A local dynamic method for tracking communities and their evolution in dynamic networks

Hu, Yanmei; Yang, Bo; Lv, Chenyang

doi:10.1016/j.knosys.2016.07.027

Cited by 17 publications

(10 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second interesting finding is that authors seem to use various datasets for comparison, as they often include synthetic graphs and/or real-world graphs. In the assessed papers, 1 made use of only synthetic graphs [32], 32 used only real graphs [13, 14, 16, 22, 27, 28, 30, 31, 33, 34, 36-41, 44, 45, 48-52, 54-57, 59-63] and 17 used both [6,15,17,18,20,21,23,24,29,42,43,46,47,53,58,64,65]. In the 49 papers that used real graphs 47 different real graphs were introduced.…”

Section: Cs Type Methodsmentioning

confidence: 99%

“…For the broad selection, the initial list of 51 papers on DCD methods was used [6, 13-18, 20-23, 27-65]. It was obtained by supplementing 32 temporal trade-off algorithms [6, 13-15, 17, 21, 22, 24, 27-50] from [1] with 19 algorithms not included in the aforementioned survey [16,18,20,23,[51][52][53][54][55][56][57][58][59][60][61][62][63][64][65] that nonetheless possess interesting characteristics with regards to community and evolution extraction. Figure 1 illustrates the relevance of adding those 19 papers as it ensures the inclusion of more recent methods.…”

Section: Algorithm Selectionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Study of Community Detection Techniques for Large Evolving Graphs

Coppens

Venter

Mitrović

et al. 2020

Machine Learning and Knowledge Discovery in Databases

View full text Add to dashboard Cite

Community detection has recently received increased attention due to its wide range of applications in many fields. While at first most techniques were focused on discovering communities in static networks, lately the focus has shifted toward evolving networks because of their high relevance in real-life problems. Given the increasing number of the methods being proposed, this paper explores the current availability of empirical comparative studies of dynamic methods and also provides its own qualitative and quantitative comparison with the aim of gaining more insight in the performance of available methods. The results show that no single best performing community detection technique exists, but rather, the choice of the method depends on the objective and dataset characteristics.

show abstract

Section: Cs Type Methodsmentioning

confidence: 99%

Section: Algorithm Selectionmentioning

confidence: 99%

A Comparative Study of Community Detection Techniques for Large Evolving Graphs

Coppens

Venter

Mitrović

et al. 2020

Machine Learning and Knowledge Discovery in Databases

View full text Add to dashboard Cite

show abstract

“…The core subgraph can quickly capture community evolution events, including formation, decomposition, and division. Hu et al [33] found the dynamic community by exploring the local view of changing nodes.…”

Section: B Community Evolutionmentioning

confidence: 99%

“…Extracting the evolution behaviors from community C t i to C t+1 i helps us analyze the evolving trends of communities. Previous research [33], [38] defined seven explicit evolution behaviors of communities.…”

Section: Evolution Behavior Extractionmentioning

confidence: 99%

Detecting and Evolving Microblog Community Based on Structure and Gravity Cohesion

Liu

2020

IEEE Access

View full text Add to dashboard Cite

Microblogs are open and real-time online social network platforms used by people to make posts about their moods, experiences, and interests. It will be very significant to gather microblog users who have similar interests and hobbies into the same community. In this paper, we propose novel approaches for detecting and evolving dynamic microblog communities. First, inspired by the universal gravitation law, we redefine the gravitation relationships among microblog users. Based on the structure of the microblog social network, we define the basic nodes and their gravity tendency and propose the microblog community detection algorithm. Second, we determine the community changes in the microblog social networks at times t and t + 1 and propose a microblog community evolution algorithm. Third, we define the mutual transformation probability between communities at times t and t + 1 and propose the microblog community evolution behavior algorithm. The experiment includes a comparison and evaluation of the microblog community detection, evolution, and behavior extraction algorithms and the optimal ranges of the parameters involved in these algorithms. The experimental results indicate that our proposed algorithms have good performance compared to other benchmarking methods.

show abstract

“…HOCTracker [13] is a unified framework which finds out the evolution patterns of hierarchical and overlapping communities in online social networks. Hu et al [14] proposed a method to track dynamic communities and their evolutionary behaviors by exploring the local views of nodes that change. Ma et al [15] proposed a semisupervised evolutionary nonnegativematrix factorization (sE-NMF) for detecting dynamic communities by incorporating a priori information into ENMF.…”

Section: A Dynamic Community Detectionmentioning

confidence: 99%

Dynamic Topical Community Detection in Social Network: A Generative Model Approach

Zhang

Ning

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Social networks that are dynamic contain rich network structure and content information. In dynamic networks, it is necessary to discover communities and their topical meanings. However, existing methods either only discover communities with ignoring their topical meaning in dynamic networks, or they discover communities and their topics in static networks. In this paper, we identify the problem of dynamic topical community detection and propose a dynamic topical community detection (DTCD) method to detect communities and their topical meanings in dynamic networks. The DTCD is a generative model integrating network structure, text, and time. The DTCD considers a community as a mixture of topics and generates the neighbors and documents of the node and their time stamps at the same time via the community. The latent variables are learned by collapsed Gibbs sampling. The DTCD not only can find communities and their topics, but also capture the temporal variations of communities and topics. The experimental results on two real-world datasets demonstrate the effectiveness of DTCD. INDEX TERMS Social network, dynamic community detection, user generated content, generative model, collapsed Gibbs sampling.

show abstract

A local dynamic method for tracking communities and their evolution in dynamic networks

Cited by 17 publications

References 20 publications

A Comparative Study of Community Detection Techniques for Large Evolving Graphs

A Comparative Study of Community Detection Techniques for Large Evolving Graphs

Detecting and Evolving Microblog Community Based on Structure and Gravity Cohesion

Dynamic Topical Community Detection in Social Network: A Generative Model Approach

Contact Info

Product

Resources

About