=Paper=
{{Paper
|id=Vol-1113/paper8
|storemode=property
|title=Structural Transitivity of Trust in Academic Social Networks Using Agent-Based Simulation
|pdfUrl=https://ceur-ws.org/Vol-1113/paper8.pdf
|volume=Vol-1113
|dblpUrl=https://dblp.org/rec/conf/eumas/AdamattiCF13
}}
==Structural Transitivity of Trust in Academic Social Networks Using Agent-Based Simulation==
https://ceur-ws.org/Vol-1113/paper8.pdf
Structural Transitivity of Trust in Academic Social
Networks Using Agent-Based Simulation
Diana F. Adamatti 1, 2, Cristiano Castelfranchi 1 and Rino Falcone1
1
Istituto di Scienze e Tecnologie della Cognizione - Consiglio Nazionale delle
Richerce – Roma – Italia
2
Centro de Ciencias Computacionais – Universidade Federal do Rio Grande – Brasil
{diana.adamatti,rino.falcone,cristiano.castelfranchi}@istc.cnr.it
Abstract. Research interest in social networks area can be explained
mainly because this type of network: (i) promotes the interpersonal re-
lationship; (ii) has a natural tendency for knowledge emergence; (iii)
generates large volumes of information. This interest is reinforced due
to the fact that since the 90's Web Social Networks, e.g. Facebook or
Orkut, have millions of users around the world. Our proposal in this pa-
per is to analyze the physical structure of three computer networks
topologies - centralized, decentralized and distributed – in a real aca-
demic social network, the Lattes Curriculum. The main question is how
the network structure could influence the flow of the trust transitivity
between the members of the network. Firstly, we conducted a survey
with researchers about their trust in Lattes Curriculum and later we de-
veloped an agent-based model and simulated it to analyze the resulting
data.
Keywords: Agent-Based Simulation, Trust, Transitivity, Academic Networks.
1 Introduction
In the Information and Communication Technologies (ICT) field the study of trust
has become a topic of increasing interest because most of its developments involve in-
teractions among several autonomous partners for the achievement of the expected
outcomes. Therefore, such outcomes depend on the execution of tasks by several au-
tonomous entities that need to perform them in a robust/deterministic (safe) way.
Thus, the entities involved in the development (human or artificial) have to cooperate
and solve conflicts towards the goal achievement, which together with the knowledge
sharing and management, need to increase the importance of trust [6]. Therefore, in a
scenario in which we do not know exactly with whom (or what) we are interacting, it
is important to define trust metrics in order to help in the decision making process
about cooperation. Several researchers attempted to escalate trust by creating degrees
of mutual trust. However, trust is a subjective function related to the personals’ beliefs
about itself, the environment, and so on, requiring further depth studies about its tran -
sitivity in a network [6].
Transitivity is especially important as in an open net we have to interact with new
or unknown agents, and it is not possible to count on our personal experience and
evaluation, or on some authority's guarantees, or on the explicit recommendation of
another agent. However, there is a network of trust or distrust relations/links; if we
might know and exploit this information we can derive our trust from the other trust
relations. It is a real collective capital: if I know that Y trusts Z, I might trust Z, and so
on (see later for the conditions). Moreover, recommendations and explicit reputation
presuppose a trust in the source (recommender, evaluator): it is precisely this trust in
Y (source) that creates my trust in Z.
Recently, the large-scale use of social networks has also promoted an increase in
available human interaction data, (a capital to be exploited) which consequently stim-
ulated an increased in the number of studies associated to this area despite some stud-
ies date back to 1950’s [16, 29]. A type of such network is the Academic Social Net -
work that keeps records of interactions and collaborations among academic re-
searchers. We consider its main aspect, the social capital that is kept undiscovered in
the represented relationships and the possible high relevance that it may have on the
academic context. Being aware of this unexplored area, some researchers have per-
formed some quantitative studies on this type of network, in which concepts of Social
Network Analysis are used to identify the centrality, density or clustering of the net-
work and to understand the levels of cooperation and collaboration among researchers
[20]. The few studies that deal with the trust aspect consider it from the data integrity
point of view. Hence, the study of trust and its transitivity among researchers is an in -
cipient research topic that has not yet been comprehensively examined in this domain
[1,13, 14, 17, 27].
In the literature there are three topologies for computer networks: centralized, de-
centralized and distributed [3]. For each type there is a different relationship between
its members. These types of topologies are applied in social networks and the most
common is the decentralized structure. Exchanges in social networks can occur in two
different ways: the positive (where A exchanges information with B, and B exchanges
with A, i.e., zero sum), and the negative (where A exchanges with B, but B does not
necessarily exchange with A). The negative one is the most common in this domain
[9].
Since the 80’s social networks structure is a subject that many authors have studied
analyzing the power and dependency between the members of this type of network.
Cook et al. (1983) [11] present a theoretical and experimental analysis performed with
laboratory experiments and computational simulations. Their conclusion is that cen-
tralized models, where one of its members is central, are models where the power is
centralized but are more susceptible to problems if the central member has some prob-
lem. In distributed networks, where all members are connected to all other members,
the power is also distributed. However, these two cases are not the common formation
in reality. Decentralized networks are the most common and they studied where
power is concentrated in this kind of network. In their experiments members known
as "intermediaries" are those who have more power, because they are the link between
agents in the center and in the peripherals places. The authors highlight that for each
“information exchange”, the conditions and values chosen for each member are dif-
ferent.
Whitmeyer (1999) [28] presents a different type of network, called "interest-net-
work structures". In this model in addition to the physical structure an interest vari-
able is associated with the agents. This interest variable indicates the interest of the
agent to exchange information with others (using 1-exchange rule 1). The author con-
siders that the interests are more important to define dependence and power than the
physical structure. However, the physical structure restricts the relationships. For in-
stance, in a scenario where an agent has great interest in other and they are not con -
nected the exchange information will not happen.
In the work of Mizrucchi (2006) [24], a revision about social network and how the
physical structures may influence the forms of interaction is presented. This author di-
vides networks in hierarchical (centralized) and non-hierarchical (distributed, where
everybody can exchange information). However, there is the "subgroups", with sub-
nets (in fact, decentralized networks). He treats about rational choice theory, where
the network members must do the choice that brings the best result (such as an utility
function). However, people do not always do rational choices. Often, friendship, emo-
tions, loyalty, etc., can influence the exchanges.
The goal of this paper is to analyze how the physical structure of networks (based
in the three topologies - centralized, decentralized and distributed) can influence in
the trust transitivity in an academic social network. We have used in our research the
Lattes curriculum. Lattes curriculum is a public available database from Brazilian re-
searchers. In fact, in our project, the main goal is to identify how the structure of the
network could help (or not) the academic information exchange between Brazilian re-
searchers.
Many authors affirm that the structure of the social network cannot define the
power of each member [8, 9, 14]. This is due to the fact that the structure is just a ba -
sic condition to the existence of interactions, i.e., if X has something needed by other
members, but not other member can access it or know that, X has not real power over
the others. Connection is a necessary but not sufficient condition for power. There -
fore, if X potentially might be able to impose something to the others, to obtain from
them what it wants, but it is not connected, it cannot access them, it cannot send or re -
ceive from them, because it is a “missing channel” and it is actually impotent (iso-
lated). Vice versa X is connected to Y but has nothing good or bad for Y, and has no
power over him for exchange, cooperation, threat, ..
Considering these facts, it is clear that it is impossible to measure the network
power based solely in the number of connections.
1
Markovsky et al. (1988) [22] defined the metric “1-exchange rule”, where each
member chooses just one other member to exchange information per round, indepen-
dent of the total number of members. The choice could be a utility function or ran-
domly.
For that reason the focus of this paper is not to find/define the network power. The
main idea is to analyze the physical structure and how it could influence the “flow”
of the trust transitivity between the members of the network.
This paper is structured in 5 sections. In sections 2 and 3, we present the two basis
subjects of this research: network structures and trust transitivity in social networks.
In section 4, we present the proposed model and our preliminary results and section 5
concludes the paper.
2 Structure of Networks and Metrics to Social
Networks
According to Baran (1964) [3] there are three topologies for computer networks:
centralized, decentralized and distributed (Figure 1). Baran's research originated from
security problems in computer networks during the Cold War. His ideas of topology
for networks are still actual nowadays and can be applied to social networks. The cen-
tralized networks, also called Star, are the most vulnerable because they have a central
node which if is destroyed the entire network is lost. Distributed networks, called
Mesh or Grids, are least vulnerable due to their high level of redundancy, where all
nodes are interconnected (a relation n:n). However, the vast majority of computer net-
works is decentralized, namely hierarchical networks, forming small centralized net-
works (subnets). They are less vulnerable that centralized networks but there are some
nodes that can cripple the communications. The structure complexity of these topolo-
gies can be defined as: the simplest level (centralized), intermediate level (decentral-
ized) and more complex (distributed). In Narayanan et al (2013) [25], a special type
of decentralized network is presented, called Federated Networks. This network has a
decentralized topology, forming small centralized networks, but closed, where only
members can access the subnets. In our project, we will work with the three first
types.
Figure 1: Types of Networks [3]
There are many metrics to analyze social networks such as degree centrality, den-
sity, clustering coefficient, giant coefficient, closeness centrality, betweenness cen-
trality, diameter, and so on [15, 20, 23]. In our work three metrics are important to ex-
plain the network structure:
a) degree centrality (dc): when a node has many connections it is considered im-
portant. On the contrary, if the node does not have any connection it is considered ir-
relevant. This degree represents the relational activity of each node. The equation (1)
presents the calculus of degree centrality to ni node.
b) density (d): it is based in the degree centrality. The number of connections of
each node is divided by the total number of connections of the network. The equation
(2) presents the calculus of density to the whole network G.
c) clustering coefficient (cc): represents the number of connections between the
neighbors of a node divided by the total number of connections of the network.
In this way, considering the network structure, we can define:
if d (P) = 1 → distributed network
∃ dc(ni) = N and ∀ d(nj) = 1, and j ≠ i → centralized network
∃ cc(ni) and ∀ dc(nj) => 32, and j ≠ i → decentralized network with subnet
Otherwise → generic decentralized network (with any formation).
3 Trust and Transitivity in Social Networks
In social networks trust has been analyzed as it is an aspect that greatly influences
the process of interaction among their members. There are works that deal with the
transitivity of trust. The main idea is: if X trusts Y and Y trusts Z, then X trusts Z. In
fact, this is not necessarily true. Trust carries not only a degree but it is related to a
content, where the agent has performance and result, and is relative to certain attribute
(qualities or defect) a for that "task/good". This scenario interferes with transitivity
[9]. For this transition to be true the trust relation T between X and Y and between Y
and Z must have specific subjective attributes in a given domain and this rarely hap-
pens [5, 6, 7, 8, 9]. In other terms transitivity is "content and context dependent".
Moreover there should be an effect of convergent or divergent attitudes/evaluation
from different agents/sources. Not only my trust in Y (as evaluator) and Y's trust in Z
can determine my trust in Z; but if also W and Q trust Z? Or if Y trusts Z but W and
Q do not trust Z? Doesn't this affect my derived trust in Z? This is an important factor
in a "network" of trust with many possible trust links on Z.
Liu et al. (2011) [21] present how to calculate the transitivity of trust based on four
parameters: trust, social relations, recommendation rules and preferences similarity.
This work presents some formalizations for the four parameters, defining principles
and properties. A very important property is that the transitivity has a "decay", i.e., if
A trusts B and B trusts C, and if A trusts C, the value of the last trust will be smaller
that the trust value of B and C.
According to Noble et al. (2004) [26], the network topology can influence knowl-
edge transmission. They conclude that in symmetrical networks the transitivity is
higher. However, in real social networks this type of distribution is not realistic.
In the work of Josang et al. (2006) [18], a quantitative formalization of forms to in-
terrelation between A and B with C is done. There are three different formalizations:
Dependent Opinions (A and B have the same beliefs about C); Independent Opinions
(A and B have different beliefs about C) and Partially Dependent (A and B have simi-
lar beliefs about C, but not identical).
2
According to Barabasi and Albert (1999), 3 links is a typical number of connections to large
networks. In fact, they identified a typical interval [2.1, 4] and they used 3 connections, be-
cause it is the average of this interval. They also defined this type of network as “scale-free”
[4].
4 Proposed Model
In our project we try to identify how the structure of the social networks can influ-
ence the trust transitivity. To achieve that we have defined some steps:
• Step 1: Define a survey to capture the trust in the Lattes Curriculum and apply it
to researchers in different knowledge areas;
• Step 2: Analyze the survey data and define “Trust Types”, i.e., divide the re-
searchers according to their trust (they trust more in one or other type of information
in Lattes);
• Step 3: Verify if co-authors or co-organizers have influence in the trust, to ana-
lyze the “weight” of transitivity in the trust;
• Step 4: Develop a computational simulation to the trust types using the parame-
ters obtained from the real data. For example, if 20% of the researchers think that
“formation” influences the interaction, 20% of the agents will be implemented using
this influence.
• Step 5: Analyze real and simulated data
Until now we have concluded the steps 1, 2 and 3, and the first proposed model
with preliminary results.
4.1 Lattes Curriculum: the Academic Network in Brazil
The Lattes Platform (lattes.cnpq.br) is an information system developed and main-
tained by CNPq (National Council for Scientific and Technological Research). The
main idea is to manage the science, technology and innovation in Brazil [10]. The
first version was released on August 1999, with the initial version of the Lattes Cur-
riculum. Recently, the Lattes Platform was cited as an example of complete database
with highly qualified information in Nature [19].
This platform is composed by the integration of four separate systems: Lattes cur-
riculum, which records the academic life of the researchers; research groups directory,
which maintains information about the research groups in the country; institutions di-
rectory, which stores information on research institutes, universities, etc; and finan-
cial/promotion management system, which manages the requests of financial/support
to researchers [2]. In our work, we have interest is Lattes curriculum. This is struc -
tured hierarchically in the following topics [12]:
• General Data and Formation: data identification, addresses, academic formation,
research areas;
• Bibliographic Production: all publications of the researcher as papers in journals
and/or conferences and books;
• Students Oriented: guidance and supervision (completed or in progress);
• Projects: projects of the researcher, with a abstract and members;
• Technical Production: software, products, technical reports;
• Events: information related to events that the researcher organized or participated;
4.2 Lattes Survey
The goal of this survey was to determine how the information provided by the re-
searchers can influence the decision-making for new activities and/or to find works by
the researcher, based on Lattes information trust.
We defined quantitative questions (with the possibility of qualitative information).
For all questions, the parameters are: strongly influence, more than the average influ-
ence, little influence,very little influence and no influence.
The qualitative information could be written in a field “Justification”, if the re-
searchers would like to express better their ideas. This survey was developed to Web
platform. In this way, researchers could access it remotely via the following address:
http://diana.c3.furg.br/index.php?Itemid=1982&option=questionario&id_site_com-
ponente=3090.
This link was sent to several mail lists, nationwide, for researchers from various
fields of knowledge. Table 1 presents the questions' survey which were based in the
main topics of Lattes curriculum.
Table 1: Questions Survey about Lattes Curriculum
Q1: Do the formation (university/institute, research area) of the researcher influence your in-
teraction with he/she?
Q2: Do the places where the researcher work(ed) influence your interaction with he/she?
Q3: Do the research areas of the researcher influence your interaction with he/she?
Q4: Do the researcher projects (in progress or completed) influence your interaction with
he/she?
Q5: Do the researcher H-index, or the citations total in databases (Web of Science or SCO-
PUS), or the impact factor of the papers influence your interaction with he/she?
Q6: Do the publishers that the research have papers/chapters influence your interaction with
he/she?
Q7: Do the technical production of researcher, as software, courses, technical reports influ-
ence your interaction with he/she?
Q8: Relating to all types of productions, do the co-authors of them influence your interaction
with he/she??
Q9: Do the quantity of organized events by the researcher influence your interaction with
he/she?
Q10: Do the co-organizers of events influence your interaction with he/she?
Q11: Do the researcher quantity of students oriented influence your interaction with he/she?
Q12: Do the researcher skills to organize events influence his/her skills to:
a) manage projects?
b) write academic or technical papers?
c) student oriented?
Q13: Do the researcher skills to write academic or technical papers influence his/her skills to:
a) manage projects?
b) organize events?
c) student oriented?
Q14: Do the researcher formation and his/her professional performance influence his/her skills
to:
a) manage projects?
b) organize events?
c) student oriented?
d) write academic or technical papers?
The survey was made available for two weeks and 94 researchers answered it.
These researchers are from all areas of knowledge. The majority of researchers de-
tailed their answers in justification field (qualitative responses) and presented “the
reasons” for the quantitative choices helping us to better understand all the process.
Table 2 presents the consolidated data to all answers with the percentage of each
item in each question. We can observe that some questions produce contradictory an-
swers between the researchers. For example, question 2 has a percentage indicating
little influence and more than the average near each other, related to the places where
the researcher works. In some questions, as question 13-c, the most of researchers
think that write papers influences in the student oriented.
The evaluation of Brazilian research has a quantitative role as researchers' work.
However, to the same researchers, the questions that cover publications are not as im-
portant for interaction/trust to themselves, as presented by questions 5, 6 and 7.
Considering the trust of Lattes information, and the claim not of an empty "trust"
but "trust for", with a content, an aboutness, the transitive relation between co-authors
and co-organizers was not confirmed. According to the answers was not possible to
conclude that the researchers believe that papers co-authorship indicates that a re-
searcher is trustful to interact as well as to events co-organization. For both cases,
several open answers highlights that a very large number of co-authors or co-organiz-
ers indicates that some people do not really participate in the processes, i.e., they just
put their name in these activities. Specifically related to events, most answers were
not positive and show that there are two "profiles" in academic: the scientific/techno-
logical and administrative. Organizition of events is considered an administrative ac-
tivity.
Taking in consideration the main topics we can conclude that the most of re-
searches have trust for:
TRUST-FOR formation
TRUST-FOR research areas
TRUST-FOR projects
TRUST-FOR students oriented
And, taking into account the questions 12, 13 and 14, the researchers have trust
transitivity between the following topics:
TRUST-FOR events → to manage projects
TRUST-FOR events → to orientate students
TRUST-FOR production → to manage projects
TRUST-FOR production → to orientate students
TRUST-FOR formation → to manage projects
TRUST-FOR formation →to orientate students
TRUST-FOR formation → to increase the production
The relations described above means, for example, that if the researcher has trust in
other to organize an event, he/she believes that it could manage projects in a good
way (transitivity between different topics/activities).
Table 2: Perceptual of answers to each question to Survey
4.3 Computational Model and First Results
We designed an agent-based model in the platform NetLogo (www.netlogo.com)
in order to test and to analyze the network structures in transitivity trust.
In the graphical interface of the model, the user can choose the following variables:
1. the topology of the network: centralized, distributed, decentralized nets or
decentralized (as defined in section 2).
2. the number of agents: [1;500]
3. the number of neighbors: [3;100]. It is used just to decentralized nets, where
the user can define the minimum number of neighbors in the subnets (in our
definition in section 2, the minimum number of neighbors is 3)
Each agent has the following internal characteristics (beliefs):
1. TF – formation trust: boolean
2. TR – research trust: boolean
3. TP – project trust: boolean
4. TS – student trust: boolean
5. TE – event trust: boolean
6. TPu – publication trust: boolean
7. Collaboration: integer
Observing the survey data, the agents received in the beliefs a percentage of TRUE
(strongly or more than the average influence) or FALSE (little, very little or no influ-
ence). In this way, the proportion used was:
1. TF : 75% true and 25% false
2. TR: 80% true and 20% false
3. TP : 70% true and 30% false
4. TS: 65% true and 35% false
5. TE: 20% true and 80% false
6. TPu: 45% true and 55% false
In each round, each agent chooses a topic to exchange (formation, research,
project, student, events or publication) with other agent. These two choice (topic and
agent) are random and based in the metric “1-exchange rule”. If the value of the belief
is “true” for the chosen topic, we will increment the belief “collaboration”. As “col-
laboration” we understand that the transmission of the information, without a depen -
dency or a real power metric.
Before to include the transitivity, we tested the following hypothesis, basing just in
the physical structure of the network:
• in distributed networks, because all nodes are connected, the collaboration
will be high.
• in centralized networks, everything depends of the central node (choosen
randomly), but the collaboration will be between high or intermediate.
• in decentralized networks with subnets (minimum of 3 neighbors), the col-
laboration will be intermediate.
• in decentralized networks (with any formation), the collaboration will be
low.
We used networks with 100 agents each and run it 20 times with 100 rounds each.
The average values to each topic to collaborate are presented in Table 3.
Table 3: Results to Trust in Structured Networks
Network TF TP TR TS TE TPu
Distributed 1241 1172 1349 1107 347 752
Centralized 1674 1643 1611 1730 32 1733
Decentralized 1236 1165 1326 1145 304 726
Net
Decentralized 1220 1101 1298 981 231 699
In Table 3, the topology with higher values was Centralized. However, to TE (trust
in events), the values in centralized networks was very low. It happens because the
proportion of TE with true values is very low (20%), and all communication pass to
central agent (randomly chosen) and each agent will have just 20% of chance to
choose this central node with TE true.
To Decentralized Nets and Distributed networks, we have values very similar. It
could be shown that if the node have a minimum number of connections, the collabo -
ration will be realized (not be necessary to know all network nodes to have collabora-
tion).
In a second step, we included in the model the transitivity. After choosing the topic
to exchange and the first neighbor, the agent will choose a “neighbor of the
neighbor”. If the value of the belief is “true” for the chosen topic in the “second”
neighbor, we will increment the belief “collaboration” (see Figure 23) .
Figure 2: Steps to implement the transitivity in the model
In this second test, the proportion of trust is static, as presented above. Again, we
used a network with 100 nodes each and run 20 times with 100 round each. The aver-
age values to each topic to collaborate with transitivity are presented in Table 4.
Table 4: Results to Transitivity Trust in Structured Networks
Network TF TP TR TS TE TPu
Distributed 901 781 1103 722 354 49
Centralized 1281 1158 1281 1095 781 31
Decentralized 931 801 1042 702 322 42
Net
Decentralized 823 741 1031 681 296 32
The results of Table 4 confirm that with trust transitivity the hypothesis about the
structure of the network. In centralized networks we have the higher values, after dis-
tributed and decentralized net. The lower level of exchange is in decentralized net-
works. The values with transitivity are lower than we have just trust (Table 3), be-
cause there is the “decay” with transitivity, according to Liu et al. (2011).
5 Conclusion
In this paper, we proposed a model to analyze the influence of the physical struc-
ture of the network to trust transitivity. Taking the trust percentage for each topic of
Lattes curriculum, we have used the survey data (real data) in a static way (they do
not change during all simulation). The choice for a topic and for other agent are ran -
domly, as well as, the formation of the decentralized network (choose the neighbor).
In this first model, in generic decentralized networks, some agents could not have
connection with any other agent, and this do not change during all simulation. In real
life, people could “create” new connections. Is the transitivity the key to generate
these new exchanges?
Besides, the perceptual of trust is static. However, these values could be increased
or decreased depending on the old interactions (they have a memory). For example, if
an agent interaction happens many times in a positive way with other node, could they
created a loyalty?
In our first insights, we can conclude that the physical structure of the network in-
fluence in the transitivity trust. It can be obvious that centralized and distributed net-
works have the higher values of collaboration but in decentralized networks (specially
with nets), the values are almost similar to distributed one. It is other important re -
search question: does a biger degree of centrality is better to the trust transitivity? Our
first results presented that, if a node has a minimum number of neighbors, the ex-
change will happen. However, the power of one node on the others is not simply de-
pendent on the number of connections, and power is directly linked to trust [9], and it
must be better investigated.
Another aspect that we must look for is about the comparison between “not realis-
tic” and real-data models. According to Cointet and Roth (2007) [30], the diffusion of
knowledge is slower in real-data. In this way, we will test the proposed computational
model with the Lattes real-data.
Acknowledgments
Diana F. Adamatti is supported by CNPq (Conselho Nacional de Desenvolvimento
Cientifico e Tecnologico) – Brazil, process number 240181/2012-3.
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