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| author | jeanpouget-abadie <jean.pougetabadie@gmail.com> | 2015-05-19 01:15:33 +0200 |
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| committer | jeanpouget-abadie <jean.pougetabadie@gmail.com> | 2015-05-19 01:15:33 +0200 |
| commit | a13116fa67cd0811c8660d38e20500433bb7a3a3 (patch) | |
| tree | 1d2cecf8acb84dc2e923200b2f0abbf21953b2c2 /paper/sections/intro.tex | |
| parent | 3d3e1b5804b871fa9c7bc8fa2a712c997f629c3e (diff) | |
| download | cascades-a13116fa67cd0811c8660d38e20500433bb7a3a3.tar.gz | |
fixed typos
Diffstat (limited to 'paper/sections/intro.tex')
| -rw-r--r-- | paper/sections/intro.tex | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/paper/sections/intro.tex b/paper/sections/intro.tex index cc29ed7..206fbf6 100644 --- a/paper/sections/intro.tex +++ b/paper/sections/intro.tex @@ -62,7 +62,7 @@ required number of observed cascades is $\O(poly(s)\log m)$ \cite{Netrapalli:2012, Abrahao:13}. A more recent line of research~\cite{Daneshmand:2014} has focused on applying -advances in sparse recovery to the graph inference problem. Indeed, the graph +advances in sparse recovery to the network inference problem. Indeed, the graph can be interpreted as a ``sparse signal'' measured through influence cascades and then recovered. The challenge is that influence cascade models typically lead to non-linear inverse problems and the measurements (the state of the |