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Diffstat (limited to 'finale/sections')
| -rw-r--r-- | finale/sections/experiments.tex | 2 | ||||
| -rw-r--r-- | finale/sections/intro.tex | 10 |
2 files changed, 11 insertions, 1 deletions
diff --git a/finale/sections/experiments.tex b/finale/sections/experiments.tex index 8e5a9eb..74025f2 100644 --- a/finale/sections/experiments.tex +++ b/finale/sections/experiments.tex @@ -43,7 +43,7 @@ for the center node, points to its (clock-wise) neighbor. In order for the baseline to be fair, we choose to create cascades starting from the source node on the fly both in the case of the uniform source and for the active learning policy. Each cascade is therefore `observed' only once. We plot the RMSE of the -graph i.e. $RMSE^2 = \frac{1}{n^2} \|\hat \mathbf{\Theta} - +graph i.e. $RMSE^2 = \frac{1}{n^2} \|\hat{\mathbf{\Theta}} - \mathbf{\Theta}\|^2_2$. graphs/datasets diff --git a/finale/sections/intro.tex b/finale/sections/intro.tex index 56f31b7..f1f1859 100644 --- a/finale/sections/intro.tex +++ b/finale/sections/intro.tex @@ -1,3 +1,12 @@ +Graphs have been extensively studied for their propagative abilities: +connectivity, routing, gossip algorithms, etc. A diffusion process taking +place over a graph provides valuable information about the presence and weights +of its edges. \emph{Cascades} are a specific type of diffusion processes in +which a particular infectious behavior spreads over the nodes of the graph. By +only observing the ``infection times'' of the nodes in the graph, one might +hope to recover the underlying graph and the parameters of the cascade model. +This problem is known in the literature as the \emph{Network Inference problem}. + \begin{itemize} \item graph inference: what is the proble? what is an observation, contagion model @@ -8,4 +17,5 @@ speedup over passive \end{itemize} \end{itemize} + \input{sections/related.tex} |