Abstract
This study presents a novel framework for the study of endogenous network growth subject to constraints. The literature on strategic network formation analysed the specific case of positive constraints: in the present work, the model is extended to constraints which can be negative and change in time depending on the actions of the agents. A characterisation of stable networks in the static case is provided, and it is proved that finding them is computationally difficult unless specific assumptions are made. The framework can be applied to contexts in which the formation of a link inhibits or implies the formation of another one, typically due to time, space or capacity constraints. Two specific examples are investigated, highlighting the importance of modelling constraints in order to obtain credible simulations and null models: the network of corporate control and the network of citations among scientific papers.
Keywords
Network formation Nash equilibrium Complexity of equilibria Network analysisJEL Classification
D85 C55 C72“Always go to other people’s funerals; otherwise they won’t go to yours.” Yogi Berra (facing a typical “constrained growth” network)
1 Introduction
In the last 20 years, the theory of networks has been recognised an important role in explaining the formation and functioning of social and economic settings in which relationships among agents are of fundamental importance. In particular, several models of network formation were developed targeting the mechanisms by which some characteristics of nodes (typically, the cost of creating/keeping alive a link, compared to the utility received from becoming—directly or indirectly—connected to some other nodes) endogenously determine the structure of a network. A stream of literature, starting from the seminal work of Bala and Goyal (2000), has developed focusing on a noncooperative approach, where the choice of adding a link between two nodes is made independently by only one of them, which bears all the cost—although other nodes potentially benefit from such link. Based on this framework, a definition of stability can be given, typically based on the concept of pairwise Nash equilibrium (such as in Galeotti 2006 and Haller et al. 2007), or some refinement of it (for instance Dutta and Mutuswami 1997 consider coalition choices, while the concept of “farsightedly stable networks” formulated by Herings et al. 2009 is based on attributing nodes a longer horizon of strategical reasoning).
The aforementioned studies share the implicit assumption that links can be added and destroyed freely (though at some cost). Even experimental works on endogenous network formation have usually been based on the assumption that participants can at any point in time—or at least repeatedly—decide to create/break a link (Goeree et al. 2009; Kirchsteiger et al. 2016). This is a natural starting point for several reasons: links in many real world networks (e.g. computer networks, social relationships...) are indeed at least potentially volatile, the data available to researchers often describe some inherently volatile flow (e.g. trade, influence, information) over them, and even considering networks which are typically characterised by a stratification of links over time (such as connections in Internet social networks, or the network of roads between cities), most databases available to researchers are snapshots of networks at given points in time, sacrificing information on their temporal evolution. However, there are several contexts in which the process of network formation is profoundly shaped by constraints, and in which the assumption that links can be freely created is at odds both with reality and with data available to researchers. Constraints may have different origins: they can for instance be related to time (e.g. networks where nodes are scientific papers, patents or other kinds of timestamped objects), space (e.g. planarity), and rivalry (e.g. crossownership networks, nodes affected by capacity limits): two specific examples will be analysed in more detail in Sect. 3. Only recently some form of constrained growth was formalised in the context of strategic network formation by Haller (2012). His study provides interesting conclusions concerning networks which grow around an exogenously fixed subset of links, shown to potentially change drastically the existence, numerosity, stability and efficiency of stable configurations. An interesting insight is that such backbone infrastructures, that is, sets of links which are guaranteed to exist ex ante and independently from individual incentives, and which hence forbid nodes from playing their individual best replies, can actually cause global welfare improvements. The present study generalises the approach to the analysis of repeated addition of nodes and/or links, under positive and negative constraints. Differently from the work of Haller, the set of guaranteed/forbidden links will not necessarily be exogenously given, but can come instead from the previous iteration of the network formation process. This results in a rich framework, which can be specialised according to the characteristics of the network under analysis.
2 The Model
As in the model by Galeotti et al. (2006), a network is composed by \(N = \{1, \dots , n\}\) nodes: for each pair of nodes (i, j) a cost parameter \(c_{ij} > 0\) and a value parameter \(v_{ij} > 0\) are given. A directed network g is formally a collection of pairs of nodes: if a pair (i, j) is in g, we say that i sponsors a link to j, and we write \(g_{ij}=1\). \(\bar{g}\) represents the corresponding undirected network, i.e. the smallest network containing g and also (j, i) for each (i, j) contained in g. The set of feasible networks is denoted by \(\mathcal G\).
2.1 Internal Constraints
Proposition 1
(Haller 2012) Consider a strategic model of network formation with payoff functions \(\Pi _i({\mathfrak {g}}^+, e, g)\), \(g \in \mathcal G\), \(i \in N\). Suppose that costs are ownerhomogeneous. Then there exists a Nash network \(g^*\).
What follows is a simplified proof of this result. Like the original proof by Haller (2012), it relies on the observation that the proof of existence by Haller et al. (2007) does not exploit the homogeneity of costs, only the ownerhomogeneity.
Proof
We start by replacing each link \((i,j) \in {\mathfrak {g}}^+\) with an “ancillary” node h “serving” i and j, which has cost \(c_{h} = 1\), \(v_{hk}=2\) for \(k=i,j\), and \(v_{hk} = 0\) otherwise. This model has ownerhomogeneous costs, so it has a Nash network \({g^*}'\) (Haller et al. 2007). In \({g^*}'\), each ancillary node must be connected (possibly indirectly) to both the nodes it serves. If it is connected indirectly, we sever the first link of the path (or any other link, if the first step is a direct connection to the other node it serves) and replace it with a direct link. Similarly, we sever any other link to any ancillary node and replace it with a link to any of the two nodes it serves. The result is still a Nash network (all utilities of “original” nodes weakly increase and their space of strategies is unchanged, while ancillary nodes have clearly no incentive to deviate), and if we restrict it to the N original nodes we obtain the desired \(g^*\). \(\square \)
The following result is analogous to the previous except that it now allows for \({\mathfrak {g}}^ \not = e\).
Proposition 2
Consider a strategic model of network formation with payoff functions \(\Pi _i({\mathfrak {g}}^+, {\mathfrak {g}}^, g)\), \(g \in \mathcal G\), \(i \in N\). Suppose that costs are ownerhomogeneous. Then there exists a Nash network \(g^*\).
Proof of Proposition 2
 (a)There exists another link (h, k)^{3} from \(N_i(g^* \ominus (i,j))\) to \(N_j(g^* \ominus (i,j))\) (see Fig. 1) or viceversa, which is not forbidden \(((h,k) {\not \in {\mathfrak {g}}^})\) and is part of the best response of h to \(g^* \ominus (i,j)\), i.e.$$\begin{aligned} c_{h,k} < \sum _{k' \in N_k(g^* \ominus (i,j))} v_{h,k'}. \end{aligned}$$(1)
 (b)
There is no such pair (h, k).
In the case A, consider the network \(g^* \ominus (i,j) \oplus (h,k)\): for any node \(l \not \in \{i,h\}\), the actions space is unchanged from \(g^*\), as well as the payoffs. For i, all available strategies now deliver a payoff increased by \(c_{i,j}\) (the cost of connecting the two components now being borne by h), so their preference ordering does not change. Finally, since costs are ownerhomogeneous, h does not have an incentive to deviate by replacing the link (h, k) with a different one. In the case B, consider instead the network \(g^* \ominus (i,j)\). For any node outside \(N_i(g^*)\), the preference ordering of strategies is unchanged. The same holds for nodes in \(N_i(g^*)\), except for strategies which would connect the two components; but such strategies are, by assumption (Eq. 1 is not satisfied), dominated. So in both cases we have a new Nash equilibrium. Since the case \(\Pi ({\mathfrak {g}}^+, e,\cdot )\) is proved by Proposition 1, the result is proved by induction for any possible \({\mathfrak {g}}^\). \(\square \)
Proposition 2 is the natural generalisation of Proposition 1 to the presence of negative restrictions.^{4} Analogously, the following result, related to networks of positive constraints which are (in the unconstrained model of network formation) Pareto optimal, generalises Proposition 2 by Haller (2012).
Proposition 3
Consider a strategic model of network formation with payoff functions \(\Pi _i({\mathfrak {g}}^+,{\mathfrak {g}}^, g), g \in \mathcal G, i \in N\). Suppose that the preexisting network or infrastructure \({\mathfrak {g}}^+ \in \mathcal G\) is Pareto optimal. Then the empty network is a strict Nash network and the only Nash network.
Proof
Let \({\mathfrak {g}}^+\) be Pareto optimal. The case \({\mathfrak {g}}^=e\) is Proposition 2 by Haller (2012). When considering \({\mathfrak {g}}^\ne e\), the actions set of some nodes is restricted, but the links in \({\mathfrak {g}}^+\) are left untouched (recall that \({\mathfrak {g}}^+\) and \({\mathfrak {g}}^\) are disjoint). Hence, the empty network is still a strict Nash network, because the preference ordering on available strategies does not change.
Suppose next that some \(g^*\ne e\) is a Nash network. The proof develops as in the original result: given some pair (i, j) with \(1 = {g^*}_{ij} \ne {{\mathfrak {g}}^+}_{ij}= 0\), it must be that \(g^*_i\) is a best response against \(g_{i}^*\). But then \(g^*\) is strictly preferred to \({\mathfrak {g}}^+\) by at least i, while it is at least equally preferred by all other agents (since it contains all links in \({\mathfrak {g}}^+\)). This contradicts the Pareto optimality of \({\mathfrak {g}}^+\). \(\square \)
 1.
considering negative constraints is important in order to understand the growth of some real world networks,
 2.
from a social planner perspective, imposing negative constraints could in principle improve the beneficial effects of an endogenously formed network, possibly at a lower cost than through positive constraints.
It is worth starting with an example. The network in Fig. 2 does not admit a Nash equilibrium, since \(\{(2,1)\}\) is the best response of 2 to any network which includes (3, 1) (allowing 2 to connect to 3), but (3, 1) is in the best response of 3 to and only to networks which do not include (2, 1) (3 prefers to connect to 1 through 2 than directly). However, the network \(\{(2,1), (3,1)\}\) can be made stable both with only positive constraints (\({\mathfrak {g}}^+ = \{(3,1)\}\)) and with only negative positive constraints (\({\mathfrak {g}}^ = \{(3,2)\}\)).
In general, any network g can be made trivially stable by setting \({\mathfrak {g}}^+ = g\) and \({\mathfrak {g}}^\) to the complementary of g. At the same time, there are obvious configurations which cannot be made stable by using only positive or only negative constraints: consider the case of \(n=2\), with \(c_{12} = 2\) and \(c_{21} = 4\). If \(v_{12} = v_{21} = \) 1, the “connected” configuration can only be obtained in presence of positive constraints, while if \(v_{12} = v_{21} =\) 3, the “disconnected” configuration can only be obtained in presence of negative constraints.
Hence, both positive and negative constraints have the sometimes exclusive ability of transforming given network configurations in Nash equilibria. This symmetry however breaks when we look at the welfare of obtained equilibria, as suggested already in the last example proposed, and as formalised by the following result.^{5}
Proposition 4
 (a)
If g becomes an equilibrium with some \({\mathfrak {g}}^ \ne e\), \({\mathfrak {g}}^+ = e\), then it is not Pareto optimal.
 (b)
If g is Pareto optimal, then it can be made an equilibrium with some \({\mathfrak {g}}^+ \ne e\), \({\mathfrak {g}}^ = e\).
Proof
It is easy to see that the creation of a new link (i, j), possibly replacing another link (i, k) to a same component of the network, is always (strictly) Pareto improving when it is part of the (strict) best reply of i. This is because it must be convenient for i, and makes two components connected (or keeps them unchanged, in the replacement case). Now consider case (a): since g becomes unstable once negative constraints are removed, there must be some \((i,j) \in {\mathfrak {g}}^\) which would be part of the (strict) best reply of i to g, possibly replacing some (i, k). So \(g \oplus (i,j)\) (or \(g \oplus (i,j) \ominus (i,k)\), in the replacement case) necessarily Pareto dominates g, which is hence not Pareto optimal. For case (b), notice that if g is Pareto optimal, the argument above states that there cannot be a link \((i,j) \not \in g\) which is part of a profitful deviation for i, so to stabilise g it is sufficient to set \({\mathfrak {g}}^+ = g\). \(\square \)
Figure 3 summarises the social planner perspective on positive and negative constraints: the latter can sometimes substitute the former (and assumingly be easier to implement) when the goal is to avoid implicit costs related to stability, but do not help in reaching Pareto optimality in the sense of the mere maximization of private values.
2.2 Complexity of Finding Nash Equilibria
Both the proof of Proposition 2 and the proof by Haller et al. (2007) it reduces to are constructive proofs, i.e. as long as the required conditions are satisfied, they provide a recipe for finding a Nash network. Such recipe is relatively simple to implement.^{6}
Outside of such assumptions, however (i.e. with nonownerhomogeneous costs), not only a Nash equilibrium might not exist, but determining if one exists, and finding it, can be a computationally hard task. What follows is a more precise characterisation of the computational complexity of such problem.
First, determining if a given network configuration is a Nash equilibrium is relatively simple (with or without constraints): it requires only to check the best response of each of the n nodes, and each of these checks requires O(n) operations; so the whole verification requires \(O(n^2)\) operations, and this locates the problem is in NP, the class of problems a solution of which can be validated in polynomial time.
The reduction of the search of Nash equilibria to 3SAT is performed by constructing a network composed of m “variable” components and k “clause” components, as represented in Fig. 4. Notice that the components are connected among them at the extrema of the links \(\ell _{hT}\) and \(\ell _{hF}\) they share. In order for a “variable node” \(v_h\) to become connected to its objective \(o_h\), it needs to sponsor \(\ell _{hT}\) or \(\ell _{hF}\) (and is ex ante indifferent between them). Now, the “clause” component is unstable whenever one of the peripheral links is built (recall the example in Fig. 2). It becomes instead a Nash equilibrium if at least one of the internal paths is built, i.e. if at least one of the variable nodes chooses the appropriate path/truth value. Hence, the collection of all clause components (and hence the network) is a Nash equilibrium if and only if an appropriate assignment of truth values is implemented.
It can be observed that the stability concept being employed is weak: each variable node \(v_h\) can deviate and build the other path to its objective \(o_h\) without incurring any loss. However it is trivial to make it strict by amending the definition of \(v_{ij}\) with the rule that \(v_{ij} = 0.5\) if \((i,j) = (v_h, l_k)\) for some h,k, and by raising the cost of \(\ell _{hF}\) to 1.1: with these changes, variable nodes have a strict incentive not to deviate from configurations which satisfy all clauses, and if this happens regardless from the value of some variable h, then the variable node \(v_h\) has a slight preference for building \(\ell _{hT}\) over \(\ell _{hF}\).
Having shown that the problem of finding Nash equilibria is both in NP and in NPhard, it is hence in NPcomplete, which is defined as the intersection of the two. Notice that the result concerning NP was proven in the more general case of arbitrary constraints, while the result concerning NPhard was proven again in the more general case of not resorting to constraints. So the whole proof of NPcompleteness applies both to the original model by Bala and Goyal (2000), and to the generalised model adopted in the present work.
2.3 Repeated Internal Constraints and NonDecreasing Network Models
A basic ingredient of virtually any real world process of network formation is time: as will be exemplified in Sect. 3.2, it can be a crucial ingredient in the study of some real world networks. A study of the consequences of repeated internal constraints, going beyond the analysis of static Nash equilibria relative to exogenous constraints, is hence a natural development of the theory exposed so far. In what follows, I will assume that the formation of the network happens in a discrete time setting. For each \(t=1,2\dots \), I will define as \({\mathfrak {g}}^{t^+}\) and \({\mathfrak {g}}^{t^}\) respectively the positive and negative constraints at that time period. At each time, the best reply of each node is the one maximizing \(\Pi _i({\mathfrak {g}}^{t^+}; {\mathfrak {g}}^{t^}; \cdot )\).^{8} The outcome, if any, of the step t, denoted as \(g^t\), will hence be a Nash equilibrium for these payoffs functions. Clearly, such outcome needs not be unique, and neither it necessarily exist: if it does not, the network formation process terminates at time t.
The introduction of endogenously determined, time dependent constraints is a powerful conceptual tool, but it increases considerably the amount of degrees of freedom, so the model is of limited utility unless one restricts to specific classes of rules which have a particular economic meaning. The result which follows considers the class of nondecreasing network models, defined as those for which \({\mathfrak {g}}^{t^+} = g^{t1}\) (the positive restriction coincides with the outcome of the previous step of the process): such class naturally maps to several real world contexts, including the case of bibliometric networks analysed in Sect. 3.2. A peculiarity of nondecreasing network models is that, since the number of links present at time t is (weakly) increasing in t itself, and since it can never exceed \(n^2n\), it must, for some t, terminate or stabilise in some configuration, which I will call a limit network. A limit network will then be defined as strict if there is no other limit network composed by a subset or a superset of its links.
Proposition 5
If \({\mathfrak {g}}^{t^}\) is constant, then the set of (strict) limits of the nondecreasing network model corresponds to the set of (strict) Nash equilibria of the static model associated to the payoffs function \(\Pi _i({\mathfrak {g}}^{1^+}; {\mathfrak {g}}^{1^}; \cdot )\).
Proof
Consider a (strict) Nash equilibrium \(g^*\) of the model associated to payoffs functions \(\Pi _i({\mathfrak {g}}^{1^+}; {\mathfrak {g}}^{1^}; \cdot )\). By, definition, it is also a (strict) Nash equilibrium for the first step of the nondecreasing network model. In order to prove that it is a limit network, it is hence sufficient to show that it is still a (strict) Nash equilibrium for \(\Pi _i(g^*; {\mathfrak {g}}^{1^}; \cdot )\). Assume it is not: this means there is some i which (weakly) prefers some \(g_i'\supset g_i^*\). But then, \(g^*\) was not a (strict) Nash network in the first place. The same applies hence for \(t=2,3\dots \)
 1.
considering partially nondecreasing network–networks in which some previously provided links can be destroyed, or
 2.
introducing timedependent negative constraints—for instance, real world networks with a population of nodes which increases over time can conveniently be modelled through appropriate negative constraints which decrease over time.^{9}
3 Applications
Several examples have been mentioned in Sect. 1 of real world networks the growth of which is significantly affected by positive or negative constraints. The present section goes more in depth in two of them in order to highlight the importance of taking into account such constraints when modelling them. The two case studies also make prominent the fact that modelling constraints inside a model of strategic network growth can result in interesting economic insights even when the distributions of costs \(c_{ij}\) and values \(v_{ij}\) are only partially known, or entirely unknown.
3.1 The Network of Corporate Control

The main natural constraint consists in the fact that control is clearly exclusive, i.e. if firm A controls firm B, firm C cannot control firm B.

A typical example of policy constraints is represented by antitrust policies, e.g. the European Commission forbidding the acquisition of a firm D on behalf of some holding E which already owns a competitor F.
While the restrictions described above are irrelevant for a mere characterisation of the network under study (i.e. quantifying the power of a core of firms), they become important in order to measure specific features relative to appropriate null models, resulting from simulations with endogenous incentives. For instance, an apparently low level of clustering (the tendency of nodes to link to neighbours of their neighbours) could be a consequence of the simple fact that ownership is exclusive, while a low assortativity (the tendency of nodes to link to similar links) might be due to local antitrust authorities limiting the control power of a single firm in a given national economy/sector. Running simulations which take into account such aspects becomes simple by resorting to the appropriate constraints, which in this case are only negative.
Interestingly, the cost of building links in the network being analysed coincides at a first approximation with the market value of the (voting) shares needed to control the target firm, which in turn is independent from the identity of the buying firm.^{11} This suggests that the case of targethomogeneous costs could be important to investigate (while the literature has so far mostly focused on ownerhomogeneous costs—see Proposition 1).
3.2 The Network of Citations Between Scientific Papers
The network of citations between scientific papers is a prominent example of an endogenously formed network in which the time component is not just crucial for the endogenous growth mechanism, but also easily observable in the data typically available to researchers. Indeed, scientific papers have well defined publication dates, which impose a clear temporal hierarchy among them and hence strong restrictions to the set of “actions”—that is, of citations—they can make: these observation, together with the specific constraints the network is subject to, are exploited by Battiston (2014) to provide a measure of the socalled “Matthew effect” (Merton 1968) in shaping citations flows, and hence bibliometric indicators. The Matthew effect consists in a cumulative advantage by which papers or authors which already received many citations in the past tend to be more cited in the future, even if hypothetically controlling for quality, originality and age.
In Sect. 2.3, the fundamental building block of the development of a network with repeated internal constraints was assumed to be the Nash equilibrium of a given step t. Under the specification given for the network of citations, in which at each step only one node is active, such a Nash equilibrium degenerates to the best response of each node. The hypothesis of all nodes existing since time 0 does not influence the strategic choice, which is determined simply as a best response among allowed links—because of the direction in which value “flows”, later links are irrelevant.
4 Conclusions
The evolution of many economic and social networks is characterised by constraints which delimit the action space of single nodes, in terms of links they can build and severe. This paper provides a general framework for introducing such constraints in models of strategic network formation where links are sponsored by individual nodes.
Previous results by Haller (2012) on the existence of Nash networks are extended to the presence of negative constraints; moreover, Pareto optimality of network configurations is put in relation with the constraints needed to transform them into equilibria: in general, negative constraints do not share the welfare benefits of positive ones, but they can provide a tool to guarantee the existence of equilibria. It is then shown that finding Nash equilibria, and even just asserting whether they exist, can be computationally unfeasible (NPcomplete) if the cost of building new links is not ownerhomogeneous.
Two prominent examples were presented of the importance of taking into account constraints in models of endogenous network formation. In the case of the network of corporate control, the constraints can be both natural (control is exclusive) and regulatory (e.g. antitrust); in the case of bibliometric networks, they are mainly related to the time factor (links are established at the time of publication, and only go backwards). The theoretical model can be specialised to study many other kinds of social networks, and provide empirical researchers with tools that go beyond what the mere static analysis of networks allows to identify. For instance, such restrictions should be taken into account when simulations of endogenous network formation are used to build null models against which to compare relevant features of real world models.
The literature has explored other kinds of strategic network formation: two examples of deviations from the basic assumptions by Galeotti et al. (2006) are network models in which links allow a oneway only flow of value (Galeotti 2006), and models in which the transmission of value over links is imperfect, and hence length of paths is relevant (Billand et al. 2010). The concept of constraints can be straightforwardly applied to these and other frameworks, but understanding which of the results exposed in the present paper extend to some extent to those other models might prove to be challenging, and is a stimulating direction for further research.
Footnotes
 1.
As in the approach of Haller (2012), the original cost of links in \({\mathfrak {g}}^+\) should be taken again into consideration when doing comparative statics and welfare analysis.
 2.
With a slight abuse of notation, when the network to be added/removed is composed of a single link, I will write \(g \oplus (i,j)\) or \(g \ominus (i,j)\), instead of \(g \oplus \{(i,j)\}\) or \(g \ominus \{(i,j)\}\), respectively.
 3.
Notice that i and h, or j and k, can coincide.
 4.
The assumption that costs are ownerhomogeneous is one of the reasons why it is impractical to define negative constraints just as arbitrarily costly links: if this was the case, in order for a ownerhomogeneous model of network formation to remain such after the imposition of negative constraints, such constraints could not consist in arbitrary sets of links, and should rather include all outgoing links from a given set of nodes. Another reason is that this would make the definition of endogenous negative constraints, as described in Sect. 2.3, much more complicated.
 5.
 6.
The proof by Haller et al. (2007) is composed of n iterative steps, each consisting in the evaluation of links from a given node to each other connected component. It is easy to see that for each connected component, the cost of such operation is bounded above by its size, so the total cost of each step is at most n. The proof of Proposition 2 adds in principle as many as \(n^2n\) steps (the maximum size of \({\mathfrak {g}}^\)). But since the initial \(g^* \ominus {\mathfrak {g}}^+\) cannot have more than n links (each link must be a bridge), all but at most n of such steps will be trivial: for instance, if one starts by looking at \((i,j) \not \in g^*\), the first steps are all trivial. So the total cost is still \(O(n^2\)).
 7.
This choice and the proof which follows are heavily inspired by Anshelevich et al. (2003), who proved the NPcompleteness of finding Nash networks in a different but related framework of network formation.
 8.
Clearly, the framework could also be an ideal context for the study of a less myopic type of rationality, such as the farsightedly stable networks (Herings et al. 2009).
 9.
I thank an anonymous reviewer for this remark.
 10.
In the aforementioned studies, such share is 50 %, but it is commonly acknowledged (Barclay and Holderness 1989) that the largest shareholder can attain de facto control even with a smaller share.
 11.
This is clearly an oversimplification made for illustrative purposes—the buyer entity could already be owning some amount of shares, and most importantly the price of the shares could reflect the interest in them on the behalf of the buyer.
 12.
This description of the network of citations excludes on purpose “spurious” effects due environmental constraints, such as the role that the fame of an author or the prestige of a journal can have in influencing the amount of citations to a given piece of research. This modelling decision is instrumental in building a null model which allows Battiston (2014), to find evidence of such spurious effect ultimately resulting in the desired measure.
 13.
In principle, given the typical publication process, which goes through a period of open discussion in seminars/workshop, an often lengthy referral process, and finally a delay from the definitive acceptance to the publication, it can easily happen that two papers i and j cite some version of each other. This very special case, which is not admissible under the simplified settings just described, would possibly deserve a specific analysis.
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