Overlapping Community Discovery Method Based on Two Expansions of Seeds

Li, Yan; He, Jing; Wu, Youxi; Lv, Rongjie

doi:10.3390/sym13010018

Cited by 9 publications

(9 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Input: a network G(V, E), the number of nodes in the network n Output: the importance of each node (1) Initialize D � ∅, CC � ∅ (2) for i in n do (3) Calculate D(i) using formula (4) during D decomposition (4) end for (5) for i in n do (6) Calculate CC(i) using formula (5) (7) end for (8) Create matrix R using formula (6) (9) for i in 2 do (10) Calculate E i using formula (7) (11) end for (12) for i in n do (13) Calculate CLC(i) using formula (10) ( 14) end for ALGORITHM 1: Node importance evaluation algorithm.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…for w in neighbors[v] do (19) if S vw > S then (20) addIsolateNode(v, w,OC) (21) end if (22) end for (23) end if (24) end for ALGORITHM 2: Community merging and isolated nodes adjustment. 6 Complexity where F1(C i , C j ′ ) is the harmonic average of the Precision and Recall between the two communities:…”

Section: Community Detection Resultsmentioning

confidence: 99%

“…According to the different characteristics of community structure, the community structure is divided into two categories: nonoverlapping community [4] and overlapping community [5,6]. Nonoverlapping community means that the nodes in the network belong to only one community; each community exists independently.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

Liu

Guo

2021

Complexity

View full text Add to dashboard Cite

Community structure is one of the most important characteristics of complex networks, which has important applications in sociology, biology, and computer science. The community detection method based on local expansion is one of the most adaptable overlapping community detection algorithms. However, due to the lack of effective seed selection and community optimization methods, the algorithm often gets community results with lower accuracy. In order to solve these problems, we propose a seed selection algorithm of fusion degree and clustering coefficient. The method calculates the weight value corresponding to degree and clustering coefficient by entropy weight method and then calculates the weight factor of nodes as the seed node selection order. Based on the seed selection algorithm, we design a local expansion strategy, which uses the strategy of optimizing adaptive function to expand the community. Finally, community merging and isolated node adjustment strategies are adopted to obtain the final community. Experimental results show that the proposed algorithm can achieve better community partitioning results than other state-of-the-art algorithms.

show abstract

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Community Detection Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

Liu

Guo

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…In 2020 [18], Two Expansion Seed (TES) proposed gravitational degree force where used in seed selection for robust discovery. As first time expanded, the selected seeds are expanded by greedy strategy based on f fitness function.…”

Section: Related Workmentioning

confidence: 99%

Exploring Overlapped Communities Based on Optimized Community’s Resolution Controlling Evaluation in Social Networks

2023

IJIES

View full text Add to dashboard Cite

Community structure is one of the main structural features of networks and detecting overlapped community structure is an important field in social network analysis. In recent years, local community detection algorithms which detect overlapped community structure have been developed. However, the most existing algorithms suffer unstable community structure because the influences of parameter for controlling community's resolution of fitness evaluation functions where used in identifying communities. Therefore, this article designs the optimized parameter evaluation formula to avoid the parameter influences and the algorithm is modelled on local expansion strategy. In this work, firstly identifies the seed or core node by using extended jaccard similarity and form initial community via seed. Then local community is detected by expanding the initial community with fitness function based on proposed optimized parameter evaluation and finally overlapped nodes are identified by merging detected local communities. In this article, the algorithm is implemented by using small dataset from network data repository site and large networks from Stanford large network datasets collection. The performance results of algorithm are compared with LFM (Local Fitness Method), OSLOM (Order Statistics Local Optimization Method), DEMON (Democratic Estimate of Modular Organization of a Network), NILPA (Node Important based Label Propagation) and GREESE (Greedy Coupled-seeds Expansion) algorithm. Then, the proposed algorithm proves that it effectively performs and saves execution time.

show abstract

“…Ameliorated Local Fitness Maximization [24] discovers overlapping communities using initial community set expansion and optimization relying on a local fitness function, which attains linear time complexity without loss of effectiveness via multiple-vertex removal and addition on the premise of prohibiting community drift. Two Expansions of Seeds [25] distinguishes the local maximum vertices as the seeds by adapting the topological feature of the network, and then twice expands seeds based on the fitness function and the gravitational degree. The distance between correlative communities is computed to merge similar communities.…”

Section: Local Seed Expansionmentioning

confidence: 99%

An Overlapping Community Detection Approach in Ego-Splitting Networks Using Symmetric Nonnegative Matrix Factorization

Huang

Jiang

et al. 2021

Symmetry

View full text Add to dashboard Cite

Overlapping clustering is a fundamental and widely studied subject that identifies all densely connected groups of vertices and separates them from other vertices in complex networks. However, most conventional algorithms extract modules directly from the whole large-scale graph using various heuristics, resulting in either high time consumption or low accuracy. To address this issue, we develop an overlapping community detection approach in Ego-Splitting networks using symmetric Nonnegative Matrix Factorization (ESNMF). It primarily divides the whole network into many sub-graphs under the premise of preserving the clustering property, then extracts the well-connected sub-sub-graph round each community seed as prior information to supplement symmetric adjacent matrix, and finally identifies precise communities via nonnegative matrix factorization in each sub-network. Experiments on both synthetic and real-world networks of publicly available datasets demonstrate that the proposed approach outperforms the state-of-the-art methods for community detection in large-scale networks.

show abstract

Overlapping Community Discovery Method Based on Two Expansions of Seeds

Cited by 9 publications

References 51 publications

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

Exploring Overlapped Communities Based on Optimized Community’s Resolution Controlling Evaluation in Social Networks

An Overlapping Community Detection Approach in Ego-Splitting Networks Using Symmetric Nonnegative Matrix Factorization

Contact Info

Product

Resources

About