Predicting Community Evolution in Social Networks

Abstract

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.

Complexity of a Microblogging Social Network in the Framework of Modern Nonlinear Science

“…In conclusion, we would like to mention that there exist many interesting problems that are not studied yet, such as critical phenomena of self-organization in microblogging networks based on the analysis of the nonlinear random dynamic system (10). This will be the subject of our future research.…”

“…The first direction relates to the analysis of the social networks data (see one of the latest reviews [9]), while the second concerns the development of models of the structure, dynamics, and evolution of social networks. The distinction between these two directions is somewhat arbitrary, since in most cases these directions overlap (see, e.g., [10,11]).…”

Predicting the evolution of communities in social networks using structural and temporal features

“…Classifiers were trained using as features the size of the communities and their evolutionary events over the last three timeframes. An extended version of this work is presented in [11], where a larger set of features and past timeframes are 978-1-5386-0756-5/17/$31.00 c 2017 IEEE used. Sequential and non-sequential classifiers were evaluated in [8] to infer four types of community evolutionary phenomena: continuation, shrinkage, growth and dissolution.…”

Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis

“…Due to the huge amount of data generated from social networks, many researchers investigated several research problems, such as predicting image popularity [16][17][18][19][20][21][22], identifying influential users [31,32], characterizing user behavior [33][34][35][36][37], and detecting community evolution [38,39]. In this paper, the problem of image popularity is addressed.…”