2014 International Conference on Intelligent Networking and Collaborative Systems
Improving the Compactness in Social Network Thematic Groups by exploiting a Multi-Dimensional User-to-Group Matching Algorithm Pasquale De Meo DICAM University of Messina Messina, Italy Email:
[email protected]
Fabrizio Messina DMI University of Catania Catania, Italy Email:
[email protected]
often producing an overwhelming amount of undesired messages/posts directed on user account. In addition, users often do not like to deal with generic/vague information but they prefer to focus their online activities to find specific information capable of fitting their information needs. Due to these reasons, users are quite prone to form thematic groups in which the discussion is focused on a given topic. The formation of groups between OSN members is a key step to elucidating the internal organisation of OSNs. Nevertheless, it is common that groups are constructed in a very confusing way [3], due to occasional interactions between the components that, however, do not necessarily imply the formation of aggregation units of who share similar interests or mutually trust each other. As a consequence, a user might be involved in undesired activities promoted by other users. The aforementioned phenomenon of the viral diffusion of certain topics is an example of such a problem. The promotion of a given topic is desirable from the viewpoint of its producers but undesirable from those users not interested in it, considering it as spam. This issue is particularly important when forming thematic groups, where there is an additional information (the topic) to be considered in the group formation process. The users of a thematic group only want to perform activity dealing with a particular topic (e.g. music, sport, politics etc.) In this context, we argue that a multi-dimensional organisation of an OSN, in which each dimension represents the projection of the network on a given topic, could improve the compactness between the users that belong to the same aggregation unit. Therefore, in this paper we propose to provide each user with a software agent associated with each topic of interest for him/her, and that represents a user’s avatar in the corresponding dimension. It allows the user to delegate to his/her agent the filtering of the content and the management of the joining requests regarding a given topic on the specific dimension, by selecting only those interlocutors which appear the most appropriate for their owners. In our approach we provide a Users-to-Group matching algorithm allowing the agents to dynamically assist the group formation within the OSN.
Abstract—The internal organization of an Online Social Network is well described by the formation of groups between members. Often groups evolve in a very confusing way, due to occasional interactions between their components. However, it does not necessarily imply the formation of aggregation units in which users have similar interests and behaviour and, contextually, mutually trust with each others. Users can form groups (or join already existing groups) on the basis of shared interests or because dense social connections exist among group members; however, it is not uncommon the birth and growth of thematic groups, i.e., those groups arising from the social aggregation of users around a specific topics of interest. In this context, we argue that a multi-dimensional organization of the social network, in which each dimension represents the projection of the network on a given topic, could facilitate the task of forming compact groups. In this paper, after defining a notion of compactness for a group, that integrates similarity and mutual trust, we propose to provide each user with a software agent associated with each topic of interest for the user, and that represents a user’s avatar in the corresponding dimension. This allows the user to delegate to his/her agent the management of group joining requests regarding a given topic, selecting only those interlocutors which appear the most appropriate for their owners. In our approach a Users-toGroup matching algorithm allows the agents to dynamically manage the evolution of the social network organization. Some experiments on real data clearly show the advantages introduced from our approach in assigning the users only to groups compatible with their orientations. Keywords-Distributed system; Multi-agent system; Social Networks; Trust systems.
A very exciting issue in current social network studies, with a big impact from a social, political and economic perspective [2], [27], [31], is represented by the formation of thematic groups. For this reason, in several popular Online Social Networks (OSNs) like Facebook [8] and Twitter [36], large amounts of users are involved in incessant conversations, sharing and spreading information and ideas related to particular topics. Interestingly, some of these topics acquire in time a global fame, due to a “viral” diffusion on the networks. The possibility of producing, sharing and consuming content on a large scale has, however, some unwanted effects. In fact, a large amount of content often translate into spam, 978-1-4799-6387-4/14 $31.00 © 2014 IEEE DOI 10.1109/INCoS.2014.71
Domenico Rosaci, Giuseppe M. L. Sarn´e {DIIES, DICEAM} Universit`a Mediterranea di Reggio Cal. Reggio Calabria, Italy Email: {domenico.rosaci, sarne}@unirc.it
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In detail, we propose a distributed approach relying on a multi-agent architecture to assist the users of an OSN in managing the activities corresponding to a given topic. The main idea underlying our approach is that of building, for each possible macro-topic of interest (e.g., politics, sport, arts, culture, etc.) a different “twin” social network of agents upon the real OSN, thus creating a multi-dimensional social network configuration. Each user agent in the twin social network is associated with a certain topic and it entirely manages the selection of the most appropriate groups to join. In our scenario, the users of the original social network are assisted by agents, and are involved only in general purpose-group, while the formation of thematic group is automatically managed in the other dimensions represented by the various twin social networks of agents. A distributed algorithm, based on a suitable compactness measure, dynamically manage the evolution of the social network organisation. We show that the algorithm, operating on several epochs, leads to form groups progressively more compact. Moreover, we verified that the use of different dimension for the multiagent system architecture allows to obtain high level of performances. It is due to the fact that the agent of each dimension can operate independently from those of the other dimensions, thus resulting in the possibility of a parallel execution of the different tasks. Experiments performed on data extracted from real social networks EPINIONS1 and CIAO2 clearly show the improvements introduced by our approach in terms of compactness of the groups. The paper is organised as follows. In Section I we describe the reference scenario, by introducing the MultiDimensional Social Network (MDS) model and the architecture of our multi-agent system. In Section II, we describe the Algorithm that we designed to realise the proposed multi dimensional social network. In Section III we discuss related literature and the novelties provided by this work. Then, in Section IV, we present the experimental campaign we have performed to validate our proposal. Finally, in Section V, we draw some conclusions and discuss our ongoing research.
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Figure 1.
The MD-social network
given topic t of common interest. A user can belong to a group, in the traditional sense, or to a t-group for performing all those activities dealing with a given topic t. In such a context, we model a Social Network S as a 4-tuple US , fS , gS , trS , where (i) US is a set of users; (ii) fS is a mapping that receives a user u ∈ US as input and returns a set of users representing the friends of u as output; (iii) gS is a mapping that requires a user u ∈ US as input and returns the set of groups the user u joined; (iv) trS is a mapping that takes two users u, v ∈ US as input and gives back the trust that u has in v. Similarly, we also define the projection of a Social Network S on a topic t, denoted as S t , a triplet US t , fS t , gS t where (i) US t is a sub-set of users belonging to S that are interested in a topic t, (ii) fS is a mapping that takes a user uS t ∈ US t as input and returns a set of users representing the t-friends of u as output and (iii) gS t is a mapping that receives a user u ∈ US t as input and returns the set of t-groups the user u is joined with. Moreover, we define a Multidimensional Social Network
I. The Multi-Dimension Social Network Scenario In our reference scenario, we assume that the users of the OSN already belong to some groups whose members have general interests. Moreover we assume that a set T of macrotopics of interest is associated with the social network, and introduce the concept of t-friend for each user u, i.e. a user that is friend of u in a context of activities related to the topic t ∈ T . Analogously, we also introduce the concept of t-group, i.e., a group whose members participate to the activities involving the topic t. Therefore, two users can be friends in a strict sense, or they can be only t-friends for each 1 www.epinions.com 2 www.ciao.com
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– a schema is depicted into Figure 1 – as a tuple MDS = S , PS , where S is a social network and PS = S t is the set of the projections of S on all the topics t ∈ T . Please note that the set of the u’s friends in the social network S , denoted by means of the mapping fS (u) can differ by the analogous set fS t (u). In other words, a user v can be a friend of u in S while he/she is not a friend of u in S t , and vice-versa. We assume that a software agent atu is associated with each user of the social network S t , and that atu , in order to perform its tasks, has the opportunity to interact with each other agent in S t by sending and receiving messages. For this purpose, agents can exploit a Communication Layer that allows an agent x to send a message to another agent y by using the name of the receiver agent in the apposite message field. We assume that a Directory Facilitator agent (DF) (see Figure 1) is associated with the OSN in order to provide a “Yellow Pages” service to all the agents. In the above context, each single user u of MDS is characterised by the following properties: t • Level of interest. We denote as Iu a real value, ranging in [0..1], to represent the interest level of the user u about the topic t. As we will discuss below, the values of this mapping are calculated on the basis of the actual behaviour of u in the t-projection of the social network. • Potentials behaviours. We categorise all the possible user behaviour in the specific t-projection of the social network – e.g. “publishing more than 2 posts per hour”, or “publishing posts longer than 250 characters”– as a set Bt = {bt1 , bt2 , . . . , btn } of boolean variables. Consequently, the overall behaviours of the user u with respect to the set B will be represented by a set Btu = {btu,1 , btu,2 , . . . , btu,n }. For instance, assuming that a user may (i) publish more than 2 posts per hour in the topic t and/or may (ii) publish posts longer than 250 characters, then we obtain the set Bt = {bt1 , bt2 }, where bt1 and bt2 respectively represent the two aforementioned properties. Consequently, the set Btu = {true, f alse} characterises a user u that publishes in the topic t more than 2 posts per hour and that does not publish frequently posts longer than 250 characters. By grouping the two information above, i.e. i) potential user behaviours and ii) level of interest, a profile ptu := Iut , Btu can be associated to each user u, such that the agent atu can manage it autonomously as follows: t t • The value of the interest Iu is updated by agent au whenever the user u have performed some activities (for instance, u publishes a post, comments an already published post, etc.) related with the topic t, as follows: Iut = α · Iut + (1 − α) · δ
•
for the new action performed by u, and the weights α and δ are two real values (belonging to [0, 1]). δ represents the increment that u desires to assign to his/her interest in t, consequently to his/her action, and α is the relevance that u wants to assign to the past values of the interest value with respect to the new contribution. Formula 1 is exploited in many multiagent system approaches resulting effective in many practical situations if the two weights α and δ are appropriately set [5], [29], [30]. Each way the user u performs an action in the social network S referred to the topic t (e.g. publishing a post, a comment, etc.) its agent atu analyses the action and, consequently, updates the set Btu by setting the appropriate boolean values for all the variables associated with the performed action.
The first time a t-group is formed in the t-projection of MDS , an agent atg is created, associated with g and registered into the DF. Moreover, a real value Igt , belonging to [0, 1], is defined to represent the relevance that the users affiliated to the group g assign to the topic t. A property Btg is defined in a similar way to the property Btu , in order to describe the behaviour of all the users affiliated to the group g. As a consequence, the agent atg manages a profile pg := Igt , Btg for the group g by executing the following tasks: • •
It asks to the agent atu , associated with the user u that created the group, to specify the values Igt . Besides, each way an action involving the group t is performed by at least the 50% of the users belonging to the t-projection of MDS , then the agent ag , informed by the corresponding agents, analyses the action and provide to set the appropriate boolean values stored in Btg .
A. Dissimilarity t between the profiles of We define the t-dissimilarity du,v a couple of users u and v by considering the weighted mean of the two contributions cI and cB , associated with the properties I and B, respectively. More in detail, each contribution measures the difference between the values of the associated property in ptu and in ptv : •
cI is the difference (computed as the absolute value) of the interests values of u and v in the topic t, that is: ctI = |Iu (t) − Iv (t)|
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where the new interest value is computed by means of (i) the previous interest value and (ii) a contribution
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cB is calculated as the average of all the differences occurring between the boolean variables contained in Bu and Bv , respectively, where this difference is 0 (resp. 1) if the two corresponding variables are equal (resp., different). t The dissimilarity duv is then computed as:
wI · ctI + wB · ctB wI + w B where wI and wB are real values ranging in [0..1]; they weight the relevance given to the interest and to the behaviour, respectively. t The dissimilarity dug between a user u and a group g is t computed similarly to duv , by substituting the user v with the group g.
acquisition. Let also M be the number of the group agents that at each epoch has to be contacted by atu .
t duv =
A. The user agent task In this task, atu behaves as follows: t • In the DF repository, au randomly chooses m groups not t present in the set G . Let Y be the set of these selected groups, and let Z = Gt Y the set containing all the groups belonging to Gt or to Y. t • For each group g ∈ Y and for each group g ∈ G , such that the date of acquisition of g verifies the condition datetg > ψ, where ψ is a fixed threshold, the agent atu sends a message to the group agent atg (whose name has been provided by the DF) requiring the profile ptg associated with the group. t t • For each received pg , then au provides to calculate the compactness measure γu,g between the t-profile of the user u and that of the group g. • Now, let τ a real value belonging to [0..1] and representing a threshold for the compactness, so that each group g ∈ Z has been evaluated as a good candidate to join with if γu,g > τ. All the good candidates are inserted by au in the set PAS S . Note that if there exist more than J groups that satisfy the condition to be inserted in PAS S , than the J groups with the highest values of compactness are selected. For each selected group g ∈ PAS S , when g Gt , the agent atu sends a join request to the agent atg , that also contains the profile ptu associated with u. Otherwise, for each group g ∈ Gt , when g PAS S , the agent atu deletes u from g.
B. Compactness We define the compactness between a user u and a user t , by means of the previously v in the topic t, denoted by γu,v t and trust trS (u, v): defined indexes dissimilarity du,v t t = WDu · 1 − du,v + (1 − WDu ) · trS (u, v) γu,v t is not a symmetric meaTherefore, the compactness γu,v t t , being the value of the sure, because, in general, γu,v γv,u trust trS (u, v) generally different from its reciprocal trS (v, u). t depends Moreover, the computation of the compactness γu,v on how much importance the user u gives to the dissimilarity with v, with respect to the trust he/she has in v. The level of importance of the dissimilarity is represented by the real coefficient WDu , belonging to [0..1]. t Similarly, the compactness γu,g between u and g can be computed as the analogous weighted mean: t t = WDu · 1 − du,g γu,g + (1 − WDu ) · trS (u, g)
The asymmetric nature of the compactness measure allow t that a group g, us to define also the compactness γg,u considered in the whole, perceives versus a user u. In particular, such a measure is computed as:
B. The group agent task Let K be the set of the k users affiliated with the group g in the t-th projection of the social network S , where k ≤ nK , being K the maximum number of members allowed by the group administrator. Supposing that atg records into an internal cache the profiles of the users u ∈ K received in the past by the associated user agents, and also stores with each profile ptu the date datetu of its acquisition. Each time atg receives a join request by a user agent ar , that also contains the profile pr associated with r, it performs the following tasks: • For each user u ∈ K, such that the date of acquisition of u verifies the condition datetu > η, where η is a fixed threshold, the agent atg sends a message to the user agent atu (whose name has been provided by the DF), requiring the profile ptu associated with the user. • After the reception of the responses from the contacted user agents, atg computes the compactness measure γg,u between the profile of each user u ∈ K {r} and that of the group g. • Now, let π a real value belonging to [0..1] and representing a threshold for the compactness, so that each user u has been evaluated as a good candidate to join
t t γg,u = WDg · (1 − dg,u ) + (1 − WDg ) · trS (g, u) t is equal for symmetry where each dissimilarity value dg,u t to the already defined dissimilarity du,g , while trS (g, u) is calculated as the mean of all the values trS (v, u) for each user v that is affiliated to g. Finally, the coefficient WDg is associated with the given group g.
II. The Multi Dimensional Users to Groups (MD-U2G) Algorithm This Section describes our algorithm that, for each projection of the social network S on a topic t, supports the formation of the best groups for that topic. Let Gt = fSt (u) be the set of the nt groups with which the user u is already affiliated in the dimension t, with nt ≤ J, where J is the maximum number of groups with which a user can be affiliated. Each time a task is executed by an agent, this time is called epoch and let T be the (constant) period between two consecutive epochs. Supposing that atu stores into an internal cache the profile ptg of the group g ∈ Gt and associates with each profile ptg the date dgt of its
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with if γg,u > π. Then, the agent atg stores in a set PAS S those users u ∈ K {r} such that γg,u > π (if there exist more than K users satisfying this condition, the K users having the highest values of compactness are chosen). If r ∈ PAS S , atg accepts its request to join with the group. Moreover, for any user u ∈ K, with u PAS S , atg deletes u from the group.
The concept of multidimensional social network has also been used to model users behavior in their shared activities [12]. In particular, they propose to extract different kinds of relationships to be grouped into layers which become the basic components of the multidimensional social network (MSN). Layers are built on the basis of social and semantic relationships. They also proposed some strength measures for each layer. Nevertheless, this method has been applied by the authors to build a social recommender system with the aim of supporting the creation of suitable relations (by means of suggestions) between users in a multimedia sharing system. In [13] a multidimensional model with three dimensions – relations, time window, group – is proposed. The authors show that the three dimensions have a common set of nodes, i.e. human beings, and relation layers reflect various relationship types inferred from different user activities monitored in the computer systems. They map also the time dimension as the temporal variability of the social network, extracting social groups by means of clustering methods, i.e. they group people close to each other. A “view” is defined as a small component of the multidimensional OSN in other words a small social sub-network which is obtained as the intersection of all dimensions, therefore they can describe the state of a social group in a time period. Although the approach is very interesting, they do not deal with group construction based on topic selection. Some authors consider not only the multiple actions which two users can jointly perform but also the multiple type of relations that can bind them in a multidimensional social network. As an example, [7] describes a supervised ranking approach to identifying relationships in a network consisting of the employees of a company with the goal of discovering subordinate-manager relationships. In [37], a co-authorship network in Computer Science is analysed; the authors provide a probabilistic model to discover the roles of authors as well as the advisor-advisee relationships. In the approaches cited above, the meaning of a social relationships can be used to predict new social links but it is not exploited to recommend groups to users.
III. Related Work OSNs are excellent platforms to let the users exchange information, collaborate and take advantage of innovative applications. Applications integrated into OSNs are particularly focused on the interests and preferences of the users’ behavior [2], [10], and the personalisation of the services provided to the members of social internetworking systems [6] are becoming central. As a consequence, a lot of data is collected into OSNs platforms, and is eventually used in order to provide suggestions about media contents [32], groups to join with [28], advertising goods matching with their profiles [22], improving homogeneity of groups [15], [16], [20], improving spam detection [11], [24], [33]. Even more, it’s a common thing that OSNs cause a change in consumer behavior and have an impact on traditional industries of content, media, and communications [25]. For these reasons, many researchers have been studying the structure and evolution of OSNs as complex networks [34], also by means of software simulations [17]–[19]. In particular, the structure of OSNs has been analysed [21] at the friendship level, confirming some features, such as the scale-free [4] properties, for which avoiding the negative effects due to the spreading of spam or malware [9], [38] within viral campaigns becomes necessary. Therefore, as discussed in the Introduction, assisting users when forming thematic groups [3], [14] can improve efficiency and effectiveness when performing a lot of tasks listed above. The authors of [14] present a broader discussion on the main characteristics of Web 2.0 [23], especially OSNs groups, how they operate in the Web 2.0 environment, i.e. the way members communicate and collaborate also by means of wikis and blogs [1]. In particular, they highlight the centrality of groups as they are interleaved in a triad relational model “Technology-People-Community” of social/work life in the Web 2.0. They finally discuss why and how the group dynamics are based on trust, support, and sharing, and that this phenomenon is independent of the nature of the communication/interaction channel (physical or virtual). In [28] the formation of on-line groups is formulated as a problem of matching the individual user profiles with group profiles. For this aim an algorithm to match OSNs users with groups is presented, together with a multi-agent system by which the computation is spread on all the user devices. Simulations on real OSNs data show both a good effectiveness and a promising efficiency of the approach.
IV. Experiments This section describe some experiments we have performed to evaluate both the effectiveness and the efficiency of the MD-U2G matching algorithm. To this purpose, we have implemented an OSN simulator, called MD-U2G-Sim, written in JAVA, capable of simulating our algorithm on a given OSN. A. Effectiveness As a measure of the internal compactness of a group, we have used the notion of average compactness, an extension of the average dissimilarity commonly exploited in Clustering Analysis [26]; it is defined as the average of the
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were randomly distributed in these groups. As we can see in Table I, we have set τ and π to a value of 0.29 because this value produces the best results in the simulation. Moreover, we set K MAX = 250, WS u = 0.5 and WS g = 0.5 for each user u and group g; in this way we assume that all the users and all the groups give the same importance to similarity and trust. The profile pu of a user u has been generated as follows: t • each values Iu is the percentage of reviews in the topic t provided by u; t • Bu contains two boolean variables, representing the user’s attitude to: (i) give to the products an average ranking higher than 3; (ii) obtain a helpfulness of their reviews higher than 3; We have repeated the experiment above on the EPINIONS dataset. In this case, we have assumed the existence of 100 groups, and we have used WS = 0.5. Figures 2 and 3 shows the results of the simulations in terms of MAC for CIAO and EPINIONS dataset, respectively. We highlight that MD-U2G algorithm improves the initial MAC of the OSN for the CIAO dataset of about 25%, while by using the EPINIONS dataset the difference of the final improvement is almost equal to 20 %.
Table I Values of the parameters used in the U2G-Sim simulator for CIAO and EPINIONS dataset.
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compactness values between each pair of objects in a cluster. In our scenario, a group g is the equivalent of a cluster of users, and the average compactness of g, denoted as ACg is computed as x,y∈g,xy γ x→y /|g|. In order to measure the global compactness of the groups of the social network, we compute the mean MAC of all the ACg of all the groups determined by our MD-U2G algorithm. We evaluate the performances of our algorithm on real data extracted from the well-known OSNs EPINIONS and CIAO. The two datasets have been crawled in the context of the research described in [35]3 EPINIONS and CIAO are product review sites providing a trust network among users. Such sites provide a sensible platform to study trust in an online world. In both these datasets we record, for each user, her/his profile, her/his ratings and her/his trust relations. For each rating, we have the product name and its topic, the rating score and the helpfulness of this rating. The EPINIONS dataset consists of 22, 166 users, while CIAO contains 12, 375 users, but it has more close-knit trust relationships. In a first experiment, we apply our matching algorithm to the CIAO dataset, assuming a number of 75 groups; users
B. Efficiency We conclude the discussion of experimental results by illustrating on the computational performance of our approach. All the experiments presented in this paper were executed on a 2Ghz I7 Intel Processor equipped with 8GB RAM. We have executed the algorithm assigning to the agents of each t-dimension a different thread, thus simulating a parallel computation. The time required for all the experiments presented in this section roughly amounts to about 2 seconds per epoch. Moreover, experiments show that 3-4 epochs are usually needed for producing the optimal solution
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dataset are publicly available at http://www.public. asu.edu/˜jtang20/datasetcode/truststudy.htm.
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on the real dataset EPINIONS and CIAO and, therefore, we can conclude that our approach seems efficient on real life OSNs.
[2] S. Ardon, A. Bagchi, A. Mahanti, A. Ruhela, A. Seth, R.M. Tripathy, and S. Triukose. Spatio-temporal and events based analysis of topic popularity in twitter. In Proceedings of the twenty-second ACM International Conference on Information & Knowledge Management, CIKM ’13, pages 219–228, New York, NY, USA, 2013. ACM.
V. Discussion and Conclusion
[3] L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan. Group formation in large social networks: membership, growth, and evolution. In Proc. of the ACM SIGKDD international conference on Knowledge discovery and data mining, pages 44–54. ACM, 2006.
In this paper, we deal with the problem of forming thematic groups in an OSN, such that the members of the groups have similar interests and behavior and also show a sufficiently high mutual trust. To this end, we have conceived a multi-dimension architecture for the social network, where each dimension is associated with a different topic, and each user is assisted by a software agent operating in that dimension. We have designed a User-to-Group matching algorithm, that allows the set of software agents associated with OSN users to dynamically and autonomously manage the evolution of the groups, by detecting for each user the most suitable groups to join with based on a synthetic measure of internal cohesion, called compactness, integrating similarity and mutual trust. Experiments on real data showed that our matching algorithm increases the internal compactness of the groups composing the OSN. Our algorithm is easy to implement and it quickly converges and these two features are particularly relevant in a real OSN (consisting of millions of users and thousands of groups). Moreover, the distribution of the agents on different dimensions allows to add efficiency to the system. In line with some results from Recommender System literature [39], trust and similarity can be profitably combined to yield more accurate results. Our experiments provide evidence that when the compactness measure is used, we achieve an increase of MAC of about 20-25% with respect to a merely random assignment of users to groups. Roughly speaking, the experimental results support our hypothesis that forming thematic groups considering different dimensions for the different topics lead to achieve the goal of obtaining high compactness values. We plan to extend our research in a number of directions. A first step is to study the structural features of generated groups: so, for instance, we aim to study the topic distribution associated with the a user profile, and if users tied by social links are also interested in the same topics. We aim to consider also other type of signals (like the text comments) to define more advanced similarity and trust metrics to incorporate in our definition of compactness. Finally, we target at studying the practical benefits of our approach: we plan to design advanced graphical interfaces supporting group administrators to selectively invite individuals to join groups.
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