The Bio-Inspired and Social Evolution of Node and Data

in a Multilayer Network

Marialisa Scatà

, Alessandro Di Stefano

, Evelina Giacchi

, Aurelio La Corte

and Pietro Liò

Department of Electrical, Electronics and Computer Science Engineering,

University of Catania, A. Doria 6 street, Catania, Italy

Computer Laboratory, Department of Computer Science, University of Cambridge,

15 JJ Thomson Avenue, Cambridge CB3 0FD, U.K.

Keywords: Bio-Inspired, ICT, Social Networks, Multilayer Networks, Data Mining, Comorbidity, Game Theory,

Decision Making, Health.

Abstract: Following a bio-inspired approach, applied to multilayer social networks, the idea is to build a novel

paradigm aimed to improve methodologies and analysis in the Information and Communication

Technologies. The social network and the multilayer structure allow to carry out an analysis of the complex

patterns, in terms of the dynamics involving the main entities, nodes and data. The nodes represent the basic

kernel from which generating ties, interactions, flow of information, influences and action strategies that

affect the communities. The data, gathered from multiple sources, after their integration, will become

complex objects, enclosing different kinds of information. The proposed approach introduces a level of

abstraction that originates from the evolution of nodes and data transformed in “social objects”. This new

paradigm consists of a multilayer social network, divided into three layers, generating an increasing

awareness, from “things” to “knowledge”, extracting as much “knowledge” as possible. This paradigm

allows to redesign the ICT in a bio-networks driven approach.

1 INTRODUCTION

The new ICT paradigm is expected to contribute to

the process of improvement in the realization of a

knowledge-based networking, characterized by

innovation, making the networks sustainable with

processes based on a strategic bio-inspired approach,

considering also the social, human and cognitive

aspects. The future network needs to meet some

requirements, such as ubiquity, mobility,

dynamicity, reliability. The ubiquitous nature leads

to a logical fusion and integration of different

aspects of real and online social network platforms.

The network nodes acquire a common representation

through identity features. These features, following a

bio-inspired approach, enclose genotypic and

phenotypic traits. In addition to these traits, it is

important to consider also context-aware

capabilities, self-organization, self-protection,

perception, decision-making processes and cognitive

behavior. Considering these features, the nodes

interact through social networks and they are able to

self-organize dynamically in communities and

groups, based on aggregation metrics. We think that

the node is an abstraction, an object which collects

bio-inspired features as well as human and social

capabilities. Similarly, data shared inside the

network are a complex object, like a box, which

travels across the network through interactions

between nodes. Data and nodes represent the objects

that trigger influence, interaction, contagion and

decision criteria. These two entities enclose many

different social aspects. In the future of ICT we will

expect to be able to obtain context-aware services,

which stem from a bio-inspired approach able to

drive methodologies for analyzing social complex

networks. We propose in this paper an evolutionary

perspective of nodes and data, an innovative

multilayer perspective. To solve the heterogeneity

issue of these entities, we propose a social object

oriented approach, following the bio-inspired

principles in a multilayer social network. The paper

is organized as follows: the second section is about

background; in the third section we will explain the

social object oriented evolution of networks and the

novel bio-multilayer network schema; in the fourth

Scatà M., Di Stefano A., Giacchi E., La Corte A. and Liò P..

The Bio-Inspired and Social Evolution of Node and Data in a Multilayer Network.

DOI: 10.5220/0005119600410046

In Proceedings of the 5th International Conference on Data Communication Networking (DCNET-2014), pages 41-46

ISBN: 978-989-758-042-0

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

section, we will focus on future directions

challenges and strategies of this work and finally we

conclude with some considerations.

2 BACKGROUND

2.1 Social Networks Analysis

The social network analysis is an analytical tool

used to understand how highly connected systems

and entities, which form social links and networks,

operate (Aggarwal, 2011). It considers social

relationships in terms of network theory, with nodes,

representing the individual actors within the

network, and ties (referred also as edges, links, or

connections), which are the relationships between

the individuals. The resulting structures are complex

graphs connecting social contacts, and the graph

theory, from a structural point of view, is able to

describe these relationships using metrics, such as

betweenness, centrality, degree, closeness, clustering

coefficient, etc. The power of social network

analysis is that it produces a different view, where

the attributes of individuals are less important than

their relationships and ties with other actors within

the network. Furthermore, the behavioral dimension

means that the individual’s actions have to be

evaluated not in isolation, but considering the

connections with the other players, who can use

different strategies (Easley and Kleinberg, 2010).

All these structural and behavioral aspects have to

cope with the network dynamics, so that connections

and behaviors between nodes change over the time,

increasing the complexity of the analysis. Since a

significantly larger amount of data is available for

the case of online social networks, these networks

have made much more robust, in terms of statistical

significance, the verification of some structural

properties, such as the small world phenomenon,

preferential attachment, and other structural

dynamics. The community detection is one of the

most well-known structural problems in the context

of social networks (Fortunato, 2009); it is closely

related to the problem of finding structurally related

groups in the network, called communities. In order

to understand the complex dynamics of social

networks, in this work we suggest to change the

perspective of analysis, and evolving the concepts of

“node” and “data”, the bio-inspired social object,

which includes the social perspective and the bio-

inspired approach. Considering a data-centric

perspective, the social network analysis helps to

transform a context-aware object in a data “social”

object, while in a node-centric perspective, by

analyzing communities and their evolution in social

networks, we can introduce cognitive features in the

nodes, obtaining a node “social” object.

2.2 Multilayer Networks

Almost all real and virtual systems are inherently

composed of multiple layers (or subsystems), which

contribute to the wholeness of their functionality but

can also be considered as systems in their own right.

Network science has been largely successful in

abstracting meaning from single-layer subsystems

(Newman et al., 2009) and it is only recently that

multilayer networks (Kivelä et al., 2010) have

become a popular paradigm for the modeling of

interrelated subsystems and entire systems. This is

largely due to the capability of multilayer models for

understanding the bigger entity more realistically. In

fact, most real world entities are connected each

other in more than one way, from transportation

systems to social relationships.

Figure 1: Bio-Inspired Multilayer Network for ICT. (See

Text).

Figure 2: Dynamic Complex Patterns of the Multilayer

Structure. (See Text).

DCNET2014-InternationalConferenceonDataCommunicationNetworking

This multiplexity, or the complexity caused by the

existence of more than one link between entities, has

been observed across multiple fields. In social

networks for instance, different and not mutually

exclusive relationships can be considered between

the same two people (e.g. friends, relatives,

colleagues, etc). Although social relationships are

difficult to measure and such rich information about

all the ties are rarely available, modeling social ties

considering only one dimension does not capture the

realistic human social dynamics. Networks, where

occur multi-edges of different types between the

same nodes, are referred as multiplex networks, or

more generally multilayer networks, since each type

of edge can be abstracted to either an independent or

interdependent layer (Kivelä et al., 2010). The real

potential of multilayer networks, in terms of

applications and value, has not been exploited yet.

One of the goals of the present work is to build and

interpret a multilayer network model able to detect

and separate the different layers and dimensions of

analysis of a bio-inspired network-oriented ICT

model.

3 A MULTILAYER APPROACH

SOCIAL OBJECT ORIENTED

The proposed paradigm in this paper is the result of

a biologically-inspired approach applied to complex

social networks. Exploiting a multilayer architecture,

it consists of an evolution process that involves both

nodes and data. This approach starts from the

consideration that all techniques and models related

to information and communication have to be based

on what governs the network dynamics. These

processes in a social-based context are determined

from nodes and data. This requires an evolution of

these entities inside a multilayer network which

shows the patterns of the different relationships and

interactions among nodes and communities, through

the sharing of data. The main goal is to solve all the

issues related to heterogeneity, which can be an

obstacle to more complex and deep analysis of

networks. The network is characterized by a

multitude of nodes of different nature, and by a

multi-channel data collection. The aim is to obtain

an evolution of the networks considering bio-

inspired social objects. This evolution will become

useful to enable the ICT procedures to be in line

with the bio-inspired processes that rule the complex

social network. The future ICT will be driven from

these objects creating strategies and applications

with an innovative and dynamic approach. The Fig.1

illustrates a multilayer social network, divided in

three different layers. The three layers show the

same topology but actually it could be different

across layers. The first layer is the social layer,

characterized by interactions between nodes through

the sharing of data. In the second layer we introduce

the comorbidity perspective, which is a medical

concept referring to the co-existing of different

diseases in the same subject, it creates other different

relationships between nodes, that represent

patients/individuals who share the same diseases or

morbidities. In the third layer we consider the ICT in

terms of interventions which involves the single

entities and the system as a whole. In this

perspective, the multilayer organization enables us

to analyze the complex patterns of analysis. Starting

from a simple node which interacts with other nodes,

we can obtain an evolution and a growing

awareness. The Fig. 2 shows the dynamic patterns as

the result of the coupling effect of interdependent

layers. Only by studying the inter-layer interactions

between nodes, it is possible to detect the emergent

behaviors and focus on the key features related to

data and nodes, from which these patterns are

generated. One perturbation in one layer could drive

changes in the other layers through interactions. The

evolution process from node and data to “social

objects”, is indicated in Fig. 3. The “social objects”

pave the way to the higher level of awareness,

referred as “knowledge”, the abstraction of the

outcome of the flow of information and social

objects. The social objects merge together all the

different cognitive, social and human aspects and the

various contexts. The node, in the proposed

paradigm, becomes an abstract object which

contains any kind of presence and/or participation in

the social networks. This can encompass simple

network nodes, both hardware and software, IoT

sensors, human nodes, etc. The node’s presence is

defined as a set of bio-inspired features, such as

genotype and phenotype. The genotype is

represented by the immutable traits of that object.

The phenotype is a combination of observable

features, behavioral manifestations of genotype, and

the result of interactions between genes,

environment, and random factors. The multitude of

heterogeneous nodes, with capabilities of self-

organization, through mechanisms of aggregation

and clustering techniques, becomes an organized

structure of communities and groups. Enabling

context-awareness and cognitive capabilities, the

nodes become smart, able to decide their strategies

inside and outside the communities. Adding abilities

TheBio-InspiredandSocialEvolutionofNodeandDatainaMultilayerNetwork

Figure 2 3: Evolution of Node and Data. The evolution, involving data and nodes, is a process which starts from

disaggregated and heterogeneous things, and gets the social objects and finally, the knowledge for the ICT.

extracted from complex social networks analysis, in

terms of emerging behaviors, we will obtain the

abstraction, which is the social object node. The

data, the other entity of the network, are any kind of

collected information, useful to network analysis.

The data could consist of statistical data, data

gathered from sensors, social data, derived from

online and real social network platforms. Collecting

data may be relatively easy, but the complexity

arises in combining and integrating datasets from

multiple sources and different contexts, in order to

extract the real knowledge about networks. This is

the reason why, as we will explain in the next

section, we need a complex mining, able to fuse and

integrate in a unique structure these heterogeneous

data, collected from different sources and of

different nature. Furthermore, we have to integrate

data considering the different contexts and

environmental conditions in which these data are

generated, considering who created them and for

what purpose, so we have to consider a context-

aware data mining, related to how attributes should

be interpreted according to the different contexts.

4 FUTURE STRATEGIES AND

APPLICATIONS

4.1 Knowledge Mining for Health

Public Health and Clinical Interventions

Management is one of the future targets of the ICT

and networking. The aim is to improve the

efficiency of some processes related with this

context, optimizing methodologies and procedures,

providing technological support systems. Social

network phenomena appear to be relevant in the

health context, in particular in the biological and

behavioral traits linked to diseases. Some diseases

appear to spread through social ties (Christakis and

Fowler, 2007), so the importance of considering the

structure of social ties and the social contagion

process for a better understanding of the subtle and

deep processes underlying these social phenomena.

These issues imply an increasingly better evaluation

of the methodologies of analysis of the social

networks. In fact, a future changing in relationships

can affect the extraction techniques in the node

behaviors, relating them to the diseases that arise

and, subsequently, evaluate the relationships

between them, and the co-occurrence in the same

individual, that will be defined and treated in the

next section. Therefore, this complex analysis

involves multiple levels of awareness, from the

nature of nodes, in terms of behavior and biological

traits (genotype and phenotype), to their behavior

when organized in communities. In this scenario,

nodes and data become the entities to focus on,

looking for both the extraction of behavioral rules

and the data mining processes. The level of

abstraction proposed in this paper allows to treat the

network entities as social objects and then, the use of

appropriate techniques for the collection, integration

and analysis of data, would make possible the

realization of a higher level of mining (Scatà, Di

Stefano and La Corte, 2014). The weights assigned

to these interactions and the social dynamics, such as

the social contagion related to diseases in this

scenario, could be crucial for extracting more

knowledge related to the probability that some

individuals, for example the ones who are more at

risk than others, are going to contract certain

diseases. This deeper analysis would be unthinkable

using the traditional methods. The diagnostics is

driven by a complex system of analysis that gathers

and integrates the traits that characterize the nodes

and data.

DCNET2014-InternationalConferenceonDataCommunicationNetworking

4.2 Comorbidity and Social Behaviors

Comorbidity means the co-occurrence of one or

more different diseases in the same patient.

Comorbidity represents an extremely complex field

of research, and the complexity arises because of the

complex relationships between conditions, disorders

or diseases, all related to a single individual/patient.

These conditions are often independent each other,

in fact there could be no pathological association

among them, but sometimes the relation is very

strong and it is due to direct or indirect causal

relationships and the shared risk factors among

diseases (Tong et al., 2007), more than a chance

alone. The complexity is also due to the uncertainty

that depends on the number of associated

morbidities (Capobianco E. and Liò P., 2013).

Following the definition provided by the US

National Library of Medicine, comorbidity is

considered to be a secondary diagnosis, detected

simultaneously or one after another in the same

patient. Comorbidity is associated with multiple

different symptoms, decreased length and quality of

life and increased healthcare management (Islam M.

et al., 2014). The study of comorbidity relationships

between diseases could be exploited to build a

model for predicting the diseases an individual may

develop in the future, so the idea would be to

prevent or at least inhibit the future occurrence. The

analysis of comorbidity has to consider different

dimensions morbidities (Capobianco E. and Liò P.,

2013): the clinical dimension, related to diagnosis

and treatment; the therapeutic dimension, which

seeks to restore the steady state but can add

complexity based on the reaction to drugs or

interventions; the genetic dimension, which looks

for the molecular causes, the levels of gene

expression, susceptibility and risk factors; the omics

dimension, which highlights the common causes

behind comorbidity and it consists also of a

functional analysis based on gene sets. It is

necessary to consider the computational aspects, as

inferential approaches may help to identify the

direction of causality, for example, using evidence

synthesis. In the future work, we want to add another

dimension, the social one, in fact we will evaluate

how social behaviors can influence the evolution of

patients in terms of comorbidity. One of the ideas

underlying this paper, in terms of comorbidity, is to

highlight also the psycho-cognitive aspects of the

individual (node) that will influence his behavior

against illness, empowering patients in the

healthcare decision process (Gorini A. and

Pravettoni G., 2011). The way to conduct this social

interaction analysis could be using theoretical and

analytical tools, such as decision-making and game-

theoretic approach, that we will introduce in the next

subsection.

4.3 Decision-Making and

Game-Theoretic Strategies

The decision's criteria depend both on individual

knowledge and social context, intrinsically linked

and dependent each other. Thanks to its individual

knowledge, the node, acquiring awareness of the

context and the environment to which it belongs, is

able to analyze the information received by its

neighbors, evaluating their actual behaviors and

predicting the future ones. The node, once

established its objective, compares all the

alternatives using its set of decision's criteria, among

them it is also possible to establish a hierarchy,

giving a different weight to each single criterion and

creating a scale of preferences. Then, the node can

choose the best alternative trying to improve its

individual utility and also the one of the community.

The evaluation could be conducted also using simple

heuristics (Bagnoli, Guazzini and Liò, 2007).

Another future challenge in the multilayer structure

is to model interactions between nodes in a

comorbidity scenario. Game theory and the game-

theoretic approach could represent an analytical tool

that helps us in studying these interactions,

considering for instance a multi-player game, where

each player is an individual/patient who has

simultaneously multiple co-occurrent diseases or

morbidities which are in competition each other. The

competition depends on the fact that the selection of

a treatment or procedure could influence another

morbidity because of the side-effects of drugs.

Figure 4: Future directions of social-object oriented

paradigm.

In this competing scenario, at each time step the

patient has to choose his strategy, considering

simultaneously different decision parameters, related

TheBio-InspiredandSocialEvolutionofNodeandDatainaMultilayerNetwork

not only to himself but also to his community, as

explained before, so there is no a strategy that is

absolutely better than the other ones.

5 CONCLUSIONS

The evolution process, that involves nodes and data,

enables us to give a new definition, which tries, first

of all, to solve the issue related to the management

of heterogeneous data and different nodes. In Fig. 4

we show the future directions of social-object

oriented paradigm. The social multilayer network

allowed us to analyze the complex dynamic patterns

involving these entities, shedding light on the

different types of interactions at various layers. The

multiplex structure, consisting of three layers, the

social layer, the comorbidity layer and the ICT layer,

allows to consider, respectively, the social

interactions and the social contagion between nodes

through the sharing of data, the comorbidity

relations between diseases, and the ICT

interventions as a result of the analysis of the

complex patterns involving entities, the context and

the system as a whole. The multilayer social network

paradigm describes the evolution of data and nodes,

considering an increasing level of awareness, from

things to knowledge between social objects nodes

through the social objects data. This evolution

process leads to a bio-inspired network-driven ICT,

redesigning the ICT communication paradigm.

Future works will be focused on a complex analysis

which accounts for the different aspects and data to

get a knowledge-based mining, supported by

datasets collected related to this context. The topic,

which we are going to develop, is linked to health

and also other applications and we will focus on

studying and modeling the decision-making

processes using a game-theoretic approach, in order

to analyze from this new social perspective the issue

of comorbidity and the influence of social behaviors.

ACKNOWLEDGEMENTS

This work was supported by "Programma

Operativo Nazionale “Ricerca & Competitività”

2007-2013” within the project "PON04a2_C –

Smart Health”.

REFERENCES

Aggarwal C. C., 2011. Social Networks Data Analytics,

Springer.

Easley D., Kleinberg J., 2010. Networks, Crowds, and

Markets: Reasoning about a Highly Connected World,

Cambridge Univ. Press.

Fortunato S., 2009. Community Detection in Graphs. In:

Physics and Society.

Newman M., Barabasi A. L., Watts D. J., 2009. The

Structure and Dynamics of Networks. Princeton Univ.

Press, Princeton and Oxford.

Kivelä M., Arenas A., Barthelemy M., Gleeson J., Moreno

Y., Porter M. A., 2014. Multilayer networks. In

Physics and Society.

Miorandi D., Sicari S., De Pellegrini F., Chlamtac I., 2012.

Internet of Things: Vision, Applications and Research

Challenges, In: AD HOC Networks, 10(7), pp. 1497-

1516.

Christakis N. A. and Fowler J. H, 2007. The Spread of

Obesity in a Large Social Network over 32 Years. In:

New England Journal of Medicine, 357, p. 370-379.

DOI: 10.1056/NEJMsa066082.

Di Stefano A., Scatà M., La Corte A., 2014. Health

Mining: a new a new data fusion and integration

paradigm. In CIBB 2014.

Tong B., Stevenson C., 2007. Comorbidity of

cardiovascular disease, diabetes and chronic kidney

disease in Australia. In Australian Institute of Health

and Welfare, Canberra.

Capobianco E. and Liò P., 2013. Comorbidity: a

multidimensional approach. In Trends in Molecular

Medicine, 19 (9), pp. 515-521.

Islam M. et al., 2014. Multimorbidity and Comorbidity of

Chronic Diseases among the Senior Australians:

Prevalence and Patterns. In PLoS ONE, 9(1), doi:

10.1371/journal.pone.0083783.

Bagnoli F., Guazzini A., Liò P., 2007. Human heuristics

for Autonomous Agents. In BIOWIRE 2007, LNCS

5151, Springer-Verlag, Berlin Heidelberg, 2008, pp.

340-351.

Gorini A. and Pravettoni G., 2011. P5 medicine: a plus for

a personalized approach to oncology. In Nature

Reviews Clinical Oncology, published online 31 May

2011; doi:10.1038/nrclinonc.2010.227-c1.

DCNET2014-InternationalConferenceonDataCommunicationNetworking