Searching overlapping communities for group query

Shan, Jing; Shen, Derong; Nie, Tiezheng; Kou, Yue; Yu, Ge

doi:10.1007/s11280-015-0378-5

Cited by 14 publications

(3 citation statements)

References 25 publications

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“…different query vertices simultaneously. Another method is based on capturing cohesiveness structures [17] such as k-truss [18]- [20], k-core [21]- [23] and k-clique [24], [25]. To deal with the changes of network data over time, Takaffoli et al [26] explored local community mining in the dynamic social network by extending L-metric [27] to an incremental version.…”

Section: F Effectiveness Evaluation Of Query Processingmentioning

confidence: 99%

Reliable Community Search in Dynamic Networks

Tang¹,

Li²,

Haldar³

et al. 2022

Preprint

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Local community search is an important research topic to support complex network data analysis in various scenarios like social networks, collaboration networks, and cellular networks. The evolution of networks over time has motivated several recent studies to identify local communities from dynamic networks. However, they only utilized the aggregation of disjoint structural information to measure the quality of communities, which ignores the reliability of communities in a continuous time interval. To fill this research gap, we propose a novel (θ, k)core reliable community (CRC) model in the weighted dynamic networks, and define the problem of the most reliable community search that couples the desirable properties of connection strength, cohesive structure continuity, and the maximal member engagement. To solve this problem, we first develop an online CRC search algorithm by proposing a definition of eligible edge set and deriving the eligible edge set based pruning rules. After that, we devise a Weighted Core Forest-Index and index-based dynamic programming CRC search algorithm, which can prune a large number of insignificant intermediate results according to the maintained weight and structure information in the index, as well as the proposed upper bound properties. Finally, we conduct extensive experiments to verify the efficiency of our proposed algorithms and the effectiveness of our proposed community model on eight real datasets under different parameter settings.

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Section: F Effectiveness Evaluation Of Query Processingmentioning

confidence: 99%

Reliable Community Search in Dynamic Networks

Tang¹,

Li²,

Haldar³

et al. 2022

Preprint

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“…Local community detection is one of the cutting-edge problems in graph mining. In a single network, local search based method [23,28], flow-based method [29], and subgraph cohesiveness-optimization based methods, such as k-clique [24,36], k-truss [9,11], k-core [3] are developed to detect local communities.…”

Section: Related Workmentioning

confidence: 99%

Local Community Detection in Multiple Networks

Luo

Bian

Yan

et al. 2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Local community detection aims to find a set of densely-connected nodes containing given query nodes. Most existing local community detection methods are designed for a single network. However, a single network can be noisy and incomplete. Multiple networks are more informative in real-world applications. There are multiple types of nodes and multiple types of node proximities. Complementary information from different networks helps to improve detection accuracy. In this paper, we propose a novel RWM (Random W alk in Multiple networks) model to find relevant local communities in all networks for a given query node set from one network. RWM sends a random walker in each network to obtain the local proximity w.r.t. the query nodes (i.e., node visiting probabilities). Walkers with similar visiting probabilities reinforce each other. They restrict the probability propagation around the query nodes to identify relevant subgraphs in each network and disregard irrelevant parts. We provide rigorous theoretical foundations for RWM and develop two speeding-up strategies with performance guarantees. Comprehensive experiments are conducted on synthetic and real-world datasets to evaluate the effectiveness and efficiency of RWM.

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“…Finding community structures [7], generic observations of networks [8], spectral clustering for community detection [9], effective community detection process [10], robust local community finding [11], identification of overlapping communities [12], triangle driven detection of communities [13], time series based clustering [14], search process for overlapping communities [15], approximate closest community [16], finding community structure [17], subspace based approach [18], detecting overlapping community using seed expansion [19] and network clustering and modularity [20] are other related researches found in the literature. From the literature it is understood that there is need for fast and parallel discovery of communities which is realized in this paper.…”

Section: Finding and Evaluating Community Structure In Networkmentioning

confidence: 99%

Large Scale Community Discovery of Complex Networks Based On Approximate Optimization

Madhuravani¹

2019

IJETER

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Study of structural features associated with complex networks has its utility. In the process a key role is played by community. Online networks are showing unprecedented growth in complexity. In this context, the traditional approach used to discovery community is not sufficient to deal with large networks. Thus it leads to the problem of inability to discovery communities efficiently in the large scale networks. To overcome this problem, a Parallel Community Discovery (PCD) algorithm is proposed based on the concept of approximate optimization. The algorithm has two parts. They are known as mountain model and landslide algorithm. The former is meant for providing approximate selection of nodes that are used for representation in graph theory. It shows users and the data posted by them in the network. The landslide algorithm on the other hand updates the modularity from time to time incrementally based on the aforementioned technique named approximate optimization. Thus the proposed methodology helps in discovering communities in parallel. The process of clustering on the modularity plays important role in mountain model as it employs weight associated with each edge in the given network. The communities also exhibit relationship among them. However, they are simplified by the landslide algorithm thus helping in extraction of community structural features from large networks.

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Searching overlapping communities for group query

Cited by 14 publications

References 25 publications

Reliable Community Search in Dynamic Networks

Reliable Community Search in Dynamic Networks

Local Community Detection in Multiple Networks

Large Scale Community Discovery of Complex Networks Based On Approximate Optimization

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