WLNI-LPA: Detecting Overlapping Communities in Attributed Networks based on Label Propagation Process

2021 ◽  
Author(s):  
Imen El Kouni ◽  
Wafa Karoui ◽  
Lotfi Ben Romdhane
Keyword(s):  
Label Propagation ◽  
Propagation Process ◽  
Overlapping Communities ◽  
Attributed Networks

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.

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.

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.

Detecting overlapping communities in networks via dominant label propagation

2015 ◽  
Vol 24 (1) ◽  
pp. 018703 ◽  
Author(s):  
He-Li Sun ◽  
Jian-Bin Huang ◽  
Yong-Qiang Tian ◽  
Qin-Bao Song ◽  
Huai-Liang Liu
Keyword(s):  
Label Propagation ◽  
Overlapping Communities

Circularly Searching Core Nodes Based Label Propagation Algorithm for Community Detection

2016 ◽  
Vol 30 (08) ◽  
pp. 1659024 ◽  
Author(s):  
Ronghua Shang ◽  
Weitong Zhang ◽  
Licheng Jiao
Keyword(s):  
Community Detection ◽  
Time Complexity ◽  
Label Propagation ◽  
Propagation Process ◽  
Small Communities ◽  
Network Partition ◽  
The Core ◽  
Modified Method ◽  
Propagation Algorithm ◽  
Degree Of Membership

With the application of community detection in complex networks becoming more and more extensive, the application of more and more algorithms for community detection are proposed and improved. Among these algorithms, the label propagation algorithm is simple, easy to perform and its time complexity is linear, but it has a strong randomness. Small communities in the label propagation process are easy to be swallowed. Therefore, this paper proposes a method to improve the partition results of label propagation algorithm based on the pre-partition by circularly searching core nodes and assigning label for nodes according to similarity of nodes. First, the degree of each node of the network is calculated. We go through the whole network to find the nodes with the maximal degrees in the neighbors as the core nodes. Next, we assign the core nodes’ labels to their neighbors according to the similarity between them, which can reduce the randomness of the label propagation algorithm. Then, we arrange the nodes whose labels had not been changed as the new network and find the new core nodes. After that, we update the labels of neighbor nodes according to the similarity between them again until the end of the iteration, to complete the pre-partition. The approach of circularly searching for core nodes increases the diversity of the network partition and prevents the smaller potential communities being swallowed in the process of partition. Then, we implement the label propagation algorithm on the whole network after the pre-partition. Finally, we adopt a modified method based on the degree of membership determined by the bidirectional attraction of nodes and their neighbor communities. This method can reduce the possibility of the error in partition of few nodes. Experiments on artificial and real networks show that the proposed algorithm can accurately divide the network and get higher degree of modularity compared with five existing algorithms.

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