1 Introduction
2 Related works
2.1 Traditional clustering approaches
2.2 Modularity optimization approaches
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Small communities are formed initially due to local optimization
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Core communities are formed as a result of merging small communities with the ability to create larger communities, and the method continues to evolve
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Local clustering of nodes
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Modification and improvement of clusters
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Network integration and community detection based on improved clusters
2.3 Label propagation approaches
2.4 Model-based approaches
3 Proposed model frameworks
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Using the probabilistic weighted model and matrix factorization method
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Heeding node connection density in detecting communities and combining it with edge weight
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Conceptual connection of community detection with the probability presence or absence of weighted edge
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Using evolutionary methods and MLE in calculations
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A weighted edge is possible between pairs of nodes in a community
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When a pair of nodes are observed in multiple communities, the possibility of a high weight edge between them is increased
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Communities can be overlap; overlap communities have higher weight density
4 Community detection by WSCD model
4.1 Updating the parameter
4.2 WSCD Algorithm
4.3 Computational complexity
4.4 initialization
5 Experiments
Method Name | Description |
---|---|
Louvain | Louvain maximizes a modularity score for each community |
Leiden | The Leiden algorithm is an improvement of the Louvain |
Label propagation | The LPA detects communities using network structure alone |
Greedy modularity | The CNM algorithm uses the modularity to find the communities structures |
ASLPAW | ASLPAW can be used for disjoint and overlapping community detection |
wCommunity | Algorithm to identify overlapping communities in weighted graphs |
5.1 Evaluation metrics
5.2 Real-world Networks
Dataset Name | N (Number of Nodes) | E (Number of Edges) |
---|---|---|
Net Science | 379 | 914 |
Wiki-Vote | 879 | 2914 |
Twitter | 1003 | 25779 |
5.2.1 Experimental results on real-world networks
5.3 Synthetic networks
Parameter | Description |
---|---|
N | Number of nodes |
K | Average degree |
Min K | Minimum degree of nodes |
Max K | Maximum degree of nodes |
μt | Mixing parameter for the topology |
μw | Mixing parameter for the weights |
Min C | Minimum for the community sizes |
Max C | Maximum for the community sizes |
#N |
μ
t
|
μ
w
| K | #Edges | #Communities | Min C | Max C |
---|---|---|---|---|---|---|---|
1000 | 0.1 | 0.1 | 20 | 7642 | 27 | 20 | 60 |
1000 | 0.2 | 0.2 | 20 | 7680 | 28 | 20 | 60 |
1000 | 0.3 | 0.3 | 20 | 7687 | 29 | 20 | 60 |
1000 | 0.4 | 0.4 | 20 | 7762 | 29 | 20 | 60 |
1000 | 0.5 | 0.5 | 20 | 7602 | 30 | 20 | 60 |
1000 | 0.6 | 0.6 | 20 | 7743 | 32 | 20 | 60 |
1000 | 0.7 | 0.7 | 20 | 7817 | 34 | 20 | 60 |
1000 | 0.8 | 0.8 | 20 | 7756 | 35 | 20 | 60 |