Epidemics-Profile Threshold

The Profile-Threshold model assumes the existence of node profiles that act as preferential schemas for individual tastes but relax the constraints imposed by the Profile model by letting nodes influenceable via peer pressure mechanisms.

Epidemics
  515

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contributed at 2019-05-09

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Method-focused categoriesData-perspectiveGeoinformation analysis
Method-focused categoriesProcess-perspectivePhysical process calculation
Method-focused categoriesProcess-perspectiveHuman-activity calculation

Model Description

English {{currentDetailLanguage}} English

配置文件阈值

Profile-Threshold模型由Milli等人于2017年引入。 [1]

Profile-Threshold模型假定存在节点概要文件,这些节点概要文件充当个人喜好的模式,但是通过让节点可通过对等压力机制影响来放松概要文件模型所施加的约束。

同伴压力以阈值建模。

扩散过程从已经采用给定行为S的一组节点开始:

  • for each of the susceptible node an unbalanced coin is flipped if the percentage of its neighbors that are already infected excedes its threhosld. As in the Profile Model the coin unbalance is given by the personal profile of the susceptible node;
  • if a positive result is obtained the susceptible node will adopt the behaviour, thus becoming infected.
  • if the blocked status is enabled, after having rejected the adoption with probability blocked a node becomes immune to the infection.
  • every iteration adopter_rate percentage of nodes spontaneous became infected to endogenous effects.

Statuses

During the simulation a node can experience the following statuses:

Name Code
Susceptible 0
Infected 1
Blocked -1

Parameters

Name Type Value Type Default Mandatory Description
threshold Node float in [0, 1] 0.1 False Individual threshold
profile Node float in [0, 1] 0.1 False Node profile
blocked Model float in [0, 1] 0 False Blocked nodes
adopter_rate Model float in [0, 1] 0 False Autonomous adoption

The initial infection status can be defined via:

  • percentage_infected: Model Parameter, float in [0, 1]
  • Infected: Status Parameter, set of nodes

The two options are mutually exclusive and the latter takes precedence over the former.

Methods

The following class methods are made available to configure, describe and execute the simulation:

Configure

classndlib.models.epidemics.ProfileThresholdModel.ProfileThresholdModel(graph)

Node Parameters to be specified via ModelConfig

Parameters:
  • profile – The node profile. As default a value of 0.1 is assumed for all nodes.
  • threshold – The node threshold. As default a value of 0.1 is assumed for all nodes.
ProfileThresholdModel.__init__(graph)

Model Constructor

Parameters: graph – A networkx graph object
ProfileThresholdModel.set_initial_status(selfconfiguration)

Set the initial model configuration

Parameters: configuration – a `ndlib.models.ModelConfig.Configuration` object
ProfileThresholdModel.reset(self)

Reset the simulation setting the actual status to the initial configuration.

Describe

ProfileThresholdModel.get_info(self)

Describes the current model parameters (nodes, edges, status)

Returns: a dictionary containing for each parameter class the values specified during model configuration
ProfileThresholdModel.get_status_map(self)

Specify the statuses allowed by the model and their numeric code

Returns: a dictionary (status->code)

Execute Simulation

ProfileThresholdModel.iteration(self)

Execute a single model iteration

Returns: Iteration_id, Incremental node status (dictionary node->status)
ProfileThresholdModel.iteration_bunch(selfbunch_size)

Execute a bunch of model iterations

Parameters:
  • bunch_size – the number of iterations to execute
  • node_status  –如果必须返回增量节点状态。
返回值:

包含每次迭代的字典的列表{“迭代”:迭代ID,“状态”:dictionary_node_to_status}

在下面的代码中显示了在随机图上实例化和执行Profile Threshold模型仿真的示例:我们将初始感染节点设置为总种群的10%,将Profile分配为0.25,将阈值分配为0.15,以所有节点。

import networkx as nx
import ndlib.models.ModelConfig as mc
import ndlib.models.epidemics.ProfileThresholdModel as pt

# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)

# Model selection
model = pt.ProfileThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('blocked', 0)
config.add_model_parameter('adopter_rate', 0)
config.add_model_parameter('percentage_infected', 0.1)

# Setting nodes parameters
threshold = 0.15
profile = 0.25
for i in g.nodes():
    config.add_node_configuration("threshold", i, threshold)
    config.add_node_configuration("profile", i, profile)

model.set_initial_status(config)

# Simulation execution
iterations = model.iteration_bunch(200)
[1] Letizia Milli,Giulio Rossetti,Dino Pedreschi,Fosca Giannotti,“复杂网络中的信息扩散:主动/被动难题”,复杂网络及其应用国际会议论文集(第305-313页)。湛史普林格。2017年

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How to Cite

Letizia Milli (2019). Epidemics-Profile Threshold, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/31a92300-ae55-47e5-91a7-b3132c63e47c
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Initial contribute: 2019-05-09

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