2016
DOI: 10.1016/j.physa.2015.09.066
|View full text |Cite
|
Sign up to set email alerts
|

Partition signed social networks via clustering dynamics

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
8
0
1

Year Published

2016
2016
2024
2024

Publication Types

Select...
5
2
2

Relationship

0
9

Authors

Journals

citations
Cited by 26 publications
(9 citation statements)
references
References 42 publications
0
8
0
1
Order By: Relevance
“…12 and 53-56 for the case of community detection in complex (unsigned) networks by means of the classical attractive Kuramoto model. Very recently, 68 another dynamical clustering based on the dynamics of a deformed KM has been implemented to analyze the structure of signed social interactions. Here, after illustrating in Sec.…”
Section: Discussionmentioning
confidence: 99%
“…12 and 53-56 for the case of community detection in complex (unsigned) networks by means of the classical attractive Kuramoto model. Very recently, 68 another dynamical clustering based on the dynamics of a deformed KM has been implemented to analyze the structure of signed social interactions. Here, after illustrating in Sec.…”
Section: Discussionmentioning
confidence: 99%
“…A dynamic algorithm is proposed in [25] for signed social networks. The authors used normalized mutual information (NMI ) to determine the performance of the detected communities.…”
Section: Community Detection In Signed Networkmentioning
confidence: 99%
“…Their approach predicts item ratings that users have not rated by the employ of SVD technology, and then uses Pearson correlation similarity measurement to find the target users neighbors, lastly produces the recommendations, which can alleviate the sparsity problems of the user item rating dataset.Kindly rephrase the sentence "Their approach predicts item ....." Superiority of CF algorithms with clustering techniques relieve the impact of data sparseness and cold start problems, which have been verified by many research experiments [17,18]. Recently, many scholars have also proposed dynamic evolutionary clustering algorithms [19][20][21], compared to the classical Kmeans algorithm, which not only reduced time-consuming and complexity, but also have indeterminate classification category. Liao et al [22] have presented an approach which applied the user-product rating matrix without the necessity of collecting extra attributes about customers and products to cluster, and clusters are formed automatically.…”
Section: Introductionmentioning
confidence: 98%