Jean-Philippe Cointet and Camille Roth (2007)
How Realistic Should Knowledge Diffusion Models Be?
Journal of Artificial Societies and Social Simulation
vol. 10, no. 3 5
<https://www.jasss.org/10/3/5.html>
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Received: 13-Nov-2006 Accepted: 21-Apr-2007 Published: 30-Jun-2007
For network analysis to fulfill its potential, however, I feel we must improve the methods of data gathering and measurement (…). Longitudinal panel designs for networks analysis of diffusion process are also needed; along with field experiments, they help secure the necessary data to illuminate the over-time process of diffusion. (Rogers 1976)
SET number_of_nodes := number of agents SET neighbor := ARRAY OF LISTS of size number_of_nodes SET state := ARRAY OF BOOLEANS ("INFORMED"/"NOT_INFORMED") of size number_of_nodes SET number_of_interactions := ARRAY OF INTEGERS of size number_of_nodes INITIALIZE neighbor[i] with the neighbor list for node i INITIALIZE state WITH "INFORMED" on a random selection of elements of size Integer_Part_Of(number_of_nodes*lambda) INTIIALIZE number_of_interactions WITH 0 on the whole array FOR each step of the simulation CHOOSE RANDOMLY an agent i IF state[i] IS "NOT_INFORMED" THEN CHOOSE RANDOMLY an agent j among neighbor[i] IF state[j] IS "INFORMED" THEN IF a uniform random variable in [[0, 1]] IS BELOW Probability_Of_Adoption_After_N_Interactions(number_of_interactions[i]) THEN state(i) := "INFORMED" END IF INCREMENT number_of_interactions(i) END IF END IF END FOR |
Algorithm |
Figure 1. Cumulated degree distributions for the various network structures, using the Medline-base collaboration network (left) and the board interlock network (right). x-axis: degree k, y-axis: N(k)=∑ _{k' ≥ k}N(k'). |
(1) |
with k_{i} the degree of node i. Empirical social networks are known to exhibit an abnormally high average clustering coefficient ‹c_{3}›, compared to those found in SF and ER random networks (Newman and Park 2003); many models traditionally try to rebuild this statistical parameter as well. Our real networks do not derogate from this rule. RN1 exhibits a high ‹c_{3}› of .827, while SF1 only has a ‹c_{3}› of .00539. We observe the same discrepancy in the second network: clustering in RN2 is high (‹c_{3}› = .889) while it is two orders of magnitude smaller in SF2 (‹c_{3}› = .00395).
(2) |
where κ_{j1,j2} is the number of nodes which the j_{1}-th & j_{2}-th neighbors of i have in common (leaving out i) and η_{j1,j2} = 1 + κ_{j1,j2} + θ_{j1,j2} where θ_{j1,j2} = 1 if j_{1} and j_{2} are connected, 0 otherwise. EB1 acceptably approaches the ‹c_{3}› of RN1, but falls short of one order of magnitude for c_{4} , suggesting that even an event-based reconstruction still misses part of the community structure of RN1. On the other hand, EB2 yields a ‹c_{4}› of .268 which is closer to the ‹c_{4}› of .398 for RN2. Values for these statistical parameters are gathered on Table 1.
Table 1: Main characteristics of the various network structures derived from the real networks RN1 and RN2 in terms of: number of agents N, number links M, density d, degree distribution shape and clustering coefficients ‹c_{3}› & ‹c_{4}‹ (averaged quantities over 1000 networks for SF, ER & EB). | |||||
RN1 | SF1 | ER1 | CN1 | EB1 | |
N | 6453 | ||||
M | 6.7410^{4} | 2.0810^{7} | 7.6210^{4} | ||
d | .00162 | 1 | .00183 | ||
degree dist. | power-law tail | Poisson | - | power-law tail | |
‹c_{3}› | .827 | .00539 | .00199 | 1 | .753 |
‹c_{4}› | .400 | .000260 | .000158 | 1 | .0694 |
RN2 | SF2 | ER2 | CN2 | EB2 | |
N | 4656 | ||||
M | 7.66·10^{4} | 2.17·10^{7} | 7.68·10^{4} | ||
d | .0035 | 1 | .0035 | ||
degree dist. | power-law tail | Poisson | - | power-law tail | |
‹c_{3}› | .889 | .00395 | .00403 | 1 | .897 |
‹c_{4}› | .398 | .000261 | .000217 | 1 | .268 |
Figure 2. Simulation results for complete, Erdös-Rényi, Scale-Free, Event-Based and real networks, using λ = 0.02 (outset) and λ = 0.002 (inset), along with associated 99% confidence intervals. Topologies built from the scientific collaboration network (left) and the board interlock network (right). |
Figure 3. Final probability of adoption P(n) after n recommending interactions (P_{max}=0.4). Empirical data adapted from (Leskovec et al. 2006), where P_{max}=0.04 (see Figure 4 for a discussion on P_{max} values). |
(3) |
from which it is straightforward to deduce that:
(4) |
as we assume that p(i) are independent one each other. Thus, the probability of adopting at each n-th interaction with an informed neighbor is p(n).
P_{TM}(n)=P_{max}H_{ν}(n)where H_{ν} is a threshold function: H_{ν}(n)=1 if n ≥ ν, 0 otherwise.
P_{CM}(n)=1-(1-p)^{min(n,ν)}Clearly, p=1-(1-P_{max})^{1/ν}; thus, once P_{max} is chosen, CM depends on only one parameter, say ν, as TM does.
Figure 4. Evolution of ρ for RM, TM and CM, given four distinct P_{max}, from left to right and from top to bottom : P_{max} =0.99, P_{max} =0.7, P_{max} =0.4, P_{max} =0.04. Confidence intervals (99%) are also shown. Insets exhibit asymptotic behavior for large time steps. |
Figure 5. Relative error between Threshold and Cascade model compared to Realistic Model |
^{2} This is especially relevant if the timescale of diffusion is smaller than that of social network evolution (e.g. in the case of rumors, hypes, etc.), then the social network can be considered static.
^{3} Wang et al. (2003) show that their model of virus diffusion is more efficient than that of Pastor-Satorras and Vespignani (2001) on various topologies, including a real computer network, while actually not directly comparing how diversely their model performs between real-world and modeled topologies. Similarly, Wu et al. (2004) simulate information propagation on a real e-mail network; yet not estimating how different the behavior would be in other kinds of (scale-free) networks.
^{4} Freely available from http://www.pubmed.com. Additionally, the particular network data used in this paper is here.
^{5} Data freely available from http://www.theyrule.net, Additionaly, the particular network data used in this paper is here.
^{6} Similarly to Leskovec et al. (2006), Backstrom et al. (2006) have measured the propension to join a "LiveJournal" community as a function of the number of friends already present in the community.
^{7} It is unclear whether such feature may or may not be accounted for by the aggregation of different categories of products and/or agents.
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