PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Stochastic models for the Web graph
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
The Journal of Machine Learning Research
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Heider vs simmel: emergent features in dynamic structures
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
Mining interesting link formation rules in social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Structural Predictors of Tie Formation in Twitter: Transitivity and Mutuality
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Who will follow you back?: reciprocal relationship prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Structural link analysis and prediction in microblogs
Proceedings of the 20th ACM international conference on Information and knowledge management
Learning latent friendship propagation networks with interest awareness for link prediction
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
LAFT-Explorer: inferring, visualizing and predicting how your social network expands
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Who proposed the relationship?: recovering the hidden directions of undirected social networks
Proceedings of the 23rd international conference on World wide web
| Hi-index | 0.00 |
Many patterns have been discovered to explain and analyze how people make friends. Among them is the triadic closure, supported by the principle of the transitivity of friendship, which means for an individual the friends of her friend are more likely to become her new friends. However, people's motivations under this principle haven't been well studied, and it's still unknown that how this principle works in diverse situations. In this paper, we try to study this principle deeply based on the behavior modeling. We study how one expands her egocentric network via her friends, also called intermediaries, based on the transitivity of friendship. We propose LaFT-Tree, a tree-based representation of friendship formation inspired from triadic closure. LaFT-Tree provides a hierarchical view of the flat structure of one's egocentric network by visualizing the expansion trace of one's egocentric network. We model people's friend-making behaviors using LaFT-LDA, a generative model for LaFT-Tree learning. The proposed model is evaluated on both synthetic and real-world social networks and experimental results demonstrate the effectiveness of LaFT-LDA for LaFT-Tree inference. We also present some interesting applications of the LaFT-Tree, showing that our model can be generalized and benefit other social network analysis tasks.