An Overlapping Community Detection Approach Based on Deepwalk and Improved Label Propagation

Yu, Hongtao; Ma, Ran; Chao, Jinbo; Zhang, Fuzhi

doi:10.1109/tcss.2022.3152579

Cited by 12 publications

(3 citation statements)

References 33 publications

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“…Notably, this algorithm uses a pruning strategy to maintain the number of labels for each node within a certain range. Finally, Yu et al [24] formed an overlapping community discovery method based on deepwalking and label propagation. The method first trains a node representation vector through the deepwork model and performs a dot product operation with the vectors of neighbouring nodes to derive the weights, before applying the proposed method to obtain the overlapping communities.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, research on overlapping community discovery has made great progress. Developments in this area include: 1) Faction filtering based on complete subgraphs to obtain overlapping communities [5,6]; 2) Fuzzy clustering [7][8][9][10], local extension [11][12][13][14][15], density peaking [16][17][18] and label propagation [19][20][21][22][23][24] based on node to discover overlapping clusters; 3) Graph partitioning based on edge to obtain overlapping communities [25][26][27]; 4) Node representation-based learning to identify overlapping communities [24,28]. Due to the complexity and diversity of real-world large-scale networks, improving the processing performance and accuracy of overlapping community discovery algorithms has been intensely researched.…”

Section: Introductionmentioning

confidence: 99%

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CILPA: An Overlapping Community Detection Algorithm Based on Label Propagation and Node Information

Liu

Shen

2023

Preprint

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With the proliferation of complex networks, a large number of effective community detection methods have been developed. Of these, the label propagation-based approach for community detection has proven to be very suitable for community detection. However, it should be noted that this approach is stochastic and unstable. To address these issues, a CILPA method is proposed based on label Propagation and node information to identify a more rational community. First, we obtain the local influence reflecting the local spatial location of nodes based on their neighbourhood relationships. The importance of the nodes is subsequently obtained by combining topological information and local influence. Secondly, a label propagation strategy with preference selection is introduced to obtain stable overlapping communities based on neighbourhood information. More importantly, a label checking strategy is articulated to reduce the impact of sparse connections. Extensive experiments on the LFR benchmark network and real-world networks demonstrate that the approach is both stable and effective.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CILPA: An Overlapping Community Detection Algorithm Based on Label Propagation and Node Information

Liu

Shen

2023

Preprint

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“…Deepwalk is a representative network structure analysis model that generates node sequences through random walks, and then inputs the sequences into the Word2vec to learn node embedding [31,32]. The model is proven effective in extracting node homogeneity and structural similarity, and is widely used in POI recommendation [30], community detection [33,34], and other areas [35,36]. In the proposed method, POI data is used to represent geographical elements; POIs within a zone are organized on a graph based on the spatial distances among POIs; then, the Deepwalk model is used to study the spatial relationship of POIs for urban functional classification.…”

Section: Introductionmentioning

confidence: 99%

Classifying Urban Functional Zones Based on Modeling POIs by Deepwalk

Yang

Zhang

2023

Sustainability

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Developing urban functional zone classification method to study urban spatial structure is a hotspot in current research. Using the word embedding model to excavate spatial relationship of the geographic elements in urban functional zones is an important way to develop urban functional zone classification method. However, in these studies, the spatial relationship of geographic elements was regarded as their homogeneity, while the structural similarity of geographical elements was ignored, which inevitably reduces the classification accuracy of urban functional zone classification method. This paper proposes to develop an urban functional zone classification method based on Deepwalk model, which could extract homogeneity and structural similarity of nodes in graph. The proposed method uses POI data to represent geographical elements, organizes POIs into graphs, and uses Deepwalk to embedding POIs for urban functional zone classification. It was applied to classify the urban functional zones of Chaoyang district in Beijing; and the classification results were compared with those of two baseline method based on Word2vec model and Place2vec model. The experimental results show that considering both the homogeneity and structural similarity of geographical elements, the proposed model has higher accuracy than the models only considering the homogeneity of geographical elements.

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Large-scale community detection based on core node and layer-by-layer label propagation

Zhang

Shang

Jiao

2023

Information Sciences

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An Overlapping Community Detection Approach Based on Deepwalk and Improved Label Propagation

Cited by 12 publications

References 33 publications

CILPA: An Overlapping Community Detection Algorithm Based on Label Propagation and Node Information

CILPA: An Overlapping Community Detection Algorithm Based on Label Propagation and Node Information

Classifying Urban Functional Zones Based on Modeling POIs by Deepwalk

Large-scale community detection based on core node and layer-by-layer label propagation

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