Local Community Detection Algorithm Research Articles

Detecting local communities in complex network via the optimization of interaction relationship between node and community.

The goal of local community detection algorithms is to explore the optimal community with a reference to a given node. Such algorithms typically include two primary processes: seed selection and community expansion. This study develops and tests a novel local community detection algorithm called OIRLCD that is based on the optimization of interaction relationships between nodes and the community. First, we introduce an improved seed selection method to solve the seed deviation problem. Second, this study uses a series of similarity indices to measure the interaction relationship between nodes and community. Third, this study uses a series of algorithms based on different similarity indices, and designs experiments to reveal the role of the similarity index in algorithms based on relationship optimization. The proposed algorithm was compared with five existing local community algorithms in both real-world networks and artificial networks. Experimental results show that the optimization of interaction relationship algorithms based on node similarity can detect communities accurately and efficiently. In addition, a good similarity index can highlight the advantages of the proposed algorithm based on interaction optimization.

Spatial-Aware Local Community Detection Guided by Dominance Relation

The problem of finding the spatial-aware community for a given node has been defined and investigated in geosocial networks. However, existing studies suffer from two limitations: 1) the criteria of defining communities are determined by parameters, which are difficult to set, and 2) algorithms may require global information and are not suitable for situations where the network is incomplete. Therefore, we propose spatial-aware local community detection (SLCD), which finds the spatial-aware local community with only local information and defines the community based on the difference in terms of the sparseness of edges inside and outside the community. Specifically, to address the SLCD problem, we design a novel spatial aware local community detection algorithm based on dominance relation, but this algorithm incurs high cost. To further improve the efficiency, we propose a greedy algorithm. Experimental results demonstrate that the proposed greedy algorithm outperforms the comparison algorithms.

A local community detection algorithm based on potential community exploration

Local community detection aims to detect local communities that have expanded from the given node. Because of the convenience of obtaining the local information of the network and nearly linear time complexity, researchers have proposed many local community detection algorithms to discover the community structure of real-world networks and have obtained excellent results. Most existing local community detection algorithms expand from the given node to a community based on an expansion mechanism that can determine the membership of nodes. However, when determining the membership of neighboring nodes of a community, previous algorithms only considered the impact from the current community, but the impact from the potential communities around the node was neglected. As the name implies, a potential community is a community structure hidden in an unexplored network around a node. This paper gives the definition of potential communities of a node for the first time, that is, a series of connected components consisting of the node’s neighbors that are in the unexplored network. We propose a three-stage local expansion algorithm, named LCDPC, that performs Local Community Detection based on Potential Community exploration. First, we search for a suitable node to replace the given node as the seed by calculating the node importance and the node similarity. Second, we form the initial community by combining the seed and its suitable potential community. Finally, the eligible nodes are selected by comparing the similarities between potential communities and the expanding community and nodes and adding them to the initial community for community expansion. The proposed algorithm is compared with eight state-of-the-art algorithms on both real-world networks and artificial networks, and the experimental results show that the performance of the proposed algorithm is better than that of the comparison algorithms and that the application of potential community exploration can help identify the community structure of networks.

Local Community Detection Algorithm Based on Alternating Strategy of Strong Fusion and Weak Fusion.

Existing fusion-based local community detection algorithms have achieved good results. However, when assigning a node to a community, similarity functions are sometimes used, which only use node information, while ignoring connection information within the community. These algorithms sometimes fail to find influential nodes, which eventually leads to the failure to find a complete local community. To address these problems, a new local community detection algorithm is proposed in this article. Two strategies, of strong fusion followed by weak fusion, are used alternately to fuse nodes. Compared with using two fusion strategies alone, the alternating loop method can improve the solution of the algorithm in each stage. In strong fusion, we propose a new membership function that considers both node information and connection information in the local community. This improves the quality of the fused node while preserving the structure of the current community. In weak fusion, we propose a parameter-based similarity measure, which can detect influential nodes for a local community. We also propose a local community evaluation metric, which does not require true division to determine the optimal local community under different parameters. Experiments, compared to six state-of-the-art algorithms, show that the proposed algorithm improves accuracy and stability, and also demonstrate the effectiveness of the new local community evaluation metrics in parameter selection.

Evaluating community quality based on ground-truth

An effective Community Scoring Function (CSF) is very important since it can properly quantify the community quality of the node groups and help us to effectively discover valuable network communities. Currently, researchers have proposed many types of CSFs. However, none of them are based on an experimental and theoretical analysis of the node groups with different scales and community qualities. This may significantly weaken their effectiveness. Besides, there are few experiments to comprehensively analyze the effectiveness of the existing CSFs. In this paper, we try to make up for these shortcomings.We analyze the node groups with different scales and community qualities in real-world networks, and find effective functions to measure internal and external connection densities of the groups. We then obtain a novel and robust CSF called ECOQUG by effectively combining two kinds of functions that we determined. We design extensive experiments to contrast the performance of ECOQUG with 13 commonly used CSFs. The final experiments show that ECOQUG performs best among them, which demonstrates its reliability. Furthermore, in order to further demonstrate the necessity and significance of an effective CSF, we optimize the TCE algorithm by applying ECOQUG and an improved expansion method, and produce a new Local Community Detection (LCD) algorithm called Max-ECOQUG. We compare Max-ECOQUG with TCE and other better designed LCD algorithms with different CSFs. The results show that the CSF has a greater impact on the performance of LCD algorithms than the design method, and an effective CSF is necessary and significant.

From communities to protein complexes: A local community detection algorithm on PPI networks.

Identifying protein complexes in protein-protein interaction (ppi) networks is often handled as a community detection problem, with algorithms generally relying exclusively on the network topology for discovering a solution. The advancement of experimental techniques on ppi has motivated the generation of many Gene Ontology (go) databases. Incorporating the functionality extracted from go with the topological properties from the underlying ppi network yield a novel approach to identify protein complexes. Additionally, most of the existing algorithms use global measures that operate on the entire network to identify communities. The result of using global metrics are large communities that are often not correlated with the functionality of the proteins. Moreover, ppi network analysis shows that most of the biological functions possibly lie between local neighbours in ppi networks, which are not identifiable with global metrics. In this paper, we propose a local community detection algorithm, (lcda-go), that uniquely exploits information of functionality from go combined with the network topology. lcda-go identifies the community of each protein based on the topological and functional knowledge acquired solely from the local neighbour proteins within the ppi network. Experimental results using the Krogan dataset demonstrate that our algorithm outperforms in most cases state-of-the-art approaches in assessment based on Precision, Sensitivity, and particularly Composite Score. We also deployed lcda, the local-topology based precursor of lcda-go, to compare with a similar state-of-the-art approach that exclusively incorporates topological information of ppi networks for community detection. In addition to the high quality of the results, one main advantage of lcda-go is its low computational time complexity.

Seed-Oriented Local Community Detection Based on Influence Spreading

In recent years, local community detection algorithms have developed rapidly because of their nearly linear computing time and the convenience of obtaining the local information of real-world networks. However, there are still some issues that need to be further studied. First, there is no local community detection algorithm dedicated to detecting a seed-oriented local community, that is, the local community with the seed as the core. The second and third issues are that the quality of local communities detected by the previous local community detection algorithms are largely dependent on the position of the seed and predefined parameters, respectively. To solve the existing problems, we propose a seed-oriented local community detection algorithm, named SOLCD, that is based on influence spreading. First, we propose a novel measure of node influence named k-core centrality that is based on the k-core value of adjacent nodes. Second, we obtain the seed-oriented local community, which is composed of the may-members and the must-member chain of the seed, by detecting the influence scope of the seed. The may-members and the must-members of the seed are determined by judging the influence relationship between the node and the seed. Five state-of-art algorithms are compared to SOLCD on six real-world networks and three groups of artificial networks. The experimental results show that SOLCD can achieve a high-quality seed-oriented local community for various real-world networks and artificial networks with different parameters. In addition, when taking nodes with different influence as seeds, SOLCD can stably obtain high-quality seed-oriented local communities.

Local Community Detection: A Survey

Community detection is a flourishing research field with a plethora of applications ranging from biology to sociology. Local community detection has emerged as a promising subfield of research concerned with community identification around a set of seeding nodes. The practical significance of local community detection is important for numerous real-world applications such as protein interactions and targeted advertising. Since 2005, when the first research paper on local community detection appeared, the literature has been vast and difficult to navigate, as each method works best under certain conditions and assumptions regarding the seed nodes and the identification of their community. For this reason, and motivated by the many real-world applications of local community detection, in this paper we provide a comprehensive overview and taxonomy of local community detection algorithms. There are quite a lot of surveys on community detection that make a compendious reference to local community detection. However, they do not achieve a systematic and comprehensive coverage of this particular field. Since the research area of local community detection is quite extensive, it is necessary to categorize and discuss the various methods, techniques, and assumptions used to address the problem. This survey aims to fill this gap and help researchers get a clear overview of the local community detection problem. To this end, we have also gathered the best documented tools and the most commonly used datasets in the local community detection literature to help researchers identify the tools they can use to prove their methods.

Local community detection based on higher-order structure and edge information

Local community detection is to discover local community where the seed is located. Most algorithms extend local community by edge information, without considering high-order information in network. The high-order information which is also named as network motif is very important for forming a community. There are also methods that focus on higher-order structure but ignore the sparsely connected edges, resulting in that fail to extend some edge points. In addition, when the seed is the edge node, how to choose the first node to integrate into the community will determine whether the community expands in a right direction. Therefore, a local community detection algorithm based on higher-order structure and edge information (HSEI) is proposed. Firstly, different ways selecting the first node joining local community according to the motif degree of seed are used. Secondly, a new motif-based modularity function is proposed to extend local community, so that the extended community will be connected more tightly. A new motif-based community central node is defined to help extend the central part of local community. For the edge of community and the area with sparse connections, edge information is used to mine the membership strength between nodes and communities, so as to obtain more complete local community members. Compared with five state-of-the-art algorithms, the proposed method achieves better results on the generated networks with different parameters and six real networks.

A fast local community detection algorithm in complex networks

A fast local community detection algorithm in complex networks

A new metric to compare local community detection algorithms in social networks using geodesic distance

A new metric to compare local community detection algorithms in social networks using geodesic distance

Gravitational community detection by predicting diameter

Community detection is a pivotal part of network analysis and is classified as an NP-hard problem. In this paper, a novel community detection algorithm is proposed, which probabilistically predicts communities’ diameter using the local information of random seed nodes. The gravitation method is then applied to discover communities surrounding the seed nodes. The individual communities are combined to get the community structure of the whole network. The proposed algorithm, named as Local Gravitational community detection algorithm (LGCDA), can also work with overlapping communities. LGCDA algorithm is evaluated based on quality metrics and ground-truth data by comparing it with some of the widely used community detection algorithms using synthetic and real-world networks.

Local community detection algorithm based on local modularity density

Local community detection algorithm based on local modularity density

Multiple Local Community Detection via High-Quality Seed Identification over Both Static and Dynamic Networks

Local community detection aims to find the communities that a given seed node belongs to. Most existing works on this problem are based on a very strict assumption that the seed node only belongs to a single community, but in real-world networks, nodes are likely to belong to multiple communities. In this paper, we first introduce a novel algorithm, HqsMLCD, that can detect multiple communities for a given seed node over static networks. HqsMLCD first finds the high-quality seeds which can detect better communities than the given seed node with the help of network representation, then expands the high-quality seeds one-by-one to get multiple communities, probably overlapping. Since dynamic networks also act an important role in practice, we extend the static HqsMLCD to handle dynamic networks and introduce HqsDMLCD. HqsDMLCD mainly integrates dynamic network embedding and dynamic local community detection into the static one. Experimental results on real-world networks demonstrate that our new method HqsMLCD outperforms the state-of-the-art multiple local community detection algorithms. And our dynamic method HqsDMLCD gets comparable results with the static method on real-world networks.

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.

LSMD: A fast and robust local community detection starting from low degree nodes in social networks

Community detection is an appropriate approach for discovering and understanding the structure and hidden information in complex networks One of the most critical issues in the community detection problem is the low-time complexity of the algorithm while preserving the accuracy of the algorithm, which is important in large-scale networks such as social networks. Local community detection algorithms try to use local information and provide acceptable results in a reasonable time. This paper proposes a fast and accurate community detection algorithm based on local information for the community’s label assigning. In the proposed algorithm, local community detection is started from low degree nodes by label assigning in a multi-level diffusion way, called LSMD algorithm, with significant low time complexity. In the first phase of the LSMD algorithm, at first, the community’s label is assigned to the node with degree 1 and its direct neighbor and second level neighbors. In fact, we used this fact that people with a smaller number of neighbors are not likely to be connected to diverse communities. Next, a community label is respectively assigned for the nodes with degrees 2, 3, and so forth. In the second phase, initial communities are merged using a new simple and fast strategy as far as possible to form the final communities. Besides, there is no random nature in the algorithm, as well as the adjustable parameter. Therefore, the obtained results show meaningful stability for the LSMD. Experiments are performed on real-world and synthetic networks to evaluate the performance and accuracy of the proposed algorithm. The results show that the proposed algorithm significantly is more accurate than the other state-of-the-art algorithms. In addition, it substantially is faster than other local algorithms such as LPA, LCCD, NIBLPA, DA, RTLCD, Louvain, G-CN, Infomap, and ECES on large-scale networks.

Mining the Local Dependency Itemset in a Products Network

Many studies have been conducted on market basket analysis such as association rules and dependent patterns. These studies mainly focus on mining all significant patterns or patterns directly associated with a given item in a dataset. The problem that has not been addressed is how to mine patterns associated with a given item from the local view. This problem becomes very meaningful when the market basket dataset is huge. To address this problem, in this study, first, a new idea called “local dependency itemset” is put forward, which refers to patterns associated with the given item. Second, a framework of mining the local dependency itemset is presented. The framework has two steps, which are executed iteratively. One is expanding the local dependency itemset that initially consists of only the given item; the other is updating the local products network. Third, this framework is implemented by three different dependence indicators and a typical local community detection algorithm. The experimental results confirm that the local dependency itemset is meaningful.

DLCD-CCE: A Local Community Detection Algorithm for Complex IoT Networks

Internet of Things (IoT) refers to the complex systems generated by the interconnections among widely available objects. Such interactions generate large networks, whose complexity needs to be addressed to provide suitable computationally efficient approaches. In this article, we propose a distributed local community detection algorithm based on specific properties of community center expansions (DLCD-CCE) for large-scale complex networks. The algorithm is evaluated via a prototype system, based on Spark, to verify its accuracy and scalability. The results demonstrate that compared to the typical local community detection algorithms, DLCD-CCE has better accuracy, stability, and scalability, and effectively overcomes the problem that existing algorithms are sensitive to the location of initial seeds.

Local community detection by the nearest nodes with greater centrality

Most community detection algorithms require the global information of the networks. However, for large scale complex networks, the global information is often expensive and even impossible to obtain. Therefore, local community detection is of tremendous significance. In this paper, a new local community detection algorithm based on NGC nodes, named LCDNN, is proposed. For any node, its NGC node refers to the nearest node with greater centrality. In the LCDNN, local community C initially consists of the given node, v. Then, the remaining nodes are added to the local community one by one, and the added node should satisfy: 1) its NGC node is in C, or it is the NGC node of the center node of C; and 2) the fuzzy relation between the node and its NGC node is the largest; 3) the fuzzy relation is no less than half of the average fuzzy relation of the current local network. The experimental results on ten real-world and synthetic networks demonstrate that LCDNN is effective and highly competitive. Concurrently, LCDNN can also be extended for multiscale local community detection, and experimental results are provided to demonstrate its effectiveness.

Supervised local community detection algorithm

Community detection aims to partition a network into internally densely connected groups of nodes. In huge networks, exploring the whole network is computationally very expensive. For applications such as antiterrorism, the spread of disease on networks and viral advertising, researchers are now more interested to find the community surrounding one or a few individuals. In this context, we propose a local community detection algorithm that starts from a seed node and iteratively expands it to reach the community that resembles the most to a real-life community. The expansion process is controlled by a neural network classifier that decides which nodes to add to the community being expanded. This classifier is built using three features quantifying the strength of relationships between nodes and communities. Experiments achieved on Lancichinetti-Fortunato-Radicchi (LFR) synthetic networks and real-world networks from different application domains, proved the high performance of our method as compared to the baselines.