Modelling social network evolution
Radosław Michalski,Sebastian Palus,Piotr Bródka,Przemysław Kazienko,Krzysztof Juszczyszyn +4 more
- 06 Oct 2011
- pp 283-286
TL;DR: The model presented in the paper focuses on definition of differences between following network snapshots by means of Graph Differential Tuple.
read more
Abstract: Most of the real social networks extracted from various data sources evolve and change their profile over time. For that reason, there is a great need to model evolution of networks in order to enable complex analyses of theirs dynamics. The model presented in the paper focuses on definition of differences between following network snapshots by means of Graph Differential Tuple.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Predicting group evolution in the social network
Piotr Bródka,Przemysław Kazienko,Bartosz Kołoszczyk +2 more
- 05 Dec 2012
TL;DR: In this paper, a new approach for group evolution prediction is presented and examined and experimental studies on four evolving social networks revealed that the prediction based on the simple input features may be very accurate, some classifiers are more precise than the others and parameters of the group evolution extracion method significantly influence the prediction quality.
56
•Posted Content
Predicting Group Evolution in the Social Network
TL;DR: A new aproach for group evolution prediction is presented and it is revealed that the prediction based on the simple input features may be very accurate, some classifiers are more precise than the others and parameters of the group evolution extracion method significantly influence the prediction quality.
Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures
Mateusz Nurek,Radosław Michalski +1 more
TL;DR: The key concept of this work is to evaluate how well social network measures when combined with other features gained from the feature engineering align with the classification of the members of organizational social network.
28
•Posted Content
A Method for Group Extraction and Analysis in Multilayer Social Networks.
TL;DR: A new approach to prediction of group evolution in the social network was developed and it was shown, that using even a simple sequence, which consists of several preceding groups sizes and events, as an input for the classifier, the learned model can produce very good results also for simple classifiers.
25
•Proceedings Article
Individual Neighbourhood Exploration in Complex Multi-layered Social Network.
Przemysław Kazienko,Piotr Bródka,Katarzyna Musial +2 more
- 01 Jan 2010
TL;DR: A new measure called cross layered multi-layered clustering coefficient (CLMCC) is proposed, which enables to analyse the density of mutual connections of neighbours that occur in at least a given number of layers in a social network.
15
References
On a relation between graph edit distance and maximum common subgraph
TL;DR: In this paper a particular cost function for graph edit distance is introduced, and it is shown that under this cost functiongraph edit distance computation is equivalent to the maximum common subgraph problem.
619
•Dissertation
Graph similarity and matching
Laura A. Zager
- 01 Jan 2005
TL;DR: A new similarity measure is developed that uses a linear update to generate both node and edge similarity scores and has desirable convergence properties and this thesis also explores the application of the similarity measure to graph matching.
52
Individual Neighbourhood Exploration in Complex Multi-layered Social Network
Przemysław Kazienko,Piotr Bródka,Katarzyna Musial +2 more
- 31 Aug 2010
TL;DR: In this paper, a new measure called cross layered multi-layered clustering coefficient (CLMCC) is proposed to analyze the density of mutual connections of neighbours that occur in at least a given number of layers in a social network.
26
•Proceedings Article
Individual Neighbourhood Exploration in Complex Multi-layered Social Network.
Przemysław Kazienko,Piotr Bródka,Katarzyna Musial +2 more
- 01 Jan 2010
TL;DR: A new measure called cross layered multi-layered clustering coefficient (CLMCC) is proposed, which enables to analyse the density of mutual connections of neighbours that occur in at least a given number of layers in a social network.
15
Matching graphs
Linda Eroh,Michelle Schultz +1 more
- 01 Oct 1998
TL;DR: In this paper, the authors studied the behavior of sequences of iterated matching graphs and showed that even cycles can be added to the list of known matching graphs, which is not known in general.
11