pruning strategy to keep the number of labels per node within a suitable range. Extensive experiments conducted on both synthetic and real-world networks show that our new method significantly outperforms state-of-the-art methods.

Label propagation algorithm based on local cycles for community detection

2015 ◽  
Vol 29 (05) ◽  
pp. 1550029 ◽  
Author(s):  
Xian-Kun Zhang ◽  
Song Fei ◽  
Chen Song ◽  
Xue Tian ◽  
Yang-Yue Ao
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Real World ◽  
Label Propagation ◽  
Fast Method ◽  
Propagation Process ◽  
Node Label ◽  
Propagation Algorithm ◽  
Random Node ◽  
Local Cycle

Label propagation algorithm (LPA) has been proven to be an extremely fast method for community detection in large complex networks. But an important issue of the algorithm has not yet been properly addressed that random update orders in label propagation process hamper the algorithm robustness of algorithm. We note that when there are multiple maximal labels among a node neighbors' labels, choosing a node' label from which there is a local cycle to the node instead of a random node' label can avoid the labels propagating among communities at random. In this paper, an improved LPA based on local cycles is given. We have evaluated the proposed algorithm on computer-generated networks with planted partition and some real-world networks whose community structure are already known. The result shows that the performance of the proposed approach is even significantly improved.

scholarly journals Partial Label Learning with Unlabeled Data

2019 ◽  
Author(s):  
Qian-Wei Wang ◽  
Yu-Feng Li ◽  
Zhi-Hua Zhou
Keyword(s):  
Real World ◽  
Reconstruction Error ◽  
Unlabeled Data ◽  
Label Propagation ◽  
Data Sets ◽  
Real World Data ◽  
Real World Applications ◽  
Training Examples ◽  
Partial Label Learning ◽  
Character Classification

Partial label learning deals with training examples each associated with a set of candidate labels, among which only one label is valid. Previous studies typically assume that the candidate label sets are provided for all training examples. In many real-world applications such as video character classification, however, it is generally difficult to label a large number of instances and there exists much data left to be unlabeled. We call this kind of problem semi-supervised partial label learning. In this paper, we propose the SSPL method to address this problem. Specifically, an iterative label propagation procedure between partial label examples and unlabeled instances is employed to disambiguate the candidate label sets of partial label examples as well as assign valid labels to unlabeled instances. The importance of unlabeled instances increases adaptively as the number of iteration increases, since they carry richer labeling information. Finally, unseen instances are classified based on the minimum reconstruction error on both partial label and unlabeled instances. Experiments on real-world data sets clearly validate the effectiveness of the proposed SSPL method.

scholarly journals An optimisation tool for robust community detection algorithms using content and topology information

2019 ◽  
Vol 76 (1) ◽  
pp. 226-254 ◽  
Author(s):  
Amhmed Bhih ◽  
Princy Johnson ◽  
Martin Randles
Keyword(s):  
Community Detection ◽  
Real World ◽  
Information Networks ◽  
Detection Methods ◽  
Sources Of Information ◽  
Detection Algorithms ◽  
And Topology ◽  
Node Attribute ◽  
Topology Information ◽  
Content Information

Abstract With the recent prevalence of information networks, the topic of community detection has gained much interest among researchers. In real-world networks, node attribute (content information) is also available in addition to topology information. However, the collected topology information for networks is usually noisy when there are missing edges. Furthermore, the existing community detection methods generally focus on topology information and largely ignore the content information. This makes the task of community detection for incomplete networks very challenging. A new method is proposed that seeks to address this issue and help improve the performance of the existing community detection algorithms by considering both sources of information, i.e. topology and content. Empirical results demonstrate that our proposed method is robust and can detect more meaningful community structures within networks having incomplete information, than the conventional methods that consider only topology information.

scholarly journals Hypergraph Label Propagation Network

2020 ◽  
Vol 34 (04) ◽  
pp. 6885-6892
Author(s):  
Yubo Zhang ◽  
Nan Wang ◽  
Yufeng Chen ◽  
Changqing Zou ◽  
Hai Wan ◽  
...  
Keyword(s):  
Real World ◽  
Computational Cost ◽  
Label Propagation ◽  
Learning Methods ◽  
Sina Weibo ◽  
Hypergraph Learning ◽  
Real World Applications ◽  
Alternative Approaches ◽  
Propagation Network ◽  
High Computational Cost

In recent years, with the explosion of information on the Internet, there has been a large amount of data produced, and analyzing these data is useful and has been widely employed in real world applications. Since data labeling is costly, lots of research has focused on how to efficiently label data through semi-supervised learning. Among the methods, graph and hypergraph based label propagation algorithms have been a widely used method. However, traditional hypergraph learning methods may suffer from their high computational cost. In this paper, we propose a Hypergraph Label Propagation Network (HLPN) which combines hypergraph-based label propagation and deep neural networks in order to optimize the feature embedding for optimal hypergraph learning through an end-to-end architecture. The proposed method is more effective and also efficient for data labeling compared with traditional hypergraph learning methods. We verify the effectiveness of our proposed HLPN method on a real-world microblog dataset gathered from Sina Weibo. Experiments demonstrate that the proposed method can significantly outperform the state-of-the-art methods and alternative approaches.

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