Motif-based embedding label propagation algorithm for community detection

Community structure is an important feature of a complex network, where detection of the community structure can shed some light on the properties of such a complex network. Amongst the proposed community detection methods, the label propagation algorithm (LPA) emerges as an effective detection method due to its time efficiency. Despite this advantage in computational time, the performance of LPA is affected by randomness in the algorithm. A modified LPA, called CLPA-GNR, was proposed recently and it succeeded in handling the randomness issues in the LPA. However, it did not remove the tendency for trivial detection in networks with a weak community structure. In this paper, an improved CLPA-GNR is therefore proposed. In the new algorithm, the unassigned and assigned nodes are updated synchronously while the assigned nodes are updated asynchronously. A similarity score, based on the Sørensen-Dice index, is implemented to detect the initial communities and for breaking ties during the propagation process. Constraints are utilised during the label propagation and community merging processes. The performance of the proposed algorithm is evaluated on various benchmark and real-world networks. We find that it is able to avoid trivial detection while showing substantial improvement in the quality of detection.

Dividing a network into communities has great benefits in understanding the characteristics of the network. The label propagation algorithm (LPA) is a fast and convenient community detection algorithm. However, the community initialization of LPA does not take advantage of topological information of networks, and its robustness is poor. In this paper, we propose a stable community detection algorithm based on density peak clustering and label propagation (DS-LPA). First, the local density calculation method in density peak clustering algorithm is improved in finding the community center of the network, so as to build a suitable initial community, which can improve the quality of community partition. Then, the label update order is determined reasonably by computing the information transmission power of nodes, and the solutions for multiple candidate labels are provided, which greatly improved the robustness of the algorithm. DS-LPA is compared with other seven algorithms on the synthetic network and real-world networks. NMI, ARI, and modularity are used to evaluate these algorithms. It can be concluded that DS-LPA has a higher performance than most comparison algorithms on synthetic network with ten different mixed parameters by statistical testing. And DS-LPA can quickly calculate the best community partition on different sizes of real-world networks.

Complex networks represent various real‐world systems. Overlapping community detection is one of the critical tasks in studying these networks and has significance to a wide variety of applications, including the exploration of online social networks because of the natural attitude of persons to participate in multiple communities at the same time. Despite a large number of existing community detection algorithms for detecting disjoint communities, the efficient and fast uncovering of overlapping communities has remained a challenging problem. To provide an efficient solution, on the one hand, the balanced link density label propagation (BLDLP) algorithm, proposed by the authors of the current study, is a fast, stable, and efficient method for disjoint community detection. On the other hand, the fuzzy theory is a worthwhile approach for overlapping community detection since it provides the membership rate of the overlapping nodes as well as the detection of overlapping communities. Hence, in this paper, based on the synergy of the BLDLP algorithm and the fuzzy theory, a novel method, called fuzzy BLDLP, for overlapping community detection is proposed. Fuzzy BLDLP is fast and efficient. The proposed method needs no prior information about the number of network communities to discover them. The experiments on both synthetic and real‐world known networks, including Zachary, Dolphins, and COVID‐19 Co‐authorship, have revealed that the proposed method successfully detects the overlapping nodes and communities and hence is comparable with the state‐of‐the‐art overlapping community detection algorithms in terms of recall, precision, F‐score and overlapping normalized mutual information.

A cascade information diffusion based label propagation algorithm for community detection in dynamic social networks

Community detection is a complex and meaningful process, which plays an important role in studying the characteristics of complex networks. In recent years, the discovery and analysis of community structures in complex networks has attracted the attention of many scholars, and many community discovery algorithms have been proposed. Many existing algorithms are only suitable for small-scale data, not for large-scale data, so it is necessary to establish a stable and efficient label propagation algorithm to deal with massive data and complex social networks. In this paper, we propose a novel label propagation algorithm, called WRWPLPA (Parallel Label Propagation Algorithm based on Weight and Random Walk). WRWPLPA proposes a new similarity calculation method combining weights and random walks. It uses weights and similarities to update labels in the process of label propagation, improving the accuracy and stability of community detection. First, weight is calculated by combining the neighborhood index and the position index, and the weight is used to distinguish the importance of the nodes in the network. Then, use random walk strategy to describe the similarity between nodes, and the label of nodes are updated by combining the weight and similarity. Finally, parallel propagation is comprehensively proposed to utilize label probability efficiently. Experiment results on artificial network datasets and real network datasets show that our algorithm has improved accuracy and stability compared with other label propagation algorithms.

There have been more and more researches on community discovery in complex networks. Expanding of a source node into a community is one of the most successful methods for local community detection, especially when the global structure of the network is not accessible. In this paper, we propose CLOSE algorithm, Local Community Detection by LOcal Structure Expansion, based on the local expansion technique in the community detection. In CLOSE, we propose a novel connective function to identify a better source node. The node is in the center of a highly connected component of a graph. CLOSE selects a group of nodes instead of a single node to be the seed for the expansion of a local community. In addition, using the neighboring group can identify a suitable community for a hub node. Moreover, the expansion strategy is based on the label propagation technique instead of local community measurements. In experiments, we compare the performance of CLOSE with previous methods both on synthetic networks from the LFR Benchmark and real-world networks. We also examine the merit of the source node selection strategy. Both source node selection and community detection in CLOSE outperform previous algorithms.

A spreading activation-based label propagation algorithm for overlapping community detection in dynamic social networks

Traditional community detection methods in attributed networks (eg, social network) usually disregard abundant node attribute information and only focus on structural information of a graph. Existing community detection methods in attributed networks are mostly applied in the detection of nonoverlapping communities and cannot be directly used to detect the overlapping structures. This article proposes an overlapping community detection algorithm in attributed networks. First, we employ the modified X‐means algorithm to cluster attributes to form different themes. Second, we employ the label propagation algorithm (LPA), which is based on neighborhood network conductance for priority and the rule of theme weight, to detect communities in each theme. Finally, we perform redundant processing to form the final community division. The proposed algorithm improves the X‐means algorithm to avoid the effects of outliers. Problems of LPA such as instability of division and adjacent communities being easily merged can be corrected by prioritizing the node neighborhood network conductance. As the community is detected in the attribute subspace, the algorithm can find overlapping communities. Experimental results on real‐attributed and synthetic‐attributed networks show that the performance of the proposed algorithm is excellent with multiple evaluation metrics.

Perhaps the most recent controversial topic in network science research is to determine whether real-world complex networks are scale-free or not. Recently, Broido and Clauset [A.D. Broido, A. Clauset, Nature Communication, 10, 1017 (2019)] asserted that the degree distributions of real-world networks are rarely power law under statistical tests. Such complex networks, including social, biological, information, temporal, and brain networks, are often heavy-tailed where the assumption on the scale-free nature of real-world heavy-tailed networks become insignificant as the complex system evolves over time. The failure of power law distribution in fitting the degree distribution data is mainly due to the presence of an identifiable non-linearity within the entire degree distribution in a log-log scale of a complex heavy-tailed network. In this study, we attempt to address this issue by proposing a new class of heavy-tailed probability distributions for modeling the entire degree distributions of complex networks. We introduce a new family of generalized Lomax models (GLM) to capture the non-linearity of these heavy-tailed networks. These newly introduced GLM-type distributions provide better fitting and greater flexibility to the entire node degree distribution of complex networks. Several statistical properties of the proposed model, such as extreme value and inferential statistical properties, are derived into this context. Interestingly, the GLM family belongs to the basin of attraction of Frechet distribution, a heavy-tailed extreme value distribution. Rigorous experimental analysis showcases the excellent performance of the proposed family of distributions while fitting the heavy-tailed real-world complex networks over fifty real-world datasets in comparison with benchmark probability models. Our results show that GLM-type distributions are not rare, able to model almost 90% of the tested networks accurately compared to benchmark probability models.

Community detection is a kind of clustering task which aims to find groups of vertices densely connected internally but sparsely connected to other groups. In comparison with conventional clustering methods, community detection methods are able to examine structural, functional and dynamical properties of the networked data, beyond its physical attributes. However, such techniques have been barely explored in the literature as most of the machine learning data sets are represented as non-graph data (e.g., feature vectors, images, texts, and so on). In this work, we propose a simple community detection framework based on the literature that covers since the graph construction process from feature vectors generated from non-graph data until the application and evaluation of community detection methods over such a graph. The framework is further evaluated on the problem of invariant pattern clustering of images, which consists of given a set of image objects taken from different angles, positions or rotations, clustering the images related to each object. Experiments were conducted considering three community detection methods (fast greedy, walk-trap and label propagation) and two relevant clustering methods (k-means and HDBSCAN). The results indicate FG as the better choice among those algorithms as it usually approximates efficiently the ground-truth groups while also keeping a reasonable number of communities. Moreover, our results suggest that community detection may be an efficient task not only to cluster graph data, but also domain applications represented by non-graph data.

We generalize the label propagation algorithm in complex networks to weighted networks by weighting the label propagation rule and the termination condition of label propagation algorithm. Experiments on computer-generated networks and real-world networks are carried out to compare the generalized algorithm with original algorithm. The results show the generalized algorithm is accurate, and the number of iteration is less. The weighed label propagation algorithm is applied to fault classification, and the good classification results are obtained.

Community detection in networks plays a key role in understanding their structures, and the application of clustering algorithms in community detection tasks in complex networks has attracted intensive attention in recent years. In this paper, based on the definition of uncertainty of node community belongings, the node density is proposed first. After that, the DD (the combination of node density and node degree centrality) is proposed for initial node selection in community detection. Finally, based on the DD and k-means clustering algorithm, we proposed a community detection approach, the density-degree centrality-jaccard-k-means method (DDJKM). The DDJKM algorithm can avoid the problem of random selection of initial cluster centers in conventional k-means clustering algorithms, so that isolated nodes will not be selected as initial cluster centers. Additionally, DDJKM can reduce the iteration times in the clustering process and the over-short distances between the initial cluster centers can be avoided by calculating the node similarity. The proposed method is compared with state-of-the-art algorithms on synthetic networks and real-world networks. The experimental results show the effectiveness of the proposed method in accurately describing the community. The results also show that the DDJKM is practical a approach for the detection of communities with large network datasets.

Community detection in complex networks is an important multidisciplinary research area and is considered crucial for understanding the structure of complex networks. Unsupervised deep learning models (e.g. stack autoencoders) have been successfully proposed for the problem of community detection, which can extract network features and use them in splitting the network into communities. Despite their effectiveness, these methods are not very efficient, especially when large networks are involved. Furthermore, existing models usually handle the network as a single object, which means that massive trainable parameters are required during training (which in normal complex networks often reach millions of parameters, not to mention large networks), thereby increasing the complexity of the model. To overcome these problems, this paper proposes a deep autoencoder method for network community detection based on three techniques: network-data partitioning, reduction and sharing of trainable parameters, which contribute significantly to improve the efficiency of the method. A new partitioning strategy is imposed on complex networks at different levels. The paper also proposes a parallel design for the proposed method. Furthermore, a new similarity constraint function is proposed to improve and preserve the performance of community detection task. We performed extensive experiments for different partitioning levels of a network-dataset to evaluate the method with CPU and GPU devices. The results showed that the proposed method significantly improved training speed and efficiency while maintaining performance. The results also showed that the efficiency of the method increases as we move to a deeper level of partitioning.

The objective of a community detection algorithm is to group similar nodes that are more connected to each other than with the rest of the network. Several methods have been proposed but many are of high complexity and require global knowledge of the network, which makes them less suitable for large-scale networks. The Label Propagation Algorithm initially assigns a distinct label to each node that iteratively updates its label with the one of the majority of its neighbors, until consensus is reached among all nodes in the network. Nodes sharing the same label are then grouped into communities. It runs in near linear time and is decentralized, but it gets easily stuck in local optima and often returns a single giant community. To overcome these problems we propose MemLPA, a variation of the classical Label Propagation Algorithm where each node implements a memory mechanism that allows them to “remember” about past states of the network and uses a decision rule that takes this information into account. We demonstrate through extensive experiments, on the Lancichinetti-Fortunato-Radicchi benchmark and a set of real-world networks, that MemLPA outperforms other existing label propagation algorithms that implement memory and some of the well-known community detection algorithms. We also perform a topological analysis to extend the performance study and compare the topological properties of the communities found to the ground-truth community structure.

Community discovery algorithm is one of the important topics in complex network research. However, there exist some problems in the traditional community discovery algorithm, such as the oscillation of label propagation, the convergence of iteration, the formation of a large community with a single label, namely “monster community”, or the poor effect of community discovery due to equal treatment of nodes. On the other hand, with the advent of the era of data, the computing power of single computer can't meet the demand of the rapid growth of complex network scale. Based on the above knowledge, this paper proposes the research and improvement of community discovery algorithm based on spark for large-scale complex networks. This paper first weights a complex network with no weight. Then, this paper chooses the classic efficient community discovery algorithm - label propagation algorithm to optimize label initialization, label propagation and label update strategy, iterative convergence strategy and so on, and establishes a new community discovery algorithm model. Then, the algorithm is connected to Spark, the algorithm is synchronized through GraphX programming, and a Spark experiment platform is established. Finally, some classic complex network data and some large-scale complex network data are tested and compared with some classic community discovery algorithms to verify the proposed algorithm is validated and verified by a large-scale complex network data set based on Spark GraphX platform does greatly improve the computational performance of community discovery in complex networks.