Controllability of Bandlimited Graph Processes Over Random TimeVarying Graphs
Abstract
Controllability of complex networks arises in many technological problems involving social, financial, road, communication, and smart grid networks. In many practical situations, the underlying topology might change randomly with time, due to link failures such as changing friendships, road blocks or sensor malfunctions. Thus leading to poorly controlled dynamics if the randomness is not properly accounted for. We consider the problem of driving a random timevarying network to a desired state that is graphbandlimited with respect to the underlying topology, i.e., it has a sparse representation in the graph spectral domain. We first formulate the problem of sparse controllability on deterministic networks and exploit graph signal processing to obtain conditions on the number of required controlled nodes and the final control time. We then move on to random timevarying networks and develop the concept of controllability in the mean to control the expected system towards the desired state. A detailed mean squared analysis is performed to quantify the statistical deviation of a particular random realization of the network. Finally, different sparse control strategies are proposed and their effectiveness is evaluated on synthetic network models and social networks.
I Introduction
The controllability of complex networks has shown to play a fundamental role in our understanding of natural and technological systems. Relevant examples involve the control of social, biological, financial, road, communication, and smart grid networks. Several attempts have highlighted the importance of the network topology on the controllability of a system defined on that network [2, 3, 4] and that of considering sparse control signals [5, 6, 7]. Specifically, particular focus is put on designing adequate control signals as well as determining the minimal set of driving nodes [5]. Other approaches consider the spectral features of the transition matrix [6] and characterize the tradeoff between the number of nodes and the energy of the driving signals [7]. The obtained results have shown that sparse inhomogeneous networks are the most challenging to control [5], while clustered networks allow for an easier sparse control [7].
Although the above works provide seminal contributions on the controllability of complex networks, they ignore the nature of the desired state with respect to the underlying topology. Recent evidence from graph signal processing (GSP) [8, 9, 10] has shown that the coupling between the network topology and the signal (state) (a.k.a. the graph signal) can bring substantial benefits in graph signal sampling [11], interpolation [12, 13], adaptive reconstruction [14], and observability of network diffusion processes [15]. A common point that unifies [9, 10, 11, 12, 13, 14, 15] is the socalled graph Fourier transform (GFT). The GFT expresses the graph signal as a linear combination of the graph oscillating modes (obtained as the eigenvectors of the graph description matrix, such as the graph Laplacian).
A particular class of graph signals is that of bandlimited graph signals, i.e., signals that enjoy a sparse representation in the graph Fourier domain. Said differently, these are signals that can be written as a combination of only a few graph oscillating modes. Bandlimited graph signals are commonly observed in practice, such as in networks that exhibit a clustering behavior, i.e., where the signal has similar values within a cluster, but can have arbitrarily different intercluster values; or when the graph signal varies smoothly in adjacent vertices, such as temperature measurements in sensor networks.
Sparse network control in the GSP framework is considered in [16] to drive the network state towards a bandlimited graph signal. This is achieved by controlling the input on a fixed set of nodes and then percolating it through the graph by graph filters. The authors first determine a tradeoff between the final control time and the number of control vertices and then provide closedform conditions that enable full network control, where the latter relate the desired signal bandwidth and the graph filter coefficients.
Differently, [17] studied the practical challenge of driving the network towards a bandlimited state using control inputs of limited energy. Their main result is the established tradeoff between the number of controlled nodes and the control signal energy. However, conditions for full network control in this limited energy setting and relations with the desired signal bandwidth are not provided.
Along these lines, in parallel with the partial version of this paper [1], the GSP framework was also used in [18] to reformulate the specific linear quadratic controller over networks as an autoregressive moving average (ARMA) graph filter [19]. These graph filters allow then for the network control in the graph spectral domain, i.e., each graph oscillating mode is controlled independently and in parallel. However, sparse controllability in the vertex domain is not addressed.
Altogether, the above works concern network controllability on timeinvariant topologies. However, in practice, the network structure may change randomly over time due to link losses, or nodes that disappear with a given probability. This might be for instance the case for a communication link that due to channel noise is random, or when in smart grids, a power line/bus goes down due to local failures. In such situations, the network controllability derivations performed in a deterministic setting are not valid anymore and may drive the network to a completely different (undesired) state.
Motivated by the above observations, we study the possibilities to perform open loop control over random timevarying networks. By exploring GSP tools similar to [16, 17, 18], we propose a novel framework that involves the graph randomness into the analysis. More specifically, the main contributions are:

We study the problem of sparse controllability on deterministic networks and characterize the effect that the subset of driving nodes has on the graph frequency content of the controlled signal (Section III). We provide conditions on network controllability that relate the minimum number of driving nodes, the signal bandwidth, and the final control time. This result encompasses the three strategies proposed earlier in [16].

We formulate the problem of sparse network controllability over random timevarying graphs. We develop the concept of controllability in the mean and control the expected system evolution towards a bandlimited state w.r.t. the expected graph (Section IVA). We then extend the network controllability conditions derived in the deterministic case to the stochastic setting.

We perform a detailed mean squared analysis to quantify the statistical deviation of a particular realization w.r.t. the desired signal (Section IVB). This analysis illustrates the role played by the graph statistics, the signal bandwidth, and the control signal.

We propose two sparse control strategies to drive the expected state to the desired value with the minimum mean squared error deviation (Section V).

We evaluate the effectiveness of the developed framework and study its performance in different settings through examples from synthetic (ErdősRényi and geometric graphs) and realworld social networks (Facebook subnetwork and Zachary’s Karate Club) (Section VI).
To the best of our knowledge, this is the first contribution that approaches the controllability of network dynamics over random timevarying topologies. Although our findings are carried out from a GSP view angle, they can be extended to the general field of network control theory and can be further enriched from the latter bulk of literature.
The remaining part of the paper proceeds as follows: Section II sets down the preliminary concepts and Section III contains our formulation of sparse controllability on deterministic graphs. Section IV formulates the framework of sparse controllability on random graphs. Section V contains the developed control strategies, while Section VI presents the numerical experiments. Finally, Section VII provides the concluding remarks. All proofs are in the appendix.
Notation. Normal case letters (or ) are used for scalars, bold lowercase letters for vectors, and bold uppercase letters for matrices. The th entry of a vector is denoted as while the th entry of a matrix is . Superscripts and denote transpose and Hermitian, respectively. The null vector is while is the vector of all ones. The identity matrix of size is . The diagonal operator denoted as is defined such that with , and is a diagonal matrix with vector on the main diagonal. The expectation operator is denoted as , the trace operator as , the rank of as , the Kronecker product as , and the elementwise Hadamard product as . The vector norm as well as the corresponding matrix norm are denoted as . The ceiling operator is denoted as and the minimum and maximum operators as and , respectively. If not otherwise stated, calligraphic letters indicate sets and the set cardinality is denoted as .
Ii Diffusion Processes on Graphs
In this work, we consider the task of controlling a diffusion process on random timevarying graphs towards a desired state^{1}^{1}1To preserve the analogy with the control of linear systems, we will often interchange a desired value with a desired state.. To achieve this, we model diffusion processes in the framework of graph signal processing (GSP). We introduce the basic concepts of GSP in Section IIA, define the random timevarying graph model in Section IIB, and discuss diffusion processes on graphs in Section IIC.
Iia Graph signal processing (GSP)
Let denote a graph with the set of vertices, the edge set, and an edge weight function. This graph serves as a mathematical representation of the network, and it is concisely captured by the graph shift operator (GSO) matrix . The th element of , , is nonzero only if or if , so that respects the sparsity of . Standard choices for are the weighted graph adjacency matrix [20, 21], the graph Laplacian matrix [9], or their respective generalizations [22]. In what follows, we consider that is a normal matrix which implies that it has an eigendecomposition , where collects the orthonormal eigenvectors and contains the associated eigenvalues. This holds for every undirected graph (on which the graph Laplacian can be defined) and also for the adjacency matrix of some directed graphs [21, 23].
A graph signal is defined as a mapping from the vertex set to the field of real numbers, i.e., , for . The load charge on a smart grid network is one example of such graph signals [24]. We collect all node signals in the vector with being the th node value [9].
The graph Fourier transform (GFT) is defined as the projection of the graph signal on the eigenbasis and is denoted by [8, 9]. The elements denote the graph Fourier coefficients of , whereas the eigenvectors form the frequency basis. Likewise, the inverse GFT is defined as , i.e., it writes as a linear combination of the frequency basis functions weighted by the frequency coefficients.
A graph signal is said to be bandlimited if it has a sparse support in the graph frequency domain (i.e., it has only a few nonzero frequency coefficients). Without loss of generality, assume that the first elements of are nonzero, so we can write where , . Then, is written in the compact form
IiB Random timevarying graphs
We consider the following random graph model over time.
Definition 1 ( graph model [27]).
Given an underlying graph , a random edge sampling (RES) graph realization of consists of the same set of nodes and assumes the edge is sampled at time (i.e., ) with a probability . The edges are sampled independently over both the graph and the temporal dimension and are considered mutually independent from the graph signal if the latter has a stochastic nature.
In other words, Def. 1 states that is a graph realization drawn from the underlying graph , where the instantaneous edge set is generated via an independent Bernoulli process with probability . Let us from now on denote with , , and the adjacency matrix, the degree matrix, and the graph Laplacian matrix of and with , , and the respective matrices of . Furthermore, to ease the exposition let denote the expected graph with respective representation matrices , , and . Under the model it holds that , , and .
We further assume the following.
Assumption 1.
The GSO of the underlying graph has an upper bounded spectral norm as for some .
This assumption is generally met in practice and implies that the graphs of interest have finite dimension and finite edge weights.
We finally remark that more complex models than the can also be found in literature. In particular, for the model in which each edge is sampled independently with a different probability , many of the results presented in this work carry over as well. However, for clarity of exposition, we will focus only on the model.
IiC Diffusion on graphs from a GSP perspective
The continuoustime diffusion of a signal on a graph with Laplacian matrix is described by the differential equation [28, 29]
(2) 
This equation can be further discretized as [30]
(3) 
which is stable as long as satisfies . Alternatively, a diffusion on a graph can be interpreted as the discretetime shift of through the graph edges [20]
(4) 
Models (3) and (4) concern timeinvariant topologies. For random timevarying graphs we have that the transition matrix is also timevarying, i.e., . In general, we consider a diffusion process to be valid if its transition matrix satisfies the following assumption.
Assumption 2.
Let be the timevarying transition matrix of a diffusion process over a random timevarying graph . Then, it is assumed that and share the same eigenvectors.
That is, we consider diffusions on random graphs such that the eigenvectors of the expected transition matrix and the underlying GSO coincide. The following lemma shows that this is indeed the case for the diffusion models (3) and (4) on graph realizations.
Lemma 1.
Other models satisfying these conditions are the wave equation on graphs and graphbased ARMA models, see [15].
Iii Sparse Controllability on Deterministic Graphs
Consider the state linear system
(5) 
where denotes the state value on all nodes at time , is the control signal injected on nodes, and and are the state transition and control input matrix, respectively. System (5) captures the above graph diffusion models and will be used throughout the paper to sparsely control the network dynamics. In this context, we define sparse controllability as follows.
Definition 2 (Sparse network controllability).
An state system on a graph is sparsely controllable from nodes if for any initial state and some final time there exists a sequence of control signals operating on nodes that drive the network state to any desired value .
Put simply, system (5) is controllable if and only if the controllability matrix
(6) 
has full rank [31]. While the full rank of guarantees converging to any desired , we here focus on bringing the network state towards an arbitrary bandlimited graph state^{2}^{2}2As mentioned in the introduction section and shown by several of the cited works, the family of bandlimited graph signals is large. with . Lead by the promising results of bandlimited graph signal reconstruction by sampling a few nodes [25, 11, 26, 14, 13], we aim to transfer through a fixed, timeinvariant, set of nodes of cardinality . For the considered scenario, let denote the selection matrix, where is the binary matrix that selects the nodes in . More formally, belongs to the combinatorial set
(7) 
that selects out of different nodes. Observe that and with , such that if and only if node belongs to .
With this in place, we write the linear system on graphs (5) in the GFT domain as
(8) 
where holds from Assumption 2. Then, by splitting (8) into its low and high graph frequency bands, we write
(9) 
where , and .
Recursion (9) leads to two main observations. First, given a controllable tuple , we can drive to any desired signal through the input signal . Second, the presence of in (9) implies that it is not possible to keep this system evolving within the subspace of bandlimited graph signals, even if we restrict ourselves to bandlimited control signals. Nevertheless, the latter is not an issue, since we can still transfer to a fixed such that and then filter out the spurious frequency content of to obtain the desired signal . In other words, by denoting , we focus only on the dynamics of the Fourier coefficients of interest
(10) 
to control the state towards a such that . Then, we use the filter that selects the desired graph frequencies to obtain the controlled bandlimited signal . We note that filter can be implemented locally with a polynomial on the GSO of degree at most [32].
Hereinafter, the design variables are the sampling matrix and the control signals , . Additionally, since the initial state is considered known, we assume without loss of generality that , which is common practice in the control literature [17, 31, 33]. With this set down, we claim our first contribution, which will also be exploited in Section IV for the sparse controllability on random graphs.
Proposition 1.
Consider the linear system (10) describing a process over a deterministic graph . A necessary condition to control the system towards the bandlimited state is that nodes should be selected to inject the control signal.
Proposition 1 provides only a necessary condition on the minimum number of nodes required to control (10) in instants. However, this is not sufficient since the system controllability is affected by the nodes’ position w.r.t. the graph. Still, this condition shows the tradeoff between the cardinality of the sampling set , the signal bandwidth , and the control time . Therefore, for there is the potential to control the network by acting only on one single node. In what follows, we show how this result relates to [16] and [17].
a) Relation with [16]. The result of Proposition 1 encompasses under one single condition the three graph signal reconstruction strategies introduced in [16]. In fact, and covers the multiple nodesingle time seeding strategy. The case and is a necessary condition for the single nodemultiple time seeding strategy to control the graph signal. Finally, covers the more involved multiple nodemultiple time seeding approach.
b) Relation with [17]. Differently from this work, [17] focuses on designing the control signal as a tradeoff between sparsity in the vertex domain and the signal energy. Specifically, this problem writes as
(11)  
subject to  
where the constant trades the energy of the control signal with its sparsity . Even though (11) might sometimes lead to a sparse , it does not lead to a fixed set of control nodes. In this regard, the results of Proposition 1 impose a minimum dimension on such that controllability is possible for a fixed set .
Iv Sparse Controllability on Random Graphs
In this section, we develop our theory for controlling dynamics over random timevarying networks. In Section IVA we introduce the concept of controllability in the mean and in Section IVB we perform a mean squared analysis.
Iva Mean controllability
The dynamics of a timevarying system on random graphs are given by
(12) 
where, under the model in Definition 1), is a set of i.i.d. random matrices with . From Assumption 2, it further holds that . Moreover, the design variables are contained in the second term of (12), which is deterministic, and the state depends on the random system matrices , which are independent from , as well as the deterministic design variables and . Therefore, the mean evolution of (12) writes as
(13) 
where . Note that (13) is a deterministic, timeinvariant system analogous to (5). We then define the following.
Definition 3 (Sparse network controllability in the mean).
Our goal then is to control the mean system to a desired bandlimited graph signal in a finite time from a few nodes, by designing both and the set (through matrix ). The above translates into controlling the mean system (analogous to (10))
(14) 
and later applying a (deterministic) linear filter to keep only the desired frequencies such that .
Then, similarly to Proposition 1, we claim the following.
Proposition 2.
Similar to Proposition 1, the above result establishes a necessary condition for controlling a linear system, now, on random timevarying graphs. As such, the same tradeoff between , , and applies here. The following corollary shows that this result extends to a sufficient condition for particular graphs.
Corollary 1.
We remark that algorithms for finding a set of linearly independent rows of a matrix are readily available, see [11].
IvB Mean squared analysis
Since system (12) is only controlled in the mean, it is paramount to quantify the mean squared error (MSE) of the controlled state to gain statistical insight into how close a specific realization is from the actual desired signal . Towards this end, define as the state transition matrix between time instants . The following theorem then determines the MSE.
Theorem 1.
Let Assumptions 1 and 2 hold and let be a graph process defined over a sequence of graphs described by the linear system (12). Given a set of control nodes characterized by selection matrix and a set of control signals with initial state , the MSE of the particular realization from the actual desired signal is
(15) 
which is a quadratic form on with coefficients , and .
We observe that, given and , the MSE (15) holds for any system described by (12), irrespective of controllability. The MSE (15) is a quadratic function in the design variables and the corresponding coefficients , , and depend all on known quantities; specifically: the graph filter , the desired state , and the statistics (first and second order moments) of the underlying support through and . Note that (15) further highlights the impact that the controlled nodes have on the overall performance, and shows its connection with the underlying support and the process bandwidth. More precisely, the coefficient provides the MSE floor if there is no control signal, given by the energy of the desired state; takes into account the similarity between the desired signal and the evolution of the sparsely controlled signal over the mean graph; and accounts for the impact of the variability of the random graph. Finally, we note that the computation of in (15) might be cumbersome for some graph shift operators (or transition matrices ). We thus provide in the appendix two practically useful results that address this issue (first, we provide a general upper bound; second, we show how to exactly compute for undirected graphs following the diffusion models in Lemma 1).
V Sparse Control Strategies
In this section, we propose sparse control strategies (i.e., find and ) for graph processes over random timevarying graphs, where depending on the scenario, one can be preferred over the others. Since we work now in a statistical framework, in Section VA we propose an unbiased control strategy, while in Section VB we introduce another control strategy that works in a biasvariance tradeoff mode, minimizing the MSE.
Va Unbiased controller
The mean state (14) for can be written in the expanded form
(16) 
where and . For an unbiased controller, it must hold that at final time . Combining (16) and , we obtain
(17) 
with , where we recall that , . For being of full rank (i.e. a controllable system), the linear system (17) has infinite solutions on . Moreover, it often happens that there exists more than one set of nodes that guarantees controllability. We can then select the set of nodes and design the control signals that minimizes the , while guaranteeing that the solution is unbiased. Let be the set of selection matrices that satisfy controllability. The optimal unbiased control strategy can then be posed as
(18)  
s. t.  
where is given in (15). Oftentimes, we are interested in controlling the system with minimum energy [7, 17]. In such cases, we observe that the minimal energy control signal is [34]
(19) 
Then, within the minimal energy control framework, we can select the nodes that minimize the as follows
(20)  
s. t.  
Problem (20) is highly nonconvex due to the binary nature of the optimization variable . A heuristic solution to this problem is to follow a constrained greedy approach, as described in Algorithm 1. The objective is to greedily select the nodes that improve the while satisfying the controllability constraint. More specifically, for each new node that we might potentially add to the selected set , we need to check that the rank keeps increasing until we reach controllability as indicated by line 11 [cf. Proposition 2]. Since we are looking for the minimal energy controller, then line 12 entails computing if , and (19) if . While we observe that for such a constrained greedy approach there are no theoretical guarantees [35], in simulations run in Section VI, Algorithm 1 exhibits a very good performance.
VB Biased controller
In cases where the requirement for an unbiased controller is not strict, we can leverage the biasvariance tradeoff to further reduce the mean squared error of the controlled state, with respect to the desired state. Given a fixed sampling set , the (15) is a quadratic function on the control signals . Therefore, we proceed by expressing as a function of as follows.
First, the derivative of (15) with respect to is
(21) 
for with and given in (15). By defining ,
(22) 
and setting (21) to zero, we can write
(23) 
with and . Note that by construction has rank since . Given then such that , is nonsingular leading to the (parameterized) minimum control signals
(24) 
From the above relation between the control signal and , we consider a twostage optimization approach [36, Section 4.1.3] to find that minimizes the . This optimization problem writes as
(25)  
s. t.  
To deal with the nonconvexity of (25), similarly to (20), we again rely on a constrained greedy approach analogous to Algorithm 1. More specifically, we replace line 10 by computation of and , condition on line 11 now becomes and line 12 is replaced by (24).
Vi Numerical Experiments
We evaluate the proposed control strategies on different scenarios to analyze the different tradeoffs between the parameters in controlling the network. We implement the unbiased minimal energy controller (20) and the biased controller (25), and compare them with the control strategies of [16], named Percolation, and [17], named Min. Energy. In the next section, we consider synthetic network models, namely ErdősRényi (ER) graphs [37] and geometric graphs, while in Section VIB we focus on realworld graphs given by Zachary’s Karate Club network [38] and a Facebook subnet [39].
Via Synthetic network models
The ER graph assumes that edges between any two nodes are drawn randomly and independently with probability and has an average degree of . For the geometric model, we first draw nodes uniformly at random in the plane and then compute the Euclidean distance between any pair of nodes, where is the position of node . Subsequently, we assign Gaussian kernel edge weights and finally keep only the nearest neighbors per node. The parameter controls the average degree of the graph. For both models, we only consider realizations that result in connected graphs. To account for the randomness in the generative models as well as in the edge loss, we average the performance over different graphs where for each of them we further accounted for RES realizations.
Unless otherwise specified, we set , and , and the RES link loss probability to . The control time is , and the number of controlled nodes is . The desired state has a bandwidth of with GFT coefficients decaying linearly as for . The controllability performance is measured as the normalized MSE between the bandlimited controlled state and the desired one, i.e., .
Timeinvariant network. To set a baseline, we first compare the biased (25) and unbiased (20) controllers with the percolation [16] and min. energy [17] strategies on a fixed timeinvariant network. This is the same control scenario that is considered in [16, 17], and is equivalent to setting . Results for geometric graphs can be found in Figure 1, where Figure 0(a) is a parametric simulation as a function of time horizon and Figure 0(b) is a parametric simulation as a function of the number of samples. In general, we observe that the biased estimator has a performance similar to the min. energy strategy, and both are slightly better than the percolation strategy. The unbiased controller lags behind in terms of .
Graph connectivity. In the first random timevarying experiment, we study the impact of the graph connectivity on the controllability performance. We account for the graph connectivity by changing the average degrees, i.e., for the ER graph and in the geometric graph. Figure 2 shows the MSE as the connectivity increases. From Figure 1(a), we observe that the ability to control the geometric network improves with the average degree. This is intuitively satisfying since larger degrees lead to higher connectivity between nodes and thus, render them more robust to the effect of the RES model. Contrarily, for the ER model in Figure 1(b) this behavior is not as much emphasized. We attribute this phenomenon to the large average degree of the ER graphs (above ) and to the relatively high value of . That is, the loss of a few edges does not impact the overall ability to control the network.
Link loss. In the second experiment, we analyze the impact of for a fixed graph average degree. From Figure 3, we note that as increases, i.e., fewer links are lost, the MSE reduces leading to an easier to control network for both models. This is because a higher yields realizations with fewer edge losses, thus more similar to the underlying (mean) graph.
Control time. In the third and last experiment, we analyze the impact of the control time horizon . From Figure 4, we observe that the proposed strategies are not significantly affected by changes in (they only improve slightly).
From this set of experiments, we make three key observations. First, the proposed strategies offer the best performance. Second, the biased controller outperforms the unbiased one. This is an intuitively expected result since it levers the biasvariance tradeoff to minimize the overall MSE, while the unbiased controller has the additional constraint of zero bias and minimum energy (since we are solving problem (20)). By combining these two remarks, we also note that the biased controller strategy achieves the lowest MSE. Third, not accounting for the graph randomness seriously affects the performance, even for . In fact, the competing alternatives of Percolation and Min. Energy that do not account for the graph randomness have a performance that is worse by orders of magnitude than the proposed techniques. This contrast is particularly evident when comparing with the simulations for a timeinvariant network in Figure 1. This could be explained by the fact that losing a link has a huge impact in the topology of the graph and severely affects the eigenbasis, thereby changing the subspace of signals that are bandlimited on a given graph.
ViB Real world graphs
We here consider two social networks, namely Zachary’s Karate Club [38] composed of nodes and a Facebook subnetwork [39] having nodes. The situation is akin to the spread of a rumor, and thus the control actions aim at curbing this information spread towards the desired state from a few selected persons.
We set the control time horizon to , the number of selected nodes to , and . For simplicity of presentation, we focus here only on the biased controller strategy which has consistently yielded the best performance. We again averaged the performance over different realizations.
Bandwidth and spectrum of the desired state. In this experiment, we analyze the impact that the desired state bandwidth and its GFT have on the controllability performance. We consider four different GFTs for the desired state, namely: (i) a step lowpass for , (ii) a step highpass, where the active frequencies correspond to the eigenvectors with highest total variation, (iii) a linear decay response given by for , and (iv) an exponential decay response with for . We normalize all desired states to have unit energy for fair comparison and analyze different values of , which are set to be proportional to (a fraction between and ). The results are depicted in Figure 5. First, we observe that controlling the system to a state with a higher bandwidth is harder since the set of graph frequencies that we need to guarantee controllability increases. Second, we observe that the highpass response is harder to achieve and that responses that decay to zero (like the linear decay and the exponential decay) yield lower MSE. This is because highpass responses are translated in the vertex domain as states that have dissimilar values in adjacent nodes, therefore, rendering the control of network dynamics to such a state more challenging.
Sampling heuristics. In the last experiment, we focus on the impact of the control nodes. In particular, we compare the proposed constrained greedy heuristic for choosing the nodes (Algorithm 1) with the optimal combinatorial solution and a uniformly random sampling scheme. We fix and consider the linear decay desired state . The obtained results are shown in Figure 6. First, we observe that the MSE decreases as more control nodes are selected. Second, we note that the greedy heuristic yields a performance similar to the optimal solution and represents a considerable improvement over the random selection.
Vii Conclusions
In this paper, we studied the problem of sparse controllability of graph signals. We considered a random timevarying network to be controlled to a desired graphbandlimited state. To address the randomness of the underlying support, we introduced the concept of controllability in the mean, where we postulate to control the system as if it were running on the expected graph. We then carried out a detailed mean squared analysis to quantify the deviation from the desired signal, when the control is designed for the expected graph but ran on any given realization of the random network. We used this analysis to propose two different control design strategies and evaluated their performance on both synthetic graph models and realworld social networks. We concluded that it is of paramount importance to take into account the random nature of the underlying topology. We leave as future work the analysis of more complex random network models, as well as other control strategies involving spectral or energetic constraints. Another direction worth investigating is the proposal of other heuristic solutions to the respective optimization problems.
Appendix A Proof of Lemma 1.
Proof.
For model , and the system transition matrix is for . First, we prove that . Note that for every and, therefore, from the Laplacian interlacing property [40] this condition always hold. The proof of Assumption 2 is straightforward, i.e., from , which means that and share the same eigenvectors. For the last condition, note that since . Therefore, is upper bounded by some finite .
For model , and the system transition matrix is . To prove that , recall that for connected graphs, the largest eigenvalue is positive and real [41]. Then, since is considered to be normal and Assumption 1 holds, . Likewise, since , then and therefore for all . The proofs of the last two conditions are straightforward since and . This completes the proof. ∎
Appendix B Proof of Proposition 1 and Corollary 1
Proof of Proposition 1.
Recall that is the set of the selected nodes and that , where with if and , otherwise. System (10) is equivalent to
(26) 
where denotes the zeroextended control signal such that if and , otherwise. Then, system (26) is controllable iff the matrix
(27) 
is full rank. Observe that
(28) 
holds from . Therefore, to ensure the full rank of , must hold, for some . This concludes the proof. ∎