Abstract
We study two agent based models of opinion formation—one stochastic in nature and one deterministic. Both models are defined in terms of an underlying graph; we study how the structure of the graph affects the long time behavior of the models in all possible cases of graph topology. We are especially interested in the emergence of a consensus among the agents and provide a condition on the graph that is necessary and sufficient for convergence to a consensus in both models. This investigation reveals several contrasts between the models—notably the convergence rates—which are explored through analytical arguments and several numerical experiments.
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This work has been supported by the NSF Grants DMS-1515592 and RNMS11-07444 (KI-Net).
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Communicated by Irene Giardina.
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A Appendix
A Appendix
1.1 A.1 Results Concerning Symmetric and Strongly Connected Networks
Proposition
The matrix L is a diagonal dominant matrix, its eigenvalues satisfy \(\text {Re}(\lambda _i)\ge 0\) and no eigenvalue \(\lambda _i\) is purely imaginary except zero.
Proof
The diagonal entries of L satisfy \(\sigma _i=\sum _{j,j\ne i} a_{ij}\) where \(a_{ij}>0\), thus summing over each row will give zero and we deduce that L is diagonal dominant. By the Gershgorin disc theorem the eigenvalues \(\lambda _i\) are contained in the closed discs \(B(\sigma _i,\sigma _i)\) (see Fig. 3). Thus, the eigenvalues \(\lambda _i\) are either 0 or have a real part strictly positive. Moreover, 0 is always an eigenvalue of L as the constant vector \(\mathbf{1}=(1,\ldots ,1)^T\) is always an eigenvector of L associated with the eigenvalue \(\lambda =0\). \(\square \)
Lemma
If L is irreducible then the eigenvalue 0 is simple.
Proof
By Proposition 1 we have that the real parts of the eigenvalues of L are all nonnegative and there are no purely imaginary eigenvalues. We also note that 0 is an eigenvalue of L of at least multiplicity one because it is associated to \(\mathbf{1}\). Consider the matrix \(D = L-cId\) where \(c\in \mathbb {R}\) and notice that \(\lambda = a + ib\) is an eigenvalue of L if and only if \(\widehat{\lambda } = a - c + ib\) is an eigenvalue of D. Therefore, since 0 is an eigenvalue of L we must have that \(-c\) is an eigenvalue of D. If we choose c such that:
we have that:
Therefore \(-c\) is the eigenvalue of largest modulus of D and since D is irreducible we have by the Perron-Frobenius theorem that \(-c\) must be simple and since the eigenvalues of D are in a one to one correspondence with the eigenvalues of L we must have that 0 is a simple eigenvalue of L as desired. \(\square \)
Corollary
Suppose L is irreducible and symmetric. Then the solution of the consensus model, \(\mathbf{s}(t)\), satisfies:
with \({\bar{s}}\) the average opinion (9). Moreover, \(|s_{i}(t)-\bar{s}(0)|\le Ce^{-\lambda _{2}t}\) where C depends only on the initial condition and \(\lambda _{2}\) is the second largest eigenvalue of L.
Proof
The consensus model is a linear system, therefore its solution is given by:
where \(\mathbf {s_{0}} = (s_{1}(0),\ldots ,s_{N}(0))^{T}\). Note that since \(\mathbf{1}\) is an eigenvector of L corresponding to the eigenvalue 0, it is also an eigenvector of \( e^{-tL}\) corresponding to the eigenvalue of 1. Therefore we may write:
Also note that since L is diagonal dominant and symmetric that there exists P such that
where \(D = \text {diag}(0, \lambda _{2}, \lambda _{3},\ldots )\) and P is the matrix composed of the eigenvectors of L. Since L is symmetric these eigenvalues are real. By Proposition 1 and Lemma 1 there is exactly one zero eigenvalue and \(\lambda _{i}\) is strictly positive for \(2\le i\le n\). We now define:
this is the difference between the opinion at time t and the average opinion represented in the diagonal coordinate system. Notice that:
Therefore, since this is an uncoupled system of linear equations we must have that:
So for \(i\ge 2\) we must have that \(y_{i}(t)\rightarrow 0\) as \(t\rightarrow +\infty \) exponentially with rate at least \(\lambda _{2}\). To conclude, it remains to show that \(y_1(t) = 0\). Using that the eigenvectors of L form an orthogonal basis, the entry \(y_1(t)\) is given by:
since the mean value \(\bar{s}(t)\) is preserved over time. \(\square \)
1.2 A.2 Convergence of Linear Systems
Lemma
Given a linear system defined by:
Assume A has d distinct eigenvalues and a zero eigenvalue of multiplicity m with m linearly independent associated eigenvectors. If for all \(\lambda _{i}\in \text {Sp}(A)\) with \(i>m\) we have that \(Re(\lambda _{i})<0\) then
where \(\mathbf {u}\) is in the center subspace of A, \(E^{c}\).
Proof
For ease of notation we will write
where \(m_{i}\) is the algebraic multiplicity of \(\lambda _{i}\). We will also write that \(\lambda _{1} = 0\). We know that given \(\lambda _{i}\) that we may find \(m_{i}\) linearly independent generalized eigenvectors of A, \(\{\mathbf{v}_{\lambda _{i}}^{1},\ldots ,\mathbf{v}_{\lambda _{i}}^{m_{i}}\}\). The generalized eigenspace corresponding to \(\lambda _{i}\) is given by \(E_{\lambda _{i}} = \text {span}\{\mathbf{v}_{\lambda _{i}}^{1},\ldots ,\mathbf{v}_{\lambda _{i}}^{m_{i}}\}\) and by the generalized eigenspace decomposition theorem
we can find a basis of \(\mathbb {R}^{n}\) consisting of generalized eigenvectors of A. Therefore, we may write:
We know by the fundamental theorem of linear systems that the solution to the system is given by:
By (37) we may write:
where for each \(1\le i \le d\) we have \(\mathbf{e}_{i}\in E_{\lambda _{i}}\). Notice that since we are given that there are \(m_{1}\) distinct eigenvectors associated with \(\lambda _{1} = 0\) that for each \(w\in E_{\lambda _{1}}\) we have that \(A\mathbf{w}=0\). Consequently we have that \(A_{|E_{\lambda _{1}}}=0\) which implies that \(A_{|E_{\lambda _{1}}}=0\) for each \(t\in \mathbb {R}\). Taking the matrix exponential of both sides yields
Therefore we must have that
We now claim that for any \(i\ge 2\):
Since we are writing \(\mathbf {x_{0}}\) with respect to the basis of generalized eigenvectors of A we have that
where \(N_I\) is nilpotent. Consequently
with \(C_1>0\). Therefore, since \(\text {Re}(\lambda _{i})<0\) and every coordinate of \((Id+tN_i+\ldots +\frac{1}{k!}t^{k}N_i^{k})\mathbf{e}_{i}\) is polynomial in t we must have that there exist \(C>0\) and \(0<\epsilon <-\text {Re}(\lambda _{i})\) such that
We deduce that for \(i\ge 2\):
which implies
Since \(\mathbf{e}_{1}\in E^{c}\) we have that \(\lim _{t\rightarrow +\infty }\mathbf {x}(t)\in E^{c}\) as desired. \(\square \)
1.3 A.3 Decomposition into Strongly Connected Components
Lemma
If L is the Laplacian of a directed graph \(G=(V,E)\) then by relabeling vertices L can be represented:
Proof
We may partition the vertex set of G, V, into its strongly connected components \(\{U_{1},\dots ,U_{k}\}\), so that:
If we consider the set \(\{U_{1},\dots ,U_{k}\}\) as the vertex set of a new graph \(G^{*}\) with edge set \(E^{*}\) given by:
Then the graph \(G^{*}\) is a directed acyclic graph as G has been partitioned into strongly connected components; if a cycle existed all vertices included in the cycle would represent the same strongly connected component of G by (39) contradicting the partition of V into strongly connected components. Therefore there exists a topological ordering \(\le ^{*}\) on the vertex set of \(G^{*}\). This ordering is given by:
We only need to label the vertices of V in such a way that they respect the topological ordering. Then, this labeling of V produces L in the desired form. \(\square \)
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Weber, D., Theisen, R. & Motsch, S. Deterministic Versus Stochastic Consensus Dynamics on Graphs. J Stat Phys 176, 40–68 (2019). https://doi.org/10.1007/s10955-019-02293-5
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DOI: https://doi.org/10.1007/s10955-019-02293-5