An algorithm for drawing general undirected graphs
Information Processing Letters
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Tracking Information Epidemics in Blogspace
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
2005 Special Issue: Cross-entropy embedding of high-dimensional data using the neural gas model
Neural Networks - 2005 Special issue: IJCNN 2005
Parametric Embedding for Class Visualization
Neural Computation
Extracting influential nodes for information diffusion on a social network
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
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Effective visualization is vital for understanding a complex network, in particular its dynamical aspect such as information diffusion process. Existing node embedding methods are all based solely on the network topology and sometimes produce counter-intuitive visualization. A new node embedding method based on conditional probability is proposed that explicitly addresses diffusion process using either the IC or LT models as a cross-entropy minimization problem, together with two label assignment strategies that can be simultaneously adopted. Numerical experiments were performed on two large real networks, one represented by a directed graph and the other by an undirected graph. The results clearly demonstrate the advantage of the proposed methods over conventional spring model and topology-based cross-entropy methods, especially for the case of directed networks.