Objective how does the topology of a network influence diffusion speed? how fast the stationary distribution can be reac

Objectives topology’s influence on dynamical process model and analyze diffusion process in complex networks. random wal

Compressive SensingMotivationCompress data while gathering. This will save a lot of storage memory. The problem is we al

Objectives analyzing robustness with generating functions robustness of scale-free network limitations of ensemble-based

Sparse CodingEncode an entity by fewer digits. What we need: a dictionary to encoding and the input signal. We will retu

Aims friendship paradox and sampling in networks emergence of a giant connected component molloy-reed criterion in rando

Generative Models generate what? according to given data, generate new data like them. methods? variational autoencode

Composition layer neurons/units input weight activation function output $x^{(l)}=\sigma^{(l)}(W^{(l)}x^{(l-1)})$​ n

Aims make statements about the properties of a network if we only know the degree distribution properties of generating

Aims learn what is the small-world network: small diameter and large clustering coefficient. learn how to generate such

Vector Quantizationassign vectors to some groups, denoted by the centroids. Methods: K-means, Mixture Model. K-Means $k$

Aims: graph theory: micro-/macro perspective ensemble perspective random graph models empirical examples Questions For