\subsection{Influence Cascades}

We consider a weighted graph $G$ over the set of vertices $V$. The edge weights
are given by an (unknown) weight matrix $\Theta:V^2\to\reals^+$. $\Theta_{i,j}
= 0$ simply expresses that there is no edge from $i$ to $j$. We define $m=|V|$,
and for $j\in V$, we write $\theta_j$ the $j$-th column of $\Theta$: this is
the indicator vector of the parents of $j$ in $G$.

An \emph{influence cascade} is a homogeneous discrete-time Markov process over
$\{0,1\}^m$.  More precisely, we represent the set of nodes infected at time
step $t-1\in\ints^+$ by a vector $x^{t}\in\{0,1\}^{m}$ where $x^{t}_k = 1$ can
be interpreted as \emph{node $k$ was infected at time step $t-1$}.

We will restrict ourselves to influence processes such that for $j\in V$,
$x_j^{t+1}$ is a Bernouilli variable whose parameter can be expressed as
a function $f$ of $\inprod{\theta_j}{x^t}$, where $\inprod{a}{b}$ is the inner
product of $a$ and $b$ in $\reals^m$:
$\theta_j$ and $x^t$:
\begin{equation}\label{eq:markov}
    \P\big[x_j^{t+1} = 1\,|\, x^t\big] \defeq f(\inprod{\theta_j}{x^t}).
\end{equation}
As we will later see, this type of influence processes encompasses the most
widely used influence processes: the Voter model (V), the Independent Cascade
model (IC), and the Linear Threshold model (T).

\subsection{Problem}

Our goal is to infer the weight matrix $\Theta$ by observing influence
cascades. Given the form of \eqref{eq:markov}, we will focus on a single row
$\theta_j$ of $\Theta$ and we will write $y^t_j \defeq x_j^{t+1}$. When there
is no ambiguity, we will simply write $y^t = y^t_j$ and $\theta= \theta_j$.

The input of our problem is a set of observations $(x^1, y^1),\ldots,(x^n,
y^n)$. If we observe more than one cascade, we simply concatenate the
observations from each cascade to form a single longer cascade.
Equation~\eqref{eq:markov} still holds for each observation.

With these notations, the set of observations follows a Generalized Linear
Model with Bernoulli distribution and link function $g$ such that $g^{-1}
= f$. The log-likelihood of $\theta$ can be written:
\begin{displaymath}
    \mathcal{L}(\theta\,|\, x, y) = \sum_{t=1}^ny^t \log f(\inprod{\theta}{x^t})
    + (1-y^t)\log \big(1-f(\inprod{\theta}{x^t})\big)
\end{displaymath}

\paragraph{Sparsity}


\subsection{Voter Model}

\subsection{Independent Cascade Model}

