Multi-label Propagation Algorithm Research Articles

A Multi-label Propagation Algorithm for Community Detection Based on Average Mutual Information

Community structure is one of the vital characteristics of complex networks. How to effectively detect communities is a hot issue. From the perspective of information theory, the community structure of complex networks can be detected and revealed more accurately. This research introduces the average mutual information (AMI) into the detection process of the multi-label propagation algorithm (MLPA) and proposes a new community detection algorithm AMI-MLPA. The algorithm initially determines the propagation order according to the influence of nodes in the network. By selecting the label with stronger propagation intensity and smaller conditional entropy in the process of label propagation, a more reasonable community partition can be obtained. Experiments on real-world datasets and synthetic datasets show that the algorithm is better than FastGN, GN, and other LPA-based algorithms in general, with high accuracy of results on large-scale synthetic networks, which verifies the effectiveness of the algorithm.

A Multi-Label Propagation Algorithm With the Double-Layer Filtering Strategy for Overlapping Community Detection

Overlapping community structures are universal in complex networks. Multi-label propagation approach, as a method for detecting overlapping community, has been received widespread attention for the near linear time complexity. In recent years, some algorithms based on multi-label propagation approach have been proposed, but there is still room to improve the accuracy of these algorithms to detect overlapping communities. In this paper, a double-layer filtering strategy is proposed to help nodes filter labels, and the node centrality of each node according to the number of its neighbors and the number of edges between its neighbors is used to fix the node update sequence. Then based on the double-layer filtering strategy, the new fixed node update sequence and the previous multi-label propagation algorithm, a new algorithm called MLPA-DF is proposed. Theory analysis shows that MLPA-DF still has the near linear time complexity. And experimental results on the synthetic and real-world networks show that MLPA-DF has better performance than previous multi-label propagation algorithms (COPRA, SLPA and LPANNI) in terms of the accuracy of detecting overlapping communities without reducing stability.

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

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.

Research on Multi-label Propagation Algorithm for Community Detection Based on Weibo

Community structure in social network has an overlapping property from where people can find a lot of valuable information by community detection. It will help users to make accurate and valuable judgments as well as decisions. However, the increasing amount of information has led to difficulties for traditional technology to meet the requirement of big data processing. In order to effectively process data in large-scale network, a multi-label propagation algorithm for community detection has been implemented by using BSP parallel model. This algorithm combines the idea of LPA and COPRA. It finds out communities with active Weibo users, and then uses these communities as labels to perform label propagation. In the end, it classifies the remaining users’ communities. The algorithm solves LPA’s problems of instability and low accuracy. Its effectiveness and efficiency were tested and verified by using data from Weibo network.

Parallel Multi-Label Propagation Based on Influence Model and Its Application to Overlapping Community Discovery

Finding communities in networks is one of the challenging issues in complex network research. We have to deal with very large networks that contain billions of vertices, which makes community discovery a computationally intensive work. Moreover, communities usually overlap each other, which greatly increases the difficulty of identifying the boundaries of communities. In this paper, we propose a parallel multi-label propagation algorithm (PMLPA) that enhances traditional multi-label propagation algorithm (MLPA) in two ways. First, the critical steps of MLPA are parallelized based on the MapReduce model to get higher scalability. Second, new label updating strategy is used to automatically determine the most valuable labels of each vertex. Furthermore, we study the improvement of PMLPA through considering the influence of vertices and labels on label updating. In this way, the importance of each label can be described with higher precision. Experiments on artificial and real networks prove that the proposed algorithms can achieve both high discovering accuracy and high scalability.

Disease-related gene module detection based on a multi-label propagation clustering algorithm.

Detecting disease-related gene modules by analyzing gene expression data is of great significance. It is helpful for exploratory analysis of the interaction mechanisms of genes under complex disease phenotypes. The multi-label propagation algorithm (MLPA) has been widely used in module detection for its fast and easy implementation. The accuracy of MLPA greatly depends on the connections between nodes, and most existing research focuses on measuring the similarity between nodes. However, MLPA does not perform well with loose connections between disease-related genes. Moreover, the biological significance of modules obtained by MLPA has not been demonstrated. To solve these problems, we designed a double label propagation clustering algorithm (DLPCA) based on MLPA to study Huntington’s disease. In DLPCA, in addition to category labels, we introduced pathogenic labels to supervise the process of multi-label propagation clustering. The pathogenic labels contain pathogenic information about disease genes and the hierarchical structure of gene expression data. Experimental results demonstrated the superior performance of DLPCA compared with other conventional gene-clustering algorithms.

Relational large scale multi-label classification method for video categorization

The problem of automated video categorization in large datasets is considered in the paper. A new Iterative Multi-label Propagation (IMP) algorithm for relational learning in multi-label data is proposed. Based on the information of the already categorized videos and their relations to other videos, the system assigns suitable categories—multiple labels to the unknown videos. The MapReduce approach to the IMP algorithm described in the paper enables processing of large datasets in parallel computing. The experiments carried out on 5-million videos dataset revealed the good efficiency of the multi-label classification for videos categorization. They have additionally shown that classification of all unknown videos required only several parallel iterations.

Balanced Multi-Label Propagation for Overlapping Community Detection in Social Networks

In this paper, we propose a balanced multi-label propagation algorithm (BMLPA) for overlapping community detection in social networks. As well as its fast speed, another important advantage of our method is good stability, which other multi-label propagation algorithms, such as COPRA, lack. In BMLPA, we propose a new update strategy, which requires that community identifiers of one vertex should have balanced belonging coefficients. The advantage of this strategy is that it allows vertices to belong to any number of communities without a global limit on the largest number of community memberships, which is needed for COPRA. Also, we propose a fast method to generate “rough cores”, which can be used to initialize labels for multi-label propagation algorithms, and are able to improve the quality and stability of results. Experimental results on synthetic and real social networks show that BMLPA is very efficient and effective for uncovering overlapping communities.