=Paper=
{{Paper
|id=Vol-3126/paper52
|storemode=property
|title=Methodological approach to agent-based modeling of social networks
|pdfUrl=https://ceur-ws.org/Vol-3126/paper52.pdf
|volume=Vol-3126
|authors=Olga Vasylieva,Borys Butvin,Yuriy Shtyfurak
}}
==Methodological approach to agent-based modeling of social networks==
https://ceur-ws.org/Vol-3126/paper52.pdf
Methodological Approach to Agent-Based Modeling of Social
Networks
Olga Vasylieva1, Borys Butvin2 and Yuriy Shtyfurak3
1
Foreign Intelligence Service of Ukraine, 24/1, Nahirna Street, Kyiv, 04107, Ukraine
2
Central Research Institute of the Armed Forces of Ukraine, 28b Povitroflotskyi Avenue, Kyiv, 03049, Ukraine
3
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 37, Prosp. Peremohy, Kyiv,
03056, Ukraine
Abstract
The article considers social networks as an environment with a complex structure, which is
dynamically changing and difficult to analyze. Here is presented the basic methodological
approaches for application in the theoretical analysis of social networks. Further, it is proposed
to apply the method of agent-based modeling, which today, in the authors’ opinion, can be the
most adaptive for modeling internal processes of such dynamic social systems as social
networks. The most popular software tools for building agent-based models are described and
the software AnyLogic developed by XJTeknologies, which has a number of competitive
advantages, is highlighted as the most versatile and multifunctional.
Keywords 1
Agent-based modeling, social networks, information influence
1. Introduction linguistics, psychology and other knowledge can
become a tool to manipulate human communities.
The most acceptable instrument for
Aim of the study. Modeling of social processes
informational influence in terms of its own
such as information influence in the social
structure and the availability of appropriate target
networks by applying agent-based modeling and
audiences are social networks today. The term
selecting the most appropriate software tool.
"virtual (network) community" was firstly
Today, the information influence on human
introduced in 1993 by G. Rheingold, who defined
resource has a special place in the system of
it this way: "Virtual communities are social
management decision-making, the political
associations, growing out of the Web, when a
component, the development of business
group of people maintain an open discussion long
processes, etc. This has become possible due,
enough and humanly enough to form a network of
firstly, to the rapid development of digital
personal relations in cyberspace" [1].
technologies, including those used for data
Social networks today are an important
dissemination and information exchange, the
element of the structure of modern society, and
creation and development of new types of
their influence extends to various spheres of
information resources, increased access to
human activity: production, daily life, culture,
information for all segments of the population.
politics, etc. They perform communication,
Second, the large masses of information that
informational, entertaining, socializing functions
circulates in cyberspace, is open, easily accessible
in the society; they provide opportunities for self-
and such that with the help of technology,
expression, exchange of information and
experience, without any age, professional or any
ISIT 2021: II International Scientific and Practical Conference
«Intellectual Systems and Information Technologies», September
13–19, 2021, Odesa, Ukraine
EMAIL: olga.vasiljeva37@gmail.com (A. 1); butvin_bl@ukr.net
(A. 2); yura.shtyfurak@gmail.com (A. 3)
ORCID: 0000-0001-8263-782X (A. 1); 0000-0001-6086-6592 (A.
2); 0000-0001-7863-8862 (A. 3)
©️ 2021 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
other restrictions [2]. The statistics are provided 2. Dynamic – attention is focused on
below demonstrate significant penetration of changes in the network structure over time.
social networks in the life of modern human The reasons of disappearance and appearance
society. of edges of a network; changes of structure of
For example, statistics of social networks in a network in case of external influences;
2021 showed that 42% of the world population - stationary configurations of a social network
that is a colossal 3.2 billion people - use social are studied [6].
networks. The number of social network users has 3. Normative – studies the level of trust
increased by more than 13% over the last year, between actors, as well as norms, rules and
with almost half a billion new users registered sanctions that affect the behavior of actors in
before 2021. On average, more than 1.3 million the social network and the processes of their
new accounts appeared every day in 2020, that's interaction. In this case, the social roles
about 15.5 new users per second, according to the associated with a particular edge of the
Digital Global 2021 report [3]. network are analyzed [6].
A social network is a set of agents (vertices) 4. Resource – considers the actors' ability to
that can interact with each other, and the attract individual and network resources to
connections between them are social. From a achieve certain goals and differentiates actors
formal point of view, such networks are dynamic being in identical structural positions of the
social systems, it is convenient to present them in social network, according to their resources.
the form of graphs and apply the appropriate Individual resources can be knowledge,
mathematical apparatus for their analysis. prestige, wealth, ethnicity, gender (gender
This is what determines the relevance of identity). Under network resources understand
applying methodological approaches for influence, status, information, capital [6].
modeling such systems in order to further predict In order to involve all four mentioned
various social processes in networks, including approaches of social network analysis, taking into
information influence. account both the structure of dynamic system and
Since formalized models of information its individual actors, defining their interaction,
dissemination process in social networks are certain rules of the game, as well as giving them
actually absent, taking into account their inherent certain characteristics, the authors proposed to
subjective uncertainty, modeling the processes consider such method of simulation modeling as
that occur in them, as well as modeling social Agent-Based Modeling [7].
network itself turns into a weakly formalized Agent-based modeling is a simulation method
problem [4]. that examines the behavior of decentralized
As a first step, we should consider the autonomous agents, their interaction (both
methodological approaches that are used to individual agents and collective, such as
analyze social networks. They differ from organizations or groups) and how such behavior
traditional approaches in sociological sciences. determines the behavior of the entire system as a
The postulate that the attributes of individual whole. It combines elements of game theory,
actors are less important than the relationships and complex systems, emergence, computational
connections with other actors in the network sociology, multi-agent systems, and evolutionary
comes to the fore. That is, the attributes of programming. It’s used in noncomputational
individual actors - friendliness or unfriendliness, scientific fields including biology, ecology, and
level of intelligence - begin to play no major social sciences.
role [5]. Agent-based modeling is used to analyze
Currently there are four main methodological decentralized systems that are quite large, have
approaches to the analysis of social networks: heterogeneous structures, and are dynamically
1. Structural – emphasizes the geometric changing: old connections die off and new ones
shape and intensity of interactions (weight of appear. That is why this method is effective to
edges). All actors are viewed as vertices of a study the process of spreading of information
graph, which influence on the configuration of influence in social networks.
edges and other actors in the network. Special To create an agent-based model, all actors are
attention is paid to the mutual arrangement of viewed as separate agents. According to the
vertices, centrality, and transitivity of structural approach, the social network can be
interactions [6]. viewed as vertices with certain connections
between them. The network structure of the model
is dynamic, it is a kind of system that is self- of personal characteristics, which determines the
created, the elements of which appear and die. In resource approach.
such a system, the rules of behavior of each of the Considering the paradigms of system
agents and their social roles are also defined. simulation modeling (Figure 1)
Finally, each of the agents is given a certain pool
Figure 1: Three paradigms of system simulation modeling
it can be concluded that agent-based modeling possibility to simulate communication and
is the most multiple-purpose one. In contrast to information exchange [8].
discrete event modeling, which is consonant with During simulation experiments, computational
the low and medium abstraction level, and the complexities can arise because agent-based
system dynamics approach with a high abstraction models on average require more hardware and
level, agent-based models can be both very software power to run simulations than system
detailed, when agents represent physical objects, dynamics or discrete event simulations. Agent-
and extremely abstract, when competing based simulations can be implemented on small
companies or state governments are modeled desktop computers, or using large clusters of
using agents. computers, or any variation between the first two.
The main difference between the agent-based Desktop agent-based models can be simple
approach and the first two is the bottom-up and used mostly to teach how to model using
construction of the model. Dependencies between agents, test agent-based model development
aggregated quantities are not set on the basis of concepts, and analyze the results. Desktop utilities
knowledge about the real world, but are obtained include spreadsheets, particularly Excel, and
in the process of modeling individual behavior of mathematical computing systems such as
tens, hundreds or thousands of agents, their Mathematica and MATLAB.
interaction with each other and with objects, Large-scale agent-based models extends the
which are modeling the environment. capabilities of simple agent-based desktop models
The advantages of the agent-based approach and allows a larger number of agents (thousands
include: the absence of determinacy in the to millions) to participate in complex interactions.
behavior of the system at the global level that can Large-scale agent-based modeling is usually
lead to new hypotheses about its functioning performed using dedicated modeling
during model simulation; realism and flexibility environments that include a time-based scheduler,
in describing the system, the ability to model the communication mechanisms, flexible agent
most complex nonlinear feedbacks and to use any interaction topologies, a wide variety of devices
required level of detail and abstraction. In agent- for storing and displaying agent state [9].
based modeling, there are no restrictions on the Due to the fact that the agent-based approach
heterogeneity of model elements; but there is emerged in the 1990s in the U.S. university
environment, so far most of the tools are intended
for academic and educational purposes, and are and Java. Unlike Repast, the Swarm scheduler
not commercial products in full. only supports time progression at fixed intervals.
One of the most popular developments of this Swarm supports a full set of communication
type is the Swarm environment. It’s a collection mechanisms and can simulate all major
of C language libraries created at the Santa Fe topologies. Swarm includes a good set of utilities
Institute. The most famous commercial tools are for storing and displaying agent states. Since
RePast, AnyLogic, NetLogo and MASON. Swarm is based on a combination of Java and
MASO is a fast multi-agent modeling toolkit Objective-C, it is object-oriented. But this mix of
in Java that was developed as a framework for a languages causes some difficulties with
wide range of multi-agent modeling tasks, from integration into some large-scale development
swarm robotics to machine learning and socially environments, such as Eclipse [10].
complex environments. MASON makes a careful NetLogo is another cross-platform agent-
distinction between models and visualization, based simulation environment that is widely used
allowing models to be dynamically separated and supported. Originally based on the StarLogo
from or attached to visualizers, and platforms to system, NetLogo adapts agent-based systems
be changed at runtime. MASON is a collaborative consisting of a combination of live and software
effort between the Computer Science Department agents. It is ideal for modeling complex systems
at George Mason University and the Center for containing hundreds or thousands of agents
Social Complexity at George Mason University. interacting simultaneously. It allows users to
One of interest sources is social and biological explore the relationship between micro-level
models, particularly models of economics, land agents and behavior at macro-level. The language
use, politics, and population dynamics [10]. has been developed heavily influenced by Logo
The REcursive Porous Agent Simulation and is intended for users from many disciplines -
Toolkit (Repast) is the open and free source of economists, anthropologists, physicists and many
libraries for large-scale agent-based modeling. others. The interface allows users to interact with
Repast supports the development of extremely variables within a simulation and visualize results
flexible agent-based models and is used in social without having to look at the code itself. The
process modeling. Users build their model by language is similar to English, which makes it
incorporating components from the Repast library easy for a non-specialist to understand the
into their programs or by using visual Repast for functionality of each line of code. In addition,
the Python Scripting environment. NetLogo contains an extensive library of models
Repast has a sophisticated built-in scheduler that includes example programs from a wide
that supports discrete-event modeling and allows variety of disciplines, which is very useful for
using a large set of communication mechanisms teaching and learning purposes [11].
with a variety of interaction topologies and AnyLogic is a development of XJTeknologies,
includes a full set of utilities for storing and which has found wide application among users.
displaying agent states. The system also includes The competitive advantage of AnyLogic is the
utilities for automatic integration with both support of all three simulation paradigms and the
commercial and freely available geographic ability to use them within a single model.
information systems (GIS). Integration with AnyLogic also features a powerful productive
commercial GIS includes automatic connection to kernel that can simulate the behavior of millions
widely used geographic information systems such of agents; rich animation and graphical model
as ESRI and ArcGIS. Moreover, since Repast is description capabilities; support for various types
based on the Java language, the Microsoft .NET of experiments, including sensitivity analysis,
platform and Python scripts, it is fully object- Monte Carlo method, built-in OptQuest
oriented [9]. optimizer; integration capabilities with databases,
Swarm was the first agent-based development ERP and CRM systems; a set of library objects
environment. First launched in 1994 by Chris from logistics, business processes, and pedestrian
Langton at the Santa Fe Institute Swarm is an dynamics areas.
open source and free set of libraries and is During developing an agent-based model in
currently maintained by the Swarm Development AnyLogic, the user inputs agent parameters
Group (SDG). The Swarm modeling system (people, companies, assets, projects, vehicles,
consists of two core components. The kernel cities, animals etc.), defines their behavior, places
components run simulation code written in them in an environment, establishes possible
general-purpose language Objective-C, Tcl/Tk, connections and then runs the simulation. The
individual behavior of each agent forms the global Figure 2: Structure of agent-based modeling the
behavior of the simulated system [12]. Covid_19 infection spread
There are also some "templates" that simplify
model creation and are included in AnyLogic: Figure 3 shows the dynamics of epidemic
- standard architecture; spread over time.
- agent-based synchronization ("steps");
- state (continuous or discrete);
- mobility and animation;
- agent-based connections (networks, e.g.,
social networks) and communication;
- dynamic creation and destruction of agents.
AnyLogic provides a graphical language that
greatly simplifies the creation of agent-based
models:
statecharts for specifying agent behavior;
the action diagram for describing
complex algorithms;
the "Environment" element is used to
describe the "world", in which agents "live"
and to collect various statistics;
the "Event" element is used to describe
random or periodically occurring events.
It should be noted that this software was used
in scientific studies regarding the prediction of the
spread of Covid_19 infection in dynamic social
Figure 3: The changing dynamics of epidemic
groups, which are immanently identical to social
networks. These studies showed high reliability of spread results over time.
the obtained results: the experimental results
coincided with the real ones. Fig.2 shows an It should also be noted that these tools allow
example of the agent-based modeling application describing almost all behavioral features of
- model structure of the Covid_19 infection spread agents. Moreover Java language allows
study on a local multiplicity (N = 10,000) of simulating any special behavior or logic. Also the
agents. specific character of AnyLogic is possibility to
combine agent-based models with discrete-event
and system-dynamic models.
That is why the authors of the article propose
to use AnyLogic software for modeling social
networks.
2. Conclusions
Therefore, agent-based modeling allows
creating a model of a social network, where, for
example, social processes such as distribution of
certain information take place, that is information
influence is carried out, and all basic
methodological approaches to the analysis of
social networks are applied. Also, by analyzing
the comparative characteristics of the most
popular agent-based modeling platforms, it was
determined that the most adaptive and multiple-
purpose, as well as supporting a pool of platforms
for optimization, is the AnyLogic platform, which
among other things is designed to model such
complex dynamic systems as social networks and mentalnye_sredstva_agentnogo_modelirova
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