Identification of Group Changes in Blogosphere

Gliwa, Bogdan; Saganowski, Stanisław; Zygmunt, Anna; Bródka, Piotr; Kazienko, Przemysław; Kozák, Josef

doi:10.1109/asonam.2012.207

“…One of the algorithms used in presented research, which identifies groups of interacting users is the SGCI algorithm [23,24]. The main idea of the algorithm is finding groups in subsequent time frames and identification of their continuation.…”

Section: Predicting Group Evolution Using Sgci Results and The Notionmentioning

confidence: 99%

“…Both approaches are similar but the first one is based on the SGCI method results and the second is based on the GED method results. The SGCI method was introduced in [23] (the concept of stable groups was used from the method described in [24]). In [25] authors of SGCI described a tool for visualisation of group evolution based on the SGCI method.…”

Section: Predicting Group Evolution In the Social Networkmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Community Evolution in Social Networks

Saganowski

¹

,

Gliwa

²

,

Bródka

³

et al. 2015

Entropy

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Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI) and Group Evolution Discovery (GED). Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3-5 last periods.

show abstract

“…Both approaches are similar but the first one is based on the SGCI method results and the second is based on the GED method results. The SGCI method was introduced in [24] (the concept of stable groups was used from the method described in [25]). In [26] authors of SGCI described a tool for visualisation of group evolution based on the SGCI method.…”

Section: Predicting Group Evolution In the Social Networkmentioning

confidence: 99%

Predicting Community Evolution in Social Networks

Saganowski

¹

2015

Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

View full text Add to dashboard Cite

Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI) and Group Evolution Discovery (GED). Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3-5 last periods. OPEN ACCESSEntropy 2015, 17 2

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“…In order to express the evolution procedure more clearly, the label mechanism is proposed. Different from existing methods which track communities after the community detection using similarity measurement [21,[24][25][26][27][28], our tracking algorithm tracks communities during the detection procedure. The goals of tracking algorithms are (Figure 8): Figure 8.…”

Section: Tracking Communitiesmentioning

confidence: 99%

Core-Based Dynamic Community Detection in Mobile Social Networks

Xu

¹

,

Hu

²

,

Wang

³

et al. 2013

Entropy

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Abstract:The topic of community detection in social networks has attracted a lot of attention in recent years. Existing methods always depict the relationship of two nodes using the snapshot of the network, but these snapshots cannot reveal the real relationships, especially when the connection history among nodes is considered. The problem of detecting the stable community in mobile social networks has been studied in this paper. Community cores are considered as stable subsets of the network in previous work. Based on these observations, this paper divides all nodes into a few of communities due to the community cores. Meanwhile, communities can be tracked through incremental computing. Experimental results based on real-world social networks demonstrate that our proposed method performs better than the well-known static community detection algorithm in mobile social networks.

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Identification of Group Changes in Blogosphere

Cited by 29 publications

References 24 publications

Predicting Community Evolution in Social Networks

Predicting Community Evolution in Social Networks

Predicting Community Evolution in Social Networks

Core-Based Dynamic Community Detection in Mobile Social Networks

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