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Rejection-Based Simulation of Stochastic Spreading Processes on Complex Networks

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Rejection-Based Simulation of Stochastic Spreading Processes on Complex Networks

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Stochastic processes can model many emerging phenomena on networks, like the spread of computer viruses, rumors, or infectious diseases. Understanding the dynamics of such stochastic spreading processes is therefore of fundamental interest. In this work we consider the wide-spread compartment model where each node is in one of several states (or compartments). Nodes change their state randomly after an exponentially distributed waiting time and according to a given set of rules. For networks of realistic size, even the generation of only a single stochastic trajectory of a spreading process is computationally very expensive. Here, we propose a novel simulation approach, which combines the advantages of event-based simulation and rejection sampling. Our method outperforms state-of-the-art methods in terms of absolute run-time and scales significantly better, while being statistically equivalent.
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Rejection-Based Simulation of Stochastic
Spreading Processes on Complex Networks
Gerrit Großmann( )[000000024933447X]and Verena Wolf
Saarland University, 66123 Saarbr¨ucken, Germany
mosi.cs.uni-saarland.de
{gerrit.grossmann,verena.wolf}@uni-saarland.de
Abstract. Stochastic processes can model many emerging phenomena
on networks, like the spread of computer viruses, rumors, or infectious
diseases. Understanding the dynamics of such stochastic spreading pro-
cesses is therefore of fundamental interest. In this work we consider the
wide-spread compartment model where each node is in one of several
states (or compartments). Nodes change their state randomly after an
exponentially distributed waiting time and according to a given set of
rules. For networks of realistic size, even the generation of only a sin-
gle stochastic trajectory of a spreading process is computationally very
expensive.
Here, we propose a novel simulation approach, which combines the ad-
vantages of event-based simulation and rejection sampling. Our method
outperforms state-of-the-art methods in terms of absolute runtime and
scales significantly better while being statistically equivalent.
Keywords: Spreading Process ·SIR ·Epidemic Modeling ·Monte-Carlo
Simulation ·Gillespie Algorithm
1 Introduction
Computational modeling of spreading phenomena is an active research field
within network science with many applications ranging from disease prevention
to social network analysis [1–6]. The most widely used approach is a continuous-
time model where each node of a given graph occupies one of several states
(e.g. infected and susceptible) at each point in time. A set of rules determines
the probabilities and random times at which nodes change their state depending
on the node’s direct neighborhood (as determined by the graph). The application
of a rule is always stochastic and the waiting time before a rule “fires” (i.e. is
applied) is governed by an exponential distribution.
The underlying stochastic dynamics are given by a continuous-time Markov
chain (CTMC) [6–9]. Each possible assignment from nodes to local node states
constitutes an individual state of the CTMC (here referred to as CTMC state or
network state to avoid confusion with the local state of a single node). Hence, the
corresponding CTMC state space grows exponentially in the number of nodes,
which renders its numerical solution infeasible.
2 Großmann et al.
As a consequence, mean-field-type approximations and sampling approaches
have emerged as the cornerstones for their analysis. Mean-field equations orig-
inate from statistical physics and provide typically a reasonably good approx-
imation of the underlying dynamics [10–14]. Generally speaking, they propose
a set of ordinary differential equations that model the average behavior of each
component (e.g., for each node, or for all nodes of a certain degree). However,
mean-field approaches only give information about the average behavior of the
system, for example, about the expected number of infected nodes for each de-
gree. Naturally, this restricts the scope of their application. In particular, they
are not suited to answer specific questions about the system.
For example, one might be interested in finding the specific source of an
epidemic [15, 16] or wants to know where an intervention (e.g. by vaccination) is
most successful. [17–20].
Consequently, stochastic simulations remain an essential tool in the compu-
tational analysis of complex networks dynamics. Different simulation approaches
for complex networks have been suggested, which can all be seen as adaptations
of the Gillespie algorithm (GA) [6]. Recently, a more efficient extension of the
GA has been proposed, called Optimized GA (OGA) [21]. A rejection step is
used to reduce the number of network updates.
Here, we propose an event-driven simulation method which also utilizes re-
jection sampling. Our method is based on an event queue which stores infection
and curing events. Unlike traditional methods, we ensure that it is not necessary
to iterate over the entire neighborhood of a node after it has changed its state.
Therefore, we allow the creation of events which are inconsistent with the cur-
rent CMTC state. These might lead to rejections when they reach the beginning
of the queue. We introduce our method for the well-known SIS (Susceptible-
Infected-Susceptible) model and show that it can easily be generalized for other
epidemic-type processes. Code will be made available.1
We formalize the semantics of spreading processes in Section 2 and explain
how the CTMC is constructed. Previous simulation approaches, such as GA and
OGA, are presented in Section 3. In Section 4 we present our rejection sampling
algorithm and discuss to which extend our method is generalizable to different
network models and spreading models. We demonstrate the effectiveness of our
approach on three different case studies in Section 5.
2 Stochastic Spreading Processes
Let G= (N,E) be a an undirected, unweighted, finite graph without self-loops.
We assume the edges are tuples of nodes and that (n1, n2)∈ E always implies
(n2, n1)∈ E. At each time point tR0each node occupies one out of m(local)
states (also called labels or compartments), denoted by S={s1, s2, . . . , sm}.
Consequently, the (global) network state is fully specified by a labeling L:N →
S. We use L={L|L:N → S} to denote all possible network states. As each
1github.com/gerritgr/Rejection-Based-Epidemic-Simulation
Rejection-Based Simulation of Stochastic Processes on Networks 3
of the |N| nodes occupies one of mstates, we know that |L| =m|N |. Nodes
change their state by the application of a stochastic rule. A node’s state and its
neighborhood determine which rules are applicable to a node and the probability
density of the random delay until a rule fires. If several rules can fire, the one
with the shortest delay is executed.
We allow two types of rules: node-based (independent, spontaneous) rules
and edge-based (contact, spreading) rules. The application of a node-based rule
Aµ
B results in a transition of a node from state A ∈ S to state B ∈ S (A6=B)
with rate µR>0. That is, the waiting time until the rule fires is governed
by the exponential distribution with rate µ. An edge-based rule has the form
A+C λ
B + C, where A,B,C∈ S,A6= B, λ R>0. Its application changes
an edge (more precisely, the state of an edge’s node). It can be applied to each
edge (n, n0)∈ E where L(n) = A, L(n0) = B. Again, the node in state A changes
after a delay that is exponentially distributed with rate λ. Note that, if a node in
state A has more than one direct B-neighbor, it is “attacked” independently by
each neighbor. Due to the properties of the exponential distribution, the rate at
which a node changes its state according to a certain contact rule is proportional
to the number of neighbors which induce the change.
SIS Model In the sequel, we use the well-known Susceptible-Infected-Susceptible
(SIS) model as a running example. Consider S={I,S}and the rules:
S+I λ
I + I I µ
S.
In the SIS model, infected nodes propagate their infection to neighboring
susceptible nodes using an edge-based rule. Thus, only susceptible nodes with
at least one infected neighbor can become infected. Infection of a node occurs
at a rate that increases proportionally with the number of infected neighbors.
Infected nodes can, independently from their neighborhood, recover (i.e. become
susceptible again) using a node-based rule.
3 Previous Approaches
In this section, we shortly revise techniques that have been previously suggested
for the simulation of SIS-type processes. For a more comprehensive description,
we refer the reader to [6, 21].
3.1 Standard Gillespie Algorithm
The Standard Gillespie Algorithm (here, simply referred to as GA) is also known
as Gillespie’s direct method and a popular method for the simulation of coupled
chemical reactions. Its adaptation to complex networks uses as key data struc-
tures two lists which are constantly updated: a list of all infected nodes (denoted
by LI) and a list of all S–I edges (denoted by LSI).
In each simulation step, we first draw an exponentially distributed delay for
the time until the next rule fires. That is, instead of sampling a waiting time for
4 Großmann et al.
each rule and each position where the rule can be applied, we directly sample
the time until the network state changes. For this, we compute an aggregated
rate c=µ|LI|+λ|LSI|. Then we randomly decide if an infection or a cur-
ing event is happening. The probability of the latter is proportional to its rate,
i.e. 1
cµ|LI|, and thus, the probability of an infection is 1
cλ|LSI|. After that we
pick an infected node (in case of a curing) or an S–I edge (in case of an infec-
tion) uniformly at random. We update the two lists accordingly. The expensive
part in each step is keeping LSIupdated. For this, we iterate over the whole
neighborhood of the node and for each susceptible neighbor we remove (after a
curing) or add (after an infection) the corresponding edge to the list. Thus, we
need one add/remove operation on the list for each susceptible neighbor.
Note that there are different possibilities to sample the node that will become
infected next. Instead of keeping an updated list of all S–I edges one can also
use a list of all susceptible nodes. In that case, we cannot sample uniformly but
decide for the infection of a susceptible node with a probability proportional to
its number of infected neighbors.
Likewise, we can randomly pick the starting point of the next infection by
only considering LI. To generate an infection event, we first sample an infected
node from this list and then we (uniformly) sample a susceptible neighbor, which
becomes infected. Since infected nodes with many susceptible neighbors have a
higher probability of being the starting point of an infection (i.e., they have more
S–I edges associated with them), we sample from LIsuch that the probability of
picking an infected node is proportional to its number of susceptible neighbors.
All three approaches are statistically equivalent but the last one motivates
the Optimized Gillespie Algorithm (OGA) [21].
3.2 Optimized Gillespie Algorithm
As discussed earlier, sampling from LIis expensive because Updating this in-
formation for all elements of LIis costly because after each event, the number
of susceptible neighbors may change for many nodes.
In [21] Cota and Ferreira suggest to sample nodes from LIwith a probability
that is proportional to the degree kof a node, which is an upper bound for the
maximal possible number of susceptible neighbors. Then they uniformly choose
a neighbor of that node and update the global clock. If this neighbor is already
infected they reject the infection event, which yields a rejection probability of
kkS
kif kSis the number of susceptible neighbors. Note that the rejection prob-
ability exactly corrects for the over-approximation of using kinstead of kS. This
is illustrated in Fig. 1.
Compared to the GA, updating the list of infected nodes becomes cheaper,
because only the node which actually changes its state is added to (or removed
from) LI. The sampling probabilities of the neighbors remain the same because
their degree remains the same. On the other hand, sampling of a node is more
expensive compared to the GA where we sample edges uniformly.
Naturally, the speedup in each step comes at the costs (of a potentially
enormous amount) of rejection events. Even a single infected node with many
Rejection-Based Simulation of Stochastic Processes on Networks 5
3
4
2
1
5
3
4
2
1
5
LI13
kS23
k34
\begin{center}
\begin{tabular}{ c| c c }
$\mathcal{L}_I$ & 1 & 3 \\
\hline
$k_S$ & 2 & 3 \\
$k$ & 3 & 4 \\
\end{tabular}
\end{center}
LI134
kS121
k343
infection
Fig. 1: Example of an infection event. We sample from LIproportional to kS.
Alternatively, we can weight according to the number of neighbors kwhich is
constant and over-approximates kS. To correct for the over-approximation we
reject a sample with probability kks
k.
infected but few susceptible neighbors will continuously lead to rejected events.
This is especially problematic in cases with many infected nodes and no or
very few susceptible neighbors which therefore make rejections many orders of
magnitude more likely than actual events. Therefore, in [21] the authors propose
the algorithm for simulations close to the epidemic threshold, where the number
of infected nodes is typically very small.
Note that, to sample a node, Cota and Ferreira also propose rejection sam-
pling based on the maximal degree. However, St-Onge et al. point out that in
the case of heterogeneous networks a binary tree can be used to speed up this
step significantly. Specifically, this allows them to derive an upper-bound for the
rejecting probability. [22]. This, however, does not overcome the fundamental
limitation of the OGA approach regarding models with a large fraction of in-
fected nodes. That is, where infected nodes are mostly surrounded by infected
nodes causing most infections to be rejected.
3.3 Event-Based Simulation
In the event-driven approach, the primary data structure is an event queue, in
which events are sorted and executed according to the time points at which they
will occur. This eliminates the costly process of randomly selecting a node for
each step (popping the first element from the queue has constant time com-
plexity). Events are either curing of a specific node or infection via a specific
edge. Moreover, it is easy to adapt the event-driven approach to rules with
non-Markovian waiting times or to a network where each node has individual
recovery and infection rates [6]. Event-based simulation of an SIS process is
done as follows: For the initialization, we draw for each node an exponentially
distributed time until recovery with rate µand add the respective curing event
to the queue. Likewise, for each susceptible node with at least one infected
neighbor we draw an exponentially distributed time until infection with rate
λדNumber of infected neighbors”. We add the resulting events to the queue.
During the simulation, we always take the earliest event from the queue,
change the network accordingly and update the global clock. If the current event
6 Großmann et al.
is the infection of a node, the infection rates of its susceptible neighbors increase.
Thus, it is necessary to iterate over all neighbors of the corresponding node, draw
renewed waiting times for their infection events, and update the event queue
accordingly. Although efficient strategies have been suggested [6], these queue
updates are rather costly.
Since each step requires an iteration over all neighbors of the node under
consideration, the worst-case runtime depends on the maximal degree of the
network. Moreover, for each neighbor, it might be necessary to reorder the event
queue. The time complexity of reordering the queue depends (typically logarith-
mically) on the number of elements in the queue and adds significant additional
costs to each step. Note that trajectories generated using the event-driven ap-
proach are statistically equivalent to those generated with the GA because all
delays are exponentially distributed and thus have the memoryless property. A
variant of this algorithm can also be found in [23].
4 Our Method
In this section, we propose a method for the simulation of SIS-type processes.
The key idea is to combine an event-driven approach with rejection sampling
while keeping the number of rejections to a minimum. We will generalize the
algorithm for different epidemic processes as well as for weighted and temporal
networks. First, we introduce the main data structures:
Event queue. It stores all future infection and curing events generated so far.
Each event is associated with a time point and with the node(s) affected by the
event. Curing events contain a reference to the recovering node and infection
events to a pair of connected nodes, an infected (source) node and a susceptible
(target) node.
Graph. In this graph structure, each node is associated with its list of neighbors,
its current state, a degree, and, if infected, a prospective recovery time.
We also keep track of the time in a global clock. We assume that an initial
network, a time horizon (or another stopping criterion), and the rate parameters
(µ, λ) are given as input. In Alg. 1-4 we provide pseudocode for the detailed steps
of the method.
Initialization Initially, we iterate over the network and sample a recovery time
(exponentially distributed with rate µ) for each infected node (cf. Line 2, Alg.
1). We push the recovery event to the queue and annotate each infected node
with its recovery time (cf. Line 5, Alg. 2). Next, we iterate over the network a
second time and generate an infection event for each infected node (cf. Line 5,
Alg. 1). The procedure for the generation of infection events is explained later.
In Alg. 1 we need two iterations because the recovery time of each infected
node has to be available for the infection events. These events identify the earliest
infection attempt of each node.
Rejection-Based Simulation of Stochastic Processes on Networks 7
Iteration The main procedure of the simulation is illustrated in Alg. 4. We
schedule events until the global clock reaches the specified time horizon (cf. Line
9). In each step, we take the earliest event from the queue (Line 7) and set the
global clock to the event time (Line 8). Then we “apply” the event (Line 11-20).
In case of a recovery event, we simply change the state of the corresponding
node from I to S and are done (Line 12). Note that we always generate (exactly)
one recovery event for each infected node, thus, each recovery event is always
consistent with the current network state. Note that the queue always contains
exactly one recovery event for each infected node.
If the event is an infection event, we apply the event if possible (Line 14-
18) and reject it otherwise (Line 19-20). We update the global clock either way.
Each infection event is associated with a source node and a target node (i.e., the
node under attack). The infection event is applicable if the current state of the
target node is S (which might not be the case anymore) and the current state of
the source node is I (which will always be the case). After a successful infection
event, we generate a new recovery event for the target node (Line 16) and two
novel infection events, one for the source node (Line 17) and one for the target
node which is now also infected (Line 18). If the infection attempt was rejected,
we only generate a novel infection event for the source node (Line 20). Thus, we
always have exactly one infection event in the queue for each infected node.
Generating Infection Events The generation of infection events and the
distinction between unsuccessful and potentially successful infection attempts is
an essential part of the algorithm.
In Alg. 3, for each infected node we only generate the earliest infection at-
tempt and add it to the queue. Therefore, we first sample the exponentially
distributed waiting time with rate , where kis the degree of the node, and
compute the time point of infection (Line 5). If the time point of the infection
attempt is after its recovery event, we stop and no infection event is added to
the queue (Lines 6-7). Note that in the graph structure, each node is annotated
with its recovery time (node.recovery time) to have it immediately available.
Next, we uniformly select a random neighbor which will be attacked (Line
8). If the neighbor is currently susceptible, we add the event to the event queue
and the current iteration step ends (Lines 9-12).
If the neighbor is currently infected we check the recovery time of the neighbor
(Line 9). If the infection attempt happens before the recovery time point, we
already know that the infection attempt will be unsuccessful (already infected
nodes cannot become infected). Thus, we perform an early reject (Lines 10-12 are
not executed). That is, instead of pushing the surely unsuccessful infection event
to the queue, we directly generate another infection attempt, i.e. we re-enter the
while-loop in Lines 4-12. We repeat the above procedure until the recovery time
of the current node is reached or the infection can be added to the queue (i.e. no
early rejection is happening).
Fig. 3 provides a minimal example of a potential execution of our method.
8 Großmann et al.
Algorithm 1 Graph Initialization
1: procedure InitGraph(G,µ,λ,Q)
2: for each node in Gdo
3: if node.state = I then
4: GenerateRecoveryEvent(node, µ, 0, Q)
5: for each node in Gdo .recovery times are available now
6: if node.state = I then
7: GenerateInfectionEvent(node, λ, 0, Q)
Algorithm 2 Generation of a Recovery Event
1: procedure GenerateRecoveryEvent(node, µ,tglobal ,Q)
2: tevent =tglobal + draw exp(µ)
3: e = Event(src node = node, t=tevent, type=recovery)
4: node.recovery time = tevent
5: Q.push(e)
Algorithm 3 Generation of an Infection Event
1: procedure GenerateInfectionEvent(node, λ,tglobal,Q)
2: tevent =tglobal
3: rate = λnode.degree
4: while true do
5: tevent += draw exp(rate)
6: if node.recovery time < tevent then .no event is generated
7: break
8: attacked node = draw uniform(node.neighbor list)
9: if attacked node.state = S
or attacked node.recovery time < tevent then .check for early reject
10: e = Event(src node=node, target=attacked node,
time=tevent, type=infection)
11: Q.push(e) .was successful
12: break
Algorithm 4 SIS Simulation
Input: Graph (G) with initial states, time horizon (h), recovery rate (µ), infection rate (λ)
Output: Graph at time h . or any other measure of interest
1: Q=emptyQueue() .sorted w.r.t. time
2: InitGraph(G,µ,λ,Q)
3: tglobal = 0
4: while true do
5: if Q.is empty() then
6: break
7: e = Q.pop()
8: tglobal = e.time
9: if tglobal > h then
10: break
11: if e.type = recovery then
12: G[e.src node].state = S
13: else
14: if G[e.target node].state = S then
15: G[e.target node].state = I
16: GenerateRecoveryEvent(e.target node, µ,tglobal ,Q)
17: GenerateInfectionEvent(e.src node, λ,tglobal,Q)
18: GenerateInfectionEvent(e.target node, λ,tglobal,Q)
19: else .late reject
20: GenerateInfectionEvent(e.src node, λ,tglobal,Q)
Fig. 2: Pseudocode for our event-based rejection sampling method.
Rejection-Based Simulation of Stochastic Processes on Networks 9
3 42
1
5
t Event
0.4 Infection Edge: 3 4
0.5 Recovery Node: 4
1.6 Recovery Node: 1
1.7 Recovery Node: 3
t Event
0.4 Infection Edge: 3 4
0.5 Recovery Node: 4
0.7 Infection Edge: 1 2
1.6 Recovery Node: 1
1.7 Recovery Node: 3
t Event
0.9 Infection Edge: 4 5
3 42
1
5
t Event
1.6 Recovery Node: 1
1.7 Recovery Node: 3
t Event
0.3 Infection Edge: 1 4
0.4 Infection Edge: 3 4
1.6 Recovery Node: 1
1.7 Recovery Node: 3
t Event
0.1 Infection Edge: 1 3
(a)
(b)
3 42
1
5
t Event
0.4 Infection Edge: 3 4
0.5 Recovery Node: 4
0.7 Infection Edge: 1 2
1.6 Recovery Node: 1
1.7 Recovery Node: 3
t Event
0.5 Recovery Node: 4
0.6 Infection Edge: 3 4
0.7 Infection Edge: 1 2
1.6 Recovery Node: 1
1.7 Recovery Node: 3
3 42
1
5
t Event
0.6 Infection Edge: 3 4
0.7 Infection Edge: 1 2
1.6 Recovery Node: 1
1.7 Recovery Node: 3
(c)
(d)
Fig. 3: First four steps of the our method for a toy example (I: red, S: blue): (a)
Initialization, generate the recovery events (left queue), and infection event for
each infected node (right queue). The first infection attempt from node 1 is an
early reject. (b) The infection from 1 to 4 was successful, we generate a recovery
event for 4 and two new infection events for 1 and 4. The infection event of node
4 is directly rejected because it happens after its recovery. (c) (Late) Reject of
the infection attempt from 3 to 4 as 4 is already infected. A new infection event
starting from 3 is inserted into the queue. (d) Node 4 recovers, the remaining
queue is shown.
10 Großmann et al.
4.1 Analysis
Our approach combines the advantages of an event-based simulation with the
advantages of rejection sampling. In contrast to the Optimized Gillespie Algo-
rithm, finding the node for the next event can be done in constant time. More
importantly, the number of rejection events is dramatically minimized because
the queue only contains events that are realistically possible. Therefore, it is
crucial that each node “knows” its own curing time and that the curing events
are always generated before the infection events. In contrast to traditional event-
based simulation, we do not have to iterate over all neighbors of a newly infected
node followed by a potentially costly reordering of the queue.
Runtime For the runtime analysis, we assume that a binary heap is used to
implement the event queue and that the graph structure is implemented using
a hashmap. Each simulation step starts by taking an element from the queue
(cf. Line 7, Alg. 4), which can be done in constant time. Applying the change of
state to a particular node has constant time complexity on average and linear
time complexity (in the number of nodes) in the worst case as it is based on
lookups in the hashmap.
Now consider the generation of infection events. Generating a waiting time
(Line 3, Alg. 3) can be done in constant time because we know the degree (and
therefore the rate) of each node. Likewise, sampling a random neighbor (Line 8) is
constant in time (assuming the number of neighbors fits in an integer). Checking
for an early reject (Line 9) can also be done in constant time because each
neighbor is sampled with the same (uniform) probability and is annotated with
its recovery time. Even though each early rejection can be computed in constant
time, the number of early rejections can of course increase with the mean (and
maximal) degree of the network. Inserting the newly generated infection event(s)
to the event queue (Line 11) has a worst-case time complexity of O(log n), where
nis the number of elements in the heap. In our case, nis bounded by twice the
number of infected nodes. However, we can expect constant insertion costs on
average [24, 25].
Correctness Here, we argue that our method generates correct sample trajec-
tories of the underlying Markov model. To see this, we assume some hypothetical
changes to our method that do not change the sampled trajectories but makes
it easier to reason about the correctness. First, assume that we abandon early
rejects and insert all events in the event queue regardless of their possibility of
success. Second, assume that we change the generation of infection events such
that we do not only generate the earliest attempt but all infection attempts until
recovery of the node. Note that we do not do this in practice, as this would lead
to more rejections (less early rejections).
Similar to [21], we find that our algorithm is equivalent to the direct event-
based implementation of the following spreading process:
Iµ
S S + I λ
I + I I + I λ
I+I.
Rejection-Based Simulation of Stochastic Processes on Networks 11
In [21], I + I λ
I + I is called a shadow process, because the application
of this rule does not change the network state. Hence, rejections of infections
in the SIS model can be interpreted as applications of the the shadow process.
Note that the rate at which this rule is applied to the network is the rate of the
rejection events. Hence, the rate at which an infected node attacks its neighbors
(no matter whether in state I or S) is exactly λk, where kis the degree of
the node. Our method includes the shadow process into our simulation in the
following way: For each S–I edge and I–I edge, an infection event is generated
with rate λand inserted into the queue. The decision if this event will be a real
or a “shadow infection” is postponed until the event is actually applied. This is
possible because both rules have the same rate, in particular, the joint rate at
which an infected k-degree node attacks its neighbors will always be kλ.
4.2 Generalizations
So far we have only considered SIS processes on static and unweighted networks.
This section shorty discusses how to generalize our simulation method to SIS-
type processes on temporal and weighted networks.
General Epidemic Models A key ingredient to our algorithm is the early
rejection of infection events. This is possible because we can compute a node’s
curing time already when the node gets infected. In particular, we exploit that
there is only one way to leave state I, that is, by the application of a node-based
rule. This gives us a guarantee about the remaining time in state I. Other epi-
demic models have a similar structure. For instance, consider the Susceptible-
Infected-Recovered (SIR) model, where infected nodes first become recovered
(immune), before entering state I again:
S+I λ
I + I I µ1
R R µ2
S.
We also consider the competing pathogens model [26], where two infectious
diseases, denoted by I and J, compete over the susceptible nodes:
S+I λ1
I+I S+J λ2
J + J I µ1
S J µ2
S.
In both cases, we can exploit that certain states (I, J, R) can only be left
under node-based rules and thus their residence time is independent of their
neighborhood. This makes it simple to annotate each node in any of these states
with their exact residence time and perform early rejections accordingly.
If we do not have these guarantees, early rejection cannot be applied. For
instance in the (fictional) system:
S+I λ1
I + I I + I λ2
I+S.
It is likely that our method will still perform better than the traditional
event-based approach, however, the number of rejection events might signifi-
cantly decrease its performance.
12 Großmann et al.
Weighted Networks In weighted networks, each edge e∈ E is associated with
a positive real-valued weight w(e)R>0. Each edge-based rule of the form
A+C λ
B+C
fires on this particular edge with rate w(e)·λ. Applying our method to weighted
networks is simple: Let nbe a node. During the generation of infection events, in-
stead of sampling the waiting time with rate λk, we now use λPn0N(n)w(n, n0)
as the rate, where N(n) is the set of neighbors of n. Moreover, instead of choosing
a neighbor that will be attacked with uniform probability, we choose them with
a probability proportionally to their edge weight. This can be done by rejection
sampling or in O(log(k)) time complexity, where kis the degree of n.
Temporal Networks Temporal (time-varying, adaptive, dynamic) networks
are an intriguing generalization of static networks which generally complicates
the analysis of their spreading behavior [27–30]. Generalizing the Gillespie algo-
rithm for Markovian epidemic-type processes is far from trivial [27].
In order to keep our model as general as possible, we assume here that an
external process governs the temporal changes in the network. This process runs
simultaneously to our simulation and might or might not depend on the current
network state. It changes the current graph by adding or removing edges, one
edge at a time. For instance, after processing one event, the external process
could add or remove an arbitrary number of edges at specific time points until
the time of the next event is reached. It is simple to integrate this into our
simulation.
Given that the external process removes an edge, we can simply update the
neighbor list and the degrees in our graph. For each infection event that reaches
the top of the queue, we first check if the corresponding edge is still present. If
not, we reject the event. This is possible because removing events only decreases
infection rates which we can correct by using rejections. When an edge is added
to the graph and at least one corresponding node is infected, the infection rate
increases. Thus, it is not sufficient to only update the graph, we also generate an
infection event which accounts for the new edge. In order to minimize the number
of generated events, we change the algorithm such that each infected node is
annotated with the time point of its subsequent infection attempt. Consider
now an infected node. When it obtains a new edge, we generate an exponentially
distributed waiting time with rate λmodeling the infection attempt through this
specific link. We only generate a new event if this time point lies before the time
point of the subsequent infection attempt of the node. In that case, we also
remove the old event associated with this node from the queue.
Since most changes in the graph do not require changes to the event queue
(and those that do only cause two operations at maximum), we expect our
method to handle temporal networks with a reasonably high number of graph
updates very efficiently. In the case that an extremely large number of edges in
the graph change at once, we can always decide to iterate over the whole network
and newly initialize the event queue.
Rejection-Based Simulation of Stochastic Processes on Networks 13
5 Case Studies
We demonstrate the effectiveness our approach on three classical epidemic-type
processes. We compare the performance of our method with the Standard Gille-
spie Algorithm (GA) and the Optimized Gillespie Algorithm (OGA) for differ-
ent network sizes. We use synthetically generated networks following the con-
figuration model [31] with a truncated power-law degree distribution, that is
P(k)kγfor 3 k1000. We compare the performance on degree distri-
butions with γ∈ {2,3}. This yields a mean degree around 30 (γ= 2) and 10
(γ= 3). We use models from the literature but adapt rate parameters freely to
generate interesting dynamics. Nevertheless, we find that our observations gen-
eralize to a wide range of parameters that yield networks with realistic degree
distributions and spreading dynamics.
We also report how the number of nodes in a network is related to the CPU
time of a single step. This is more informative than using the total runtime of a
simulation because the number of steps obviously increases with the number of
nodes when the time horizon is fixed. The CPU time per step is defined as the
total runtime of the simulation divided by the number of steps, only counting
the steps that actually change the network state (i.e., excluding rejections). We
do not count rejection events, because that would give an unfair advantage to
the rejection based approach. The evaluation was performed on a 2017 MacBook
Pro with a 3.1 GHz Intel Core i5 CPU and 16 GB of RAM.
Note that an implementation of the OGA was only available for the SIS
model and the comparison is therefore not available for other models. Due to
the high number of rejection steps in all models, we expect a similar difference
in the performance between our approach and the OGA also for other models.
5.1 SIS Model
For the SIS model we used rate parameters of (µ, λ) = (1.0,0.6) and an initial
distribution of 95% susceptible nodes and 5% infected nodes. CPU times are
reported in Fig. 4a, where “reject” refers to our rejection-based algorithm (as
described in Section 4). For a sample trajectory, we plot the fraction of nodes in
each state w.r.t. time (Fig. 4b). To have a comparison with OGA we used the
official Fortran-implementation in [21] and estimated the average CPU time per
step based on the absolute runtime. Note that the comparison is not perfectly
fair due to implementation differences and additional input/output of the OGA
code. It is not surprising that the OGA performs comparably bad, as the method
is suited for simulations close to the epidemic threshold. Moreover, our maximal
degree is very large, which negatively affects the performance of the OGA.
We also conducted experiments on models closer to the epidemic threshold
(i.e., where the number of infection events is very small, e.g. λ= 0.1) and with
smaller maximal degree (e.g. kmax = 100). The relative speed-up to the GA in-
creased slightly compared to the results in Fig. 4a. The performance of the OGA
improved significantly compared our method leading to a similar performance
as our method (results not shown).
14 Großmann et al.
(a) (b)
Fig. 4: SIS model (a): Average CPU time for a single step (i.e., change of network
state) for different networks. The GA method run out of memory for γ= 2.0,
|N|= 107. (b): Sample dynamics for a network with γ= 3.0 and 105nodes.
(a) (b)
Fig. 5: SIR model (a): Average CPU time for a single step (i.e., change of network
state) for different networks. (b): Sample dynamics for a network with γ= 2.0
and 105nodes.
5.2 SIR Model
Next, we considered the SIR model, which has more complex dynamics. We used
rate parameters of (µ1, µ2, λ) = (1.1,0.3,0.6) and an initial distribution of 96%
susceptible nodes and 2% infected and recovered nodes, respectively. Similar as
above, CPU times and example dynamics are reported in Fig. 5. We see that
runtime behavior is almost the same as in the SIS model.
5.3 Competing Pathogens Model
Finally, we considered the Competing Pathogens model. We used rate param-
eters of (λ1, λ2, µ1, µ2) = (0.6,0.63,0.6,0.7) and an initial distribution of 96%
susceptible nodes and 2% infected nodes for both pathogens (denoted by I, J),
respectively. CPU times and network dynamics are reported in Fig. 6. The model
Rejection-Based Simulation of Stochastic Processes on Networks 15
(a) (b)
Fig. 6: Competing pathogens model (a): Average CPU time for a single step (i.e.,
change of network state) for different networks. (b): Mean fractions and standard
deviations of a network with γ= 2.0 and 104nodes.
is interesting because we see that in the beginning J dominates I due to its higher
infection rate. However, nodes infected with pathogen J recover faster than those
infected with I. This gives the I pathogen the advantage that infected nodes have
more time to attack their neighbors. In the limit, I takes over and J dies out.
For this model stochastic noise has a significant influence on the macroscopic
dynamics. Therefore, we also reported the standard deviation of the fractions
(cf. Fig. 6). Note that the fraction of susceptible nodes is almost deterministic.
Performance-wise our rejection method performs slightly worse than in the pre-
vious models (w.r.t. the baseline). We believe that this is due to the even larger
number of infection events and rejections.
6 Conclusions
In this paper, we presented a novel rejection algorithm for the simulation of
epidemic-type processes. We combined the advantages of rejection sampling and
event-driven simulation. In particular, we exploited that nodes can only leave
certain states using node-based rules, which made it possible to precompute
their residence times, which then again allowed us to perform early rejection of
certain events.
Our numerical results show that our method outperforms previous approaches
especially for networks which are not close the epidemic threshold. In particular,
the speed-up increases as the maximal degree of the network increases.
As future work, we plan to extend the method to compartment models with
arbitrary rules, including an automated decision for which states early rejections
can be computed and are useful.
Acknowledgments We thank Michael Backenk¨ohler for his comments on the
manuscript.
16 Großmann et al.
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