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Diffstat (limited to 'notes')
| -rw-r--r-- | notes/images/greedy_sparse_comparison.png | bin | 0 -> 35393 bytes | |||
| -rw-r--r-- | notes/reportYaron.tex | 2 |
2 files changed, 1 insertions, 1 deletions
diff --git a/notes/images/greedy_sparse_comparison.png b/notes/images/greedy_sparse_comparison.png Binary files differnew file mode 100644 index 0000000..3fab5b0 --- /dev/null +++ b/notes/images/greedy_sparse_comparison.png diff --git a/notes/reportYaron.tex b/notes/reportYaron.tex index bbde9dc..3f31ecd 100644 --- a/notes/reportYaron.tex +++ b/notes/reportYaron.tex @@ -300,7 +300,7 @@ The results of our findings on a very small social network (a subset of the famo \begin{figure} \centering \label{fig:comparison-with-greedy} -\includegraphics[scale=.35]{images/greedy_sparse_comparison.pdf} +\includegraphics[scale=.35]{images/greedy_sparse_comparison.png} \caption{Plot of the F1 score for different number of cascades for both the \textsc{greedy} algorithm and Algorithm~\ref{eq:optimization_program}. The cascades, graphs, and edge probabilities were identical for both algorithms. The dataset is a subset of 333 nodes and 5039 edges taken from the Facebook graph, made available by \cite{snap}. We chose $p_\text{init}=.05$, and the edge probability were chosen uniformly between $0$ and $0.8$. For Algorithm~\ref{eq:optimization_program}, we chose $\lambda=10$ and kept all edges with probability greater than $.1$ as true edges.} \end{figure} |
