Local Community Detection Methods Research Articles

Uncovering local community structure on line graph through degree centrality and expansion

Local community detection algorithms are an important type of overlapping community detection methods. Local community detection methods identify local community structure through searching seeds and expansion process. In this paper, we propose a novel local community detection method on line graph through degree centrality and expansion (LCDDCE). We firstly employ line graph model to transfer edges into nodes of a new graph. Secondly, we evaluate edges relationship through a novel node similarity method on line graph. Thirdly, we introduce local community detection framework to identify local node community structure of line graph, combined with degree centrality and PageRank algorithm. Finally, we transfer them back into original graph. The experimental results on three classical benchmarks show that our LCDDCE method achieves a higher performance on normalized mutual information metric with other typical methods.

Social collaborative filtering using local dynamic overlapping community detection

Recommender systems play an important role in dealing with the problems caused by the great and growing amount of information, and the collaborative filtering method can propose high-quality suggestions through using other individuals’ opinions. In real world and with the changing nature of individuals’ preferences, recommender systems are not only responsible for fulfilling the users’ interests, but also for modeling their dynamic behaviors. On the other hand, social networks provide new types of data that contribute to personalization and improvement of the performance of the recommender systems. In this paper, we propose a dynamic collaborating filtering-based social recommender system using a dynamic, local, and an overlapping community detection approach. In this study, in addition to the temporal users’ rating data for items, temporal friendship relations among users in social network are also considered and a local community detection method is combined with social recommendation technique in order to improve scalability, sparsity, and cold start issues of collaborative filtering. The proposed method is compared with a number of state-of-the-art recommendation methods. The experimental results on benchmark datasets show that the proposed method outperforms the compared methods based on different evaluation metrics. It has fewer errors (at least 5%) and higher accuracy (at least 12%) than pervious methods in sparse rating matrix and dynamic environment.

Overlapping community detection by constrained personalized PageRank

Given a network, local community detection (a.k.a. graph clustering) methods aim at finding communities around the selected initial nodes (also referred to as seeds, starting nodes or core nodes). Methods in this kind successfully address the efficiency problem confronted by global clustering methods. And techniques, such as personalized PageRank and heat kernel diffusion, for ranking the proximity score of vertices nearby with respect to the corresponding starting nodes are developed. However, most of the random-walk based metrics allow a walker to diffuse without any constraint, and the walker can easily run into irrelevant communities. As a result, the corresponding community could include irrelevant high-quality communities (communities with good fitness score) nearby, we refer to the case that a walker goes into irrelevant communities and causes inaccurate expansion of a community as redundant diffusion. In this work, we develop a constrained personalized PageRank method for community expansion to reduce the problem of redundant diffusion. In the mechanism, a walker moves with lower probability to neighbor nodes already in the existing communities, and a walker tends to walk out of the community if the walker walks into an irrelevant community. Extensive experiments on synthetic and large real-world networks demonstrate that the proposed method outperforms approaches in the state of the art by a large margin in accuracy and efficiency.

A three-stage algorithm for local community detection based on the high node importance ranking in social networks

Community detection aims to discover and reveal community structures in complex networks. Some community detection method is called local methods that only apply local information in discovering steps. Local community detection methods are actually an attempt to increase efficiency in large-scale networks. Most of local community detection methods concentrate on finding the important nodes as initial communities. The quality of the detected communities fundamentally depends on the selected important nodes as community cores. Most of the existing works have disadvantages such as low accuracy, weak scalable, and instability in outcomes that makes the algorithm to detect different communities in each run. In order to solve these problems, this paper proposes a novel local community detection based on high importance nodes Ranking (LCDR). In the proposed algorithm, a new index for computing node importance is presented. With regards to the network locality, the proposed index can fully reflect the node importance of all nodes in the network. LCDR method initially selects important nodes to expand the initial communities based on a local similarity criterion until all nodes become members of one of the communities. Finally, it merges the discovered communities to form final community structures. Experiments on real and synthetic networks show that LCDR can significantly improve the accuracy of communities. Correspondingly, it is promising in different settings based on accuracy and modularity with near-linear time complexity.

Sparse Nonnegative Matrix Factorization for Multiple-Local-Community Detection

Local community detection consists of finding a group of nodes closely related to the seeds, a small set of nodes of interest. Such groups of nodes are densely connected or have a high probability of being connected internally than their connections to other clusters in the network. Existing local community detection methods focus on finding either one local community that all seeds are most likely to be in or finding a single community for each of the seeds. However, a seed member usually belongs to multiple local overlapping communities. In this work, we present a novel method of detecting multiple local communities to which a single-seed member belongs. The proposed method consists of three key steps: 1) local sampling with Personalized PageRank (PPR); 2) using the sparseness generated by a sparse nonnegative matrix factorization (SNMF) to estimate the number of communities in the sampled subgraph; and 3) using SNMF soft community membership vectors to assign nodes to communities. The proposed method shows favorable accuracy performance and a good conductance compared with state-of-the-art community detection methods by experiments using a combination of artificial and real-world networks.

Finding and tracking local communities by approximating derivatives in networks

Since various complex systems are represented by networks, detecting and tracking local communities has become a crucial task nowadays. Local community detection methods are getting much attention because they can address large networks. One famous class of local community detection is to find communities around a seed node. In this research, a novel local community detection method, inspired by geometric active contours, is proposed for finding a community surrounding an initial seed. While most of real world networks are dynamic and the majority of local community detection cannot tackle dynamic networks, the proposed model has the ability to track a local community in a dynamic network. The proposed model introduces and uses the derivative-based concepts curvature and gradient of the boundary of a connected sub-graph in networks. Then, a velocity function based on curvature and gradient is proposed to determine if the boundary of a community should evolve to include a neighbouring candidate. Approximating derivatives in discrete Euclidean space has a long history. However, compared to Euclidean space, graphs follow a non-uniform space in which the dimensionality, given by the the fluctuation in degrees of nodes, fluctuates from one node to another. This complexity complicates the approximation of derivatives which are needed for defining the curvature and gradient of a node in the boundary of a community. A new framework to approximate derivatives in graphs is proposed for such a purpose. For finding local communities, benchmarking our method against two recent methods indicates that it is capable of finding communities with equal or better conductance; and, for tracking dynamic local communities, benchmarking of the proposed method against ground-truth dataset shows a noticeable level of accuracy.

Local community detection based on network motifs

Local community detection aims to find a cluster of nodes by exploring a small region of the network. Local community detection methods are faster than traditional global community detection methods because their runtime does not depend on the size of the entire network. However, most existing methods do not take the higher-order connectivity patterns crucial to the network into consideration. In this paper, we develop a new Local Community Detection method based on network Motif (LCD-Motif) which incorporates the higher-order network information. LCD-Motif adopts the local expansion of a seed set to identify the local community with minimal motif conductance, representing a generalization of the conductance metric for network motifs. In contrast to PageRank-like diffusion methods, LCD-Motif finds the community by seeking a sparse vector in the span of the local spectra, such that the seeds are in its support vector. We evaluate our approach using real-world datasets across various domains and synthetic networks. The experimental results show that LCD-Motif can achieve a higher performance than state-of-the-art methods.

Memory-based random walk for multi-query local community detection

Local community detection, which aims to find a target community containing a set of query nodes, has recently drawn intense research interest. The existing local community detection methods usually assume all query nodes are from the same community and only find a single target community. This is a strict requirement and does not allow much flexibility. In many real-world applications, however, we may not have any prior knowledge about the community memberships of the query nodes, and different query nodes may be from different communities. To address this limitation of the existing methods, we propose a novel memory-based random walk method, MRW, that can simultaneously identify multiple target local communities to which the query nodes belong. In MRW, each query node is associated with a random walker. Different from commonly used memoryless random walk models, MRW records the entire visiting history of each walker. The visiting histories of walkers can help unravel whether they are from the same community or not. Intuitively, walkers with similar visiting histories are more likely to be in the same community. Moreover, MRW allows walkers with similar visiting histories to reinforce each other so that they can better capture the community structure instead of being biased to the query nodes. We provide rigorous theoretical foundations for the proposed method and develop efficient algorithms to identify multiple target local communities simultaneously. Comprehensive experimental evaluations on a variety of real-world datasets demonstrate the effectiveness and efficiency of the proposed method.

A Novel Local Community Detection Method Using Evolutionary Computation.

The local community detection is a significant branch of the community detection problems. It aims at finding the local community to which a given starting node belongs. The local community detection plays an important role in analyzing the complex networks and recently has drawn much attention from the researchers. In the past few years, several local community detection algorithms have been proposed. However, the previous methods only make use of the limited local information of networks but overlook the other valuable information. In this article, we propose an evolutionary computation-based algorithm called evolutionary-based local community detection (ELCD) algorithm to detect local communities in the complex networks by taking advantages of the entire obtained information. The performance of the proposed algorithm is evaluated on both synthetic and real-world benchmark networks. The experimental results show that the proposed algorithm has a superior performance compared with the state-of-the-art local community detection methods. Furthermore, we test the proposed algorithm on incomplete real-world networks to show its effectiveness on the networks whose global information cannot be obtained.

Research on Community Center-metric and Community Detection Algorithm for Complex Networks

To resolved the present-existing problem that local community detection is great sensitive to the initial node position in large-scale network, a new community detection algorithm CCMA (Community Central Metric Algorithm) by using the community central node-set as the initial node is proposed. Firstly, community centrality node-set (LC ≥ 0) is determined by using Leverage Centrality (LC). Then, starting from the node with the largest central index, we expand the community according to the community fitness function until completing the acquisition of the entire local community. And community’s numbers does not need to be given. Experiments on existing real networks and artificial networks show that our community central metric algorithm can detect local community structures more efficiently and accurately by comparing the method of expanding community’s central node with the existing best local community detection method.

Multiscale Local Community Detection in Social Networks

In real-world social networks, global information (e.g., the number of nodes and the connections between them) is incomplete or expensive to acquire; therefore, local community detection becomes especially important. Local community detection is used to identify the local community to which the given starting node belongs according to local information. For a given node, most existing local community detection methods can only find single scale local communities but not those of variable sizes. However, local communities with different scales are often required. Therefore, it is necessary and meaningful to find local communities of the given starting node with different scales; we call this multiscale local community detection. In this paper, we propose a new local modularity inspired by the global modularity and prove the equivalence of the proposed local modularity with two other typical local modularities. Furthermore, to detect local communities with different scales, we present a method based on the proposed local modularity. We test this method on several synthetic and real datasets, and the experimental results indicate that the detected community is meaningful and its scale can be changed reasonably.

The multi-walker chain and its application in local community detection

Local community detection (or local clustering) is of fundamental importance in large network analysis. Random walk-based methods have been routinely used in this task. Most existing random walk methods are based on the single-walker model. However, without any guidance, a single walker may not be adequate to effectively capture the local cluster. In this paper, we study a multi-walker chain (MWC) model, which allows multiple walkers to explore the network. Each walker is influenced (or pulled back) by all other walkers when deciding the next steps. This helps the walkers to stay as a group and within the cluster. We introduce two measures based on the mean and standard deviation of the visiting probabilities of the walkers. These measures not only can accurately identify the local cluster, but also help detect the cluster center and boundary, which cannot be achieved by the existing single-walker methods. We provide rigorous theoretical foundation for MWC and devise efficient algorithms to compute it. Extensive experimental results on a variety of real-world and synthetic networks demonstrate that MWC outperforms the state-of-the-art local community detection methods by a large margin.

Local Community Detection With the Dynamic Membership Function

Most of the community detection methods require the global information of the original network to be available, however, it is often expensive (even no way) to obtain the global information of the network in many real-world networks. So, the local community detection, only based on the local information, becomes especially important. The local community is the community in the network to which a given starting node belongs. Some local community detection methods have been proposed. However, these methods did not consider the characteristics of the local community during the local community formation. In this paper, we analyze the formation of the local community and propose two local community detection algorithms based on the dynamic membership function. Each of the algorithms is divided into three stages: 1) the initial stage, 2) the middle stage, and 3) the closing stage. At the initial stage, we design a dynamical membership function to detect local community and nodes with the greatest neighborhood intersect rate could be added to the local community. At the middle stage, we design another dynamical membership function, and the goal of this stage is to make the connection of the node in the local community closest. At the closing stage, the third dynamical membership function is provided, and the local community is further improved by collecting some nodes that should not be omitted. We test our algorithms on several synthetic datasets and real datasets; the results show that the local communities detected by our method are closer to the real local communities.

Generative models for local network community detection.

Local network community detection aims to find a single community in a large network, while inspecting only a small part of that network around a given seed node. This is much cheaper than finding all communities in a network. Most methods for local community detection are formulated as ad hoc optimization problems. In this paper, we instead start from a generative model for networks with a community structure. By assuming that the network is uniform, we can approximate the structure of the unobserved parts of the network to obtain a method for local community detection. We apply this local approximation technique to two variants of the stochastic block model. This results in local community detection methods based on probabilistic models. Interestingly, in the limit, one of the proposed approximations corresponds to conductance, a popular metric in this field. Experiments on real and synthetic data sets show comparable or improved results compared to state-of-the-art local community detection algorithms.