Extremal optimization-based semi-supervised algorithm with conflict pairwise constraints for community detection

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

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

Community detection has gained significant research interest within the data mining field. It involves identifying subsets of nodes with dense internal connections and sparse external connections. Most studies on community detection focus solely on identifying non-overlapping communities in a static graph. However, in practice, communities often overlap, and the structure of the graphs is dynamically evolving. This dynamic nature leads to community changes and poses a significant challenge in detecting overlapping communities on temporal graphs (T-OCD). While graph neural networks have shown great performance in generating node representations for community detection, learning representations that capture temporal graph structures and support overlapping community detection remain an open question. To address these challenges, we present T-OCDIB , a novel approach for T emporal O verlapping C ommunity D etection guided by I nformation B ottleneck. Specifically, we first propose an overlapping community detection approach for static graphs, under the guidance of a community-oriented information bottleneck. This approach allows us to learn discriminative node representations specific to each community, facilitating the detection of overlapping communities. Following this, we extend this method to temporal graphs by presenting a temporal convolution module. This module uses adaptive weight matrices based on evolving graph structures to capture temporal dependencies for community detection. Additionally, to promote smooth transitions between consecutive communities, we introduce a temporal smoothing module to further constrain changes in community structure. We evaluate the proposed approach on both real-world and synthetic temporal networks. Experimental results illustrate the superiority of T-OCDIB over other community detection methods.

Community detection in complex networks has attracted a lot of attentions in recent years. Compared with the traditional single-objective community detection approaches, the multi-objective approaches based on evolutionary computation can provide a decision maker with more flexible and promising solutions. How to make effective use of the optimal solution set returned by the multi-objective community detection approaches is an important yet unsolved issue. Through leveraging an existing multi-objective community detection algorithm, this paper proposes four model selection methods to aid the decision makers to select the preferable community structures. The experiments with three synthetic and real social networks illustrate that the proposed method can discover more authentic and comprehensive community structures than traditional single-objective approaches.

Community structure is found everywhere from simple networks to real world complex networks. The problem of community detection is to predict clusters of nodes that are densely connected among themselves. The task of community detection has a wide variety of applications ranging from recommendation systems, advertising, marketing, epidemic spreading, cancer detection etc. The two mainly existing approaches for community detection, namely, stochastic block model and modularity maximization model focus on building a low dimensional network embedding to reconstruct the original network structure. However the mapping to low dimensional space in these methods is purely linear. Understanding the fact that real world networks contain non-linear structures in abundance, aforementioned methods become less practical for real world networks. Considering the nonlinear representation power of deep neural networks, several solutions based on autoencoders are being proposed in the recent literature. In this work, we propose a deep neural network architecture for community detection wherein we stack multiple autoencoders and apply parameter sharing. This method of training autoencoders has been successfully applied for the problems of link prediction and node classification in the literature. Our enhanced model with modified architecture produced better results compared to many other existing methods. We tested our model on a few benchmark datasets and obtained competitive results.

Research on community structures promotes the discovery of the relationship between network structure and functionality, while community detection is the foundation and core of community structure research. In this study, a community division algorithm is proposed based on a hierarchical division; a modified Jaccard similarity coeffcient is employed to detect the edges between the nodes; the network is decomposed by deleting edges between nodes to detect community structures within a network. According to the experiments on the datasets of artificial networks and real networks, this algorithm can yield accurate and meaningful community structure without prior information, of which the accuracy exceeds or approaches to that of classic community detection algorithms. In addition, compared with the classic GN splitting algorithm, the proposed algorithm produces a division of community structures that is consistent with that of GN algorithm, with a significantly improved time performance.

Community detection methods that are based entirely on the topology of the network do not always achieve higher accuracy. This implies that the topological information alone is insufficient to accurately uncover the community structures of networks. Recently, some methods were proposed that used prior information to improve the performance and accuracy of community detection. However, most of these methods have high time consumption and are not suitable for dealing with large-scale networks. In this paper, we propose a fast semi-supervised community detection method called SemiAttractor that integrates the prior information into the distance dynamics model. Experimental results from both artificial and real-world networks show that the proposed method can effectively improve the accuracy of community detection and reduce the time costs.

Identification of semantic community structures is important for understanding the interactions and sentiments of different groups of people and predicting the social emotion. A robust community detection method needs to autonomously determine the number of communities and community structure for a given network. Nonnegative matrix factorization (NMF), a component decomposition approach for latent sentiment discovery, has been extensively used for community detection. However, the existing NMF-based methods require the number of communities to be determined a priori , limiting their applicability in practice of affective computing. Here, we develop a novel NMF-based method to autonomously determine the number of semantic communities and community structure simultaneously. In our method, we use an initial number of semantic communities, larger than the actual number, in the NMF formulation, and then suppress some of the communities by introducing an adaptively weighted group-sparse low-rank regularization to derive the target number of communities and at the same time the corresponding community structure. Our method not only maintains the efficiency without increasing the complexity compared to the original NMF method but also can be straightforwardly extended to handle the non-network data. We thoroughly examine the new method, showing its superior performance over several competing methods on synthetic and large real-world social networks.

Subspace methods have been successfully applied to face recognition tasks. It is well-studied in both unsupervised learning and supervised learning, such as Eigenface and Fisherface. In practice, besides abundant unlabeled examples, domain knowledge in the form of pairwise constraints is commonly available, which specifies whether a pair of instances belong to the same class or different classes. In this study, we propose a face recognition method based on semi-supervised locality preserving learning together with pairwise constraints and unlabeled data, called Semi-supervised Laplacianface (S-Laplacianface). It tries to preserve the local geometric structure of the face manifold as Laplacianface, also requires the subspace to satisfy the pairwise constraints defined by the user. Experimental results on two face databases demonstrate the effectiveness of proposed algorithm.

Community detection in complex networks via adapted Kuramoto dynamics

Community detection in network-type data provides a powerful tool in analyzing and understanding real-world systems. In fact, community detection approaches aim to reduce the network’s dimensionality and partition it into a set of disjoint clusters or communities. However, real networks are usually corrupted with noise or outliers which affect the detected community structure quality. In this paper, a new robust community detection algorithm that is capable of recovering a clean or a smoothed version of the corrupted graph and detecting the correct community structure is introduced. The proposed approach combines robust principal component analysis (RPCA) and symmetric nonnegative matrix factorization (SymNMF) in a single optimization problem. The proposed problem is solved under the framework of alternating direction methods of multipliers (ADMM). In particular, the corrupted adjacency matrix is decomposed into a low-rank and sparse components using RPCA and the community structure is detected by applying SymNMF to the extracted low-rank component. Extensive experiments that have been conducted on real and simulated binary and weighted networks show that the proposed approach significantly outperforms existing algorithms in detecting the correct community structure even in grossly corrupted networks.

To unravel the driving patterns of networks, the most popular models rely on community detection algorithms. However, these approaches are generally unable to reproduce the structural features of the network. Therefore, attempts are always made to develop models that incorporate these network properties beside the community structure. In this article, we present a probabilistic generative model and an efficient algorithm to both perform community detection and capture reciprocity in networks. Our approach jointly models pairs of edges with exact two-edge joint distributions. In addition, it provides closed-form analytical expressions for both marginal and conditional distributions. We validate our model on synthetic data in recovering communities, edge prediction tasks and generating synthetic networks that replicate the reciprocity values observed in real networks. We also highlight these findings on two real datasets that are relevant for social scientists and behavioural ecologists. Our method overcomes the limitations of both standard algorithms and recent models that incorporate reciprocity through a pseudo-likelihood approximation. The inference of the model parameters is implemented by the efficient and scalable expectation–maximization algorithm, as it exploits the sparsity of the dataset. We provide an open-source implementation of the code online.

Community Detection method based on Random walk and Multi objective Evolutionary algorithm in complex networks

With the rapid development of computer technology, the research on complex networks has attracted more and more attention. At present, the research directions of cloud computing, big data, internet of vehicles, and distributed systems with very high attention are all based on complex networks. Community structure detection is a very important and meaningful research hotspot in complex networks. It is a difficult task to quickly and accurately divide the community structure and run it on large-scale networks. In this paper, we put forward a new community detection approach based on internode attraction, named IACD. This algorithm starts from the perspective of the important nodes of the complex network and refers to the gravitational relationship between two objects in physics to represent the forces between nodes in the network dataset, and then perform community detection. Through experiments on a large number of real-world datasets and synthetic networks, it is shown that the IACD algorithm can quickly and accurately divide the community structure, and it is superior to some classic algorithms and recently proposed algorithms.

Adaptive community detection incorporating topology and content in social networks✰