In this appendix, we provide the missing proofs of Section~\ref{sec:results}
and Section~\ref{sec:lowerbound}. We also show additional experiments on the
running time of our recovery algorithm which could not fit in the main part of
the paper.

\subsection{Proofs of Section~\ref{sec:results}}

\paragraph{Bounded gradient} The proof of Lemma~\ref{lem:ub} giving an upper
bound on the $\ell_{\infty}$ norm of the gradient of the likelihood function is
given below.

\begin{proof}
The gradient of $\mathcal{L}$ is given by:
\begin{multline*}
    \nabla \mathcal{L}(\theta^*) =
    \frac{1}{|\mathcal{T}|}\sum_{t\in \mathcal{T}}x^t\bigg[
        x_i^{t+1}\frac{f'}{f}(\inprod{\theta^*}{x^t})\\
    - (1-x_i^{t+1})\frac{f'}{1-f}(\inprod{\theta^*}{x^t})\bigg]
\end{multline*}

Let $\partial_j \mathcal{L}(\theta)$ be the $j$-th coordinate of
$\nabla\mathcal{L}(\theta^*)$.  Writing
$\partial_j\mathcal{L}(\theta^*)
= \frac{1}{|\mathcal{T}|}\sum_{t\in\mathcal{T}} Y_t$ and since
$\E[x_i^{t+1}|x^t]= f(\inprod{\theta^*}{x^t})$, we have that $\E[Y_{t+1}|Y_t]
= 0$. Hence $Z_t = \sum_{k=1}^t Y_k$ is a martingale.

Using assumption (LF), we have almost surely $|Z_{t+1}-Z_t|\leq
\frac{1}{\alpha}$ and we can apply Azuma's inequality to $Z_t$:
\begin{displaymath}
    \P\big[|Z_{\mathcal{T}}|\geq \lambda\big]\leq
    2\exp\left(\frac{-\lambda^2\alpha}{2n}\right)
\end{displaymath}

Applying a union bound to have the previous inequality hold for all coordinates
of $\nabla\mathcal{L}(\theta)$ implies:
\begin{align*}
    \P\big[\|\nabla\mathcal{L}(\theta^*)\|_{\infty}\geq \lambda \big]
    &\leq 2m\exp\left(\frac{-\lambda^2n\alpha}{2}\right)
\end{align*}
Choosing $\lambda\defeq 2\sqrt{\frac{\log m}{\alpha n^{1-\delta}}}$ concludes
the proof.
\end{proof}

%\subsubsection{Approximate sparsity proof}
\paragraph{(RE) with high probability} We now prove Proposition~\ref{prop:fi}.
The proof mostly relies on showing that the Hessian of likelihood function
$\mathcal{L}$ is sufficiently well concentrated around its expectation.

\begin{proof}Writing $H\defeq \nabla^2\mathcal{L}(\theta^*)$, if
    $ \forall\Delta\in C(S),\;
        \|\E[H] - H]\|_\infty\leq \lambda $
    and $\E[H]$ verifies the $(S,\gamma)$-(RE)
    condition then:
    \begin{equation}
        \label{eq:foo}
    \forall \Delta\in C(S),\;
    \Delta H\Delta \geq
    \Delta \E[H]\Delta(1-32s\lambda/\gamma)
    \end{equation}
    Indeed, $
    |\Delta(H-E[H])\Delta| \leq 2\lambda \|\Delta\|_1^2\leq
    2\lambda(4\sqrt{s}\|\Delta_s\|_2)^2
    $.
    Writing
    $\partial^2_{i,j}\mathcal{L}(\theta^*)=\frac{1}{|\mathcal{T}|}\sum_{t\in
    T}Y_t$ and using $(LF)$ and $(LF2)$ we have $\big|Y_t - \E[Y_t]\big|\leq
    \frac{3}{\alpha}$.
    Applying Azuma's inequality as in the proof of Lemma~\ref{lem:ub}, this
    implies:
    \begin{displaymath}
        \P\big[\|\E[H]-H\|_{\infty}\geq\lambda\big] \leq
        2\exp\left(-\frac{n\alpha\lambda^2}{3} + 2\log m\right)
    \end{displaymath}
    Thus, if we take $\lambda=\sqrt{\frac{9log m}{\alpha
    n^{1-\delta}}}$, $\|E[H]-H\|_{\infty}\leq\lambda$ w.p at least
    $1-e^{-n^{\delta}\log m}$. When $n^{1-\delta}\geq
    \frac{1}{28\gamma\alpha}s^2\log m$, \eqref{eq:foo} implies
    $
    \forall \Delta\in C(S),\;
    \Delta H\Delta \geq \frac{1}{2} \Delta \E[H]\Delta,
    $ w.p.~at least $1-e^{-n^{\delta}\log m}$ and the conclusion of
    Proposition~\ref{prop:fi} follows.
\end{proof}

\subsection{Proof of Theorem~\ref{thm:lb}}

Let us consider an algorithm $\mathcal{A}$ which verifies the recovery
guarantee of Theorem~\ref{thm:lb}: there exists a probability distribution over
measurements such that for all vectors $\theta^*$, \eqref{eq:lb} holds w.p.
$\delta$. This implies by the probabilistic method that for all distribution
$D$ over vectors $\theta$, there exists an $n\times m$ measurement matrix $X_D$
with such that \eqref{eq:lb} holds w.p. $\delta$ ($\theta$ is now the random
variable).

Consider the following distribution $D$: choose $S$ uniformly at random from a
``well-chosen'' set of $s$-sparse supports $\mathcal{F}$ and $t$ uniformly at
random from
$X \defeq\big\{t\in\{-1,0,1\}^m\,|\, \mathrm{supp}(t)\in\mathcal{F}\big\}$.
Define $\theta = t + w$ where
$w\sim\mathcal{N}(0, \alpha\frac{s}{m}I_m)$ and $\alpha = \Omega(\frac{1}{C})$.

Consider the following communication game between Alice and Bob: \emph{(1)}
Alice sends $y\in\reals^m$ drawn from a Bernouilli distribution of parameter
$f(X_D\theta)$ to Bob. \emph{(2)} Bob uses $\mathcal{A}$ to recover
$\hat{\theta}$ from $y$.  It can be shown that at the end of the game Bob now
has a quantity of information $\Omega(s\log \frac{m}{s})$ about $S$. By the
Shannon-Hartley theorem, this information is also upper-bounded by $\O(n\log
C)$. These two bounds together imply the theorem.


\subsection{Running Time Analysis}

\begin{figure}
  \centering
  \includegraphics[scale=.4]{figures/n_nodes_running_time.pdf}
  \caption{Running time analysis for estimating the parents of a \emph{single
    node} on a Barabasi-Albert graph as a function of the number of nodes in
    the graph. The parameter $k$ (number of nodes each new node is attached to)
    is constant equal to $30$.  $p_{\text{init}}$ is chosen equal to $.15$, and
    the edge weights are chosen uniformly at random in $[.2,.7]$. The
  penalization parameter $\lambda$ is chosen equal to $.1$.}
    \label{fig:running_time_n_nodes}
\end{figure}

\begin{figure}
  \centering
  \includegraphics[scale=.4]{figures/n_cascades_running_time.pdf}
  \caption{Running time analysis for estimating the parents of a \emph{single
    node} on a Barabasi-Albert graph as a function of the number of total
    observed cascades.  The parameter $k$ (number of nodes each new node is
    attached to) is constant equal to $30$.  $p_{\text{init}}$ is chosen equal
  to $.15$, and the edge weights are chosen uniformly at random in $[.2,.7]$.
The penalization parameter $\lambda$ is chosen equal to $.1$.}
    \label{fig:running_time_n_cascades}
\end{figure}

We include here a running time analysis of the different algorithms, as shown
in Figure~\ref{fig:running_time_n_nodes} and
Figure~\ref{fig:running_time_n_cascades}. As expected, the simple greedy
algorithm is the fastest algorithm. Interestingly, the non-penalized convex
optimization program also converges faster than our suggested penalized
maximum-likelihood program.

%\subsection{Other continuous time processes binned to ours: proportional
%hazards model}

%\subsection{Irrepresentability vs. Restricted Eigenvalue Condition}
%In the words and notation of Theorem 9.1 in \cite{vandegeer:2009}:
%\begin{lemma}
%\label{lemm:irrepresentability_proof}
%Let $\phi^2_{\text{compatible}}(L,S) \defeq \min \{ \frac{s
%\|f_\beta\|^2_2}{\|\beta_S\|^2_1} \ : \ \beta \in {\cal R}(L, S) \}$, where
%$\|f_\beta\|^2_2 \defeq \{ \beta^T \Sigma \beta \}$ and ${\cal R}(L,S) \defeq
%\{\beta : \|\beta_{S^c}\|_1 \leq L \|\beta_S\|_1 \neq 0\}$. If
%$\nu_{\text{irrepresentable}(S,s)} < 1/L$, then $\phi^2_{\text{compatible}}(L,S)
%\geq (1 - L \nu_{\text{irrepresentable}(S,s)})^2 \lambda_{\min}^2$.
%\end{lemma}

%Since ${\cal R}(3, S) = {\cal C}$, $\|\beta_S\|_1 \geq \|\beta_S\|_2$, and
%$\|\beta_S\|_1 \geq \frac{1}{3} \|\beta_{S^c}\|_1$ it is easy to see that
%$\|\beta_S\|_1 \geq \frac{1}{4} \|\beta\|_2$ and therefore that: $\gamma_n \geq
%\frac{n}{4s}\phi^2_{\text{compatible}}(3,S)$

%Consequently, if $\epsilon > \frac{2}{3}$, then
%$\nu_{\text{irrepresentable}(S,s)} < 1/3$ and the conditions of
%Lemma~\ref{lemm:irrepresentability_proof} hold.



%\subsection{Lower bound for restricted eigenvalues (expected hessian) for
%different graphs}

%\subsection{Better asymptotic w.r.t expected hessian}

%\subsection{Confidence intervals?}

%\subsection{Active learning}
