Name
Abstract | ||
|---|---|---|
The present paper considers a finite sequence of graphs, e.g., coming from technological, biological, and social networks, each of which is modelled as a realization of a graph-valued random variable, and proposes a methodology to identify possible changes in stationarity in its generating stochastic process. In order to cover a large class of applications, we consider a general family of attributed graphs, chatacterized by a possible variable topology (edges and vertices) also in the stationary case. A Change Point Method (CPM) approach is proposed, that (i) maps graphs into a vector domain; (ii) applies a suitable statistical test; (iii) detects the change --if any-- according to a confidence level and provides an estimate for its time of occurrence. Two specific CPMs are proposed: one detecting shifts in the distribution mean, the other addressing generic changes affecting the distribution. We ground our proposal with theoretical results showing how to relate the inference attained in the numerical vector space to the graph domain, and vice versa. Finally, simulations on epileptic-seizure detection problems are conducted on real-world data providing evidence for the CPMs effectiveness. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Machine Learning | Mathematical optimization,Vector space,Random variable,Vertex (geometry),Inference,Stochastic process,Algorithm,Confidence interval,Versa,Statistical hypothesis testing,Mathematics |
DocType | Volume | Citations |
Journal | abs/1805.07113 | 0 |
PageRank | References | Authors |
0.34 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniele Zambon | 1 | 8 | 2.16 |
| Cesare Alippi | 2 | 1040 | 115.84 |
| Lorenzo Livi | 3 | 304 | 25.67 |