Finding and Matching Communities in Social Networks Using Data Mining

Slah Alsaleh, Richi Nayak and Yue Xu Computer Science Discipline, Queensland University of Technology

Brisbane, Australia s.alsaleh@qut.edu.au , r.nayak@qut.edu.au and xu.yue@qut.edu.au

Abstract: The rapid growth in the number of users using social networks and the information that a social network requires about their users make the traditional matching systems insufficiently adept at matching users within social networks. This paper introduces the use of clustering to form communities of users and, then, uses these communities to generate matches. Forming communities within a social network helps to reduce the number of users that the matching system needs to consider, and helps to overcome other problems from which social networks suffer, such as the absence of user activities’ information about a new user. The proposed system has been evaluated on a dataset obtained from an online dating website. Empirical analysis shows that accuracy of the matching process is increased using the community information.

Keywords: Social network, online communities, and recommender system

1. INTRODUCTION

Recommending people within social networks is more complicated and sensitive when compared with recommending products (Tobias, 2007). Additionally, the increasing number of social network members, along with their information and communications, builds up the computational complexity. Examples of social networks include: Facebook, MySpace, E-Harmony, Pathable (Jiang & Carroll, 2009) and RSVP. In Facebook, for example, there are more than 500 million active users, with 50% of the active users logging on in any given day (www.facebook.com, 2010). Another example is RSVP which is considered to be the largest dating network in Australia, with more than 2 million members, and an average of 1,000 new members every day (http://www.rsvp.com.au/, 2010). In such social networks, a lot of personal information is kept about the users. This information can be divided into user information and user activities. The user information is collected by asking the user to answer a series of questions which represent his/her characteristics and preferences. Many social networks also record the user’s activities on the network (such as sending messages, watching profiles, etc.). Different techniques are employed to match users in social networks, including content-based, collaborative and rule-based techniques. Such techniques contain two main phases: the data collection phase, and the learning phase (Mobasher, 2007). In the data collection phase, information about the user is collected from their profiles and online activities, whereas, in the learning phase, the collected data are used to identify the relevant users

who could be a potential match. Since the existing techniques use pairwise user matching, the process is a further increase in the complexity of the recommender system. Even though social networks are increasingly used by, and attract attention from, both academic and industrial researchers, many aspects still need to be explored and developed. One major issue is the high computational complexity involved in finding the similarities between users, which is then used to identify potential matches. Another major issue that needs to be investigated is the low accuracy rate of user matching. Clustering is a data mining technique that can directly solve the first problem, and can indirectly solve the second problem. Clustering divide the users into communities where similar users are grouped together. This technique improves the matching process in social networks by reducing both the data size and the complexity of the matching process. Moreover, a clustering technique assists the matching process in social networks by overcoming a number of existing problems, such as cold start and sparsity. For example, the cold start problem occurs when a new user joins a social network and the system has not gathered enough information about the user for a match to occur. However, by assigning this user to an existing community, which already has been matched with its appropriate communities, allows the use to receive one or more matches instantly. Thus, in this paper, the clustering technique has been used for grouping together users who have similar characteristics. This process of clustering the users in social networks can be divided into two phases. The first phase is the data pre-processing phase, where the relevant data are collected, cleaned and transformed into vectors. The second phase is the data mining process, where vectors are clustered and the communities are identified. This paper, therefore, proposes the idea of matching users in social networks through their communities; this is achieved by considering both their profile data as well as their links information. The social network chosen for the study was a dating network or social matching system in which opposite gender users are recommended to each other. . The user profile data (information used by members to describe themselves) is used to group the male and female users, separately, into homogeneous communities. Next, the male communities are matched with the female communities using the links (or connections) in the network, and a user community is recommended to another opposite gender user community. The proposed method is evaluated using a dataset obtained from an online dating website. The empirical analysis demonstrated that the proposed method improves the accuracy of the

2011 International Conference on Advances in Social Networks Analysis and Mining

978-0-7695-4375-8/11 $26.00 © 2011 IEEE

DOI 10.1109/ASONAM.2011.90

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recommendations from 16% to 50.32%, when compared to the other techniques, while also reducing the matching complexity by 93.95% and overcoming the cold start problem. Following the Introduction, the paper is organized into five sections. Section 2 reviews the literature on current social networks and online dating systems. Section 3 details the proposed system and its implementation process. Section 4 discusses the results, and Section 5 concludes the paper.

2. RELATED WORK

Three learning approaches are widely used in social networks to match people; they are: the rules-based approach, the contents-based approach, and the collaborative approach (Brusilovsky, Kobsa, & Nejdl, 2007). Rule-based systems recommend users by using manually or automatically generated decision rules. In the manually generated systems, administrators are allowed to specify the rules based on the users’ characteristics. These rules affect the delivered recommendation to the user. The user profile is built upon the user’s answer to a range of questions, and by obtaining some information about the users’ interaction with the system. The main concern in relation to rule-based systems is that they involve, in the main, information provided by the users; this information is both subjective and static (Pazzani, 1999). Content-based filtering systems are based on what the users have specified in their profiles. Users are represented by sets of features which enable them to be matched with a similar user profile. To achieve this outcome, user profiles are represented as

weighted term vectors using weighting schemes, such as Term Frequency–Inverse Document Frequency (TF-IDF), BM25, etc. The main concern with content-based filtering systems is that they depend on what users have specified in their profiles; as a result, this information limits the recommendation (Pazzani & Billsus, 2007). Collaborative filtering systems have advantages over the previous two approaches. In collaborative filtering systems, the user’s preferences are considered, along with the preferences of similar users (nearest neighbours). However, the collaborative filtering approach still suffers from the sparsity problem. The sparsity problem occurs when users are inactive and, therefore, fail to provide accessible preferences. Additionally, when users are inactive they are, consequently, excluded from the recommendations. The systems also suffer from a lack of scalability. Another problem arises from the explosion in the number of users, and that it may take a long time to process the recommendations within the social networks. Currently, a range of social networks exist which use different profiling methods, and which match their users in different ways. Table 1 summarizes four social networks, along with their profiling methods and matching techniques; the first two are friendship types, while the other two are dating types. Although, these social networks contain user activity data, it appears that such information has not been used in the matching process. Further, the existing social matching systems use pairwise algorithms to find the similarity between the users, which adds to the very high computation complexity.

3. THE PROPOSED METHOD

A. Preliminaries An online dating system is a type of social network that aims to introduce people to their potential partners. These systems contain two subsets of data. The first subset relates to the user’s profile, which includes information about the user (such as their personality traits and interests), as well as information about their preferred partner. The second subset relates to data about the user’s activities, for example, viewing other profiles and the messages sent to other users.

Let U be the set of users in a social network, U = {u1, u2 ,… un}, where ui is either a male or a female user. Each user has a set of attributes attr(ui) = {o1, o2 ,… ox ,p1, p2 ,… pg}, where oi is an attribute that explicitly describes the user (such as age, height, education, etc.), and pi is an attribute that describes their preferred partner. The users select one value (or none) for each oi attribute; however, they can select a null, single or multiple values for each pi attribute. A user ui can perform a number of activities on the network,such as viewing another user’s profile V(u1, u2), where u1 views the profile of u2. This activity shows that u1 may be interested in contacting u2. Other user activities include communicating via short pre-defined messages (called a Link, as these messages are used to form the network), and long free-text messages (email). A Link can be defined as L(u1, u2, lt, lr), where u1 creates a link lt that contains a pre-defined message to u2 and, thus, confirms their further interest in u2. Further, users can select one of a

Table 1: Examples of social networks

Name Profiles Matching Techniques

Facebook User profile (information that users provided when they register)

Demographic: based on the user’s profile (school, university, city, interests, etc.) and emails

MySpace User profile (information that users provided when they register)

Content-based: based on the user’s profile (school, university, city, interests, etc.) and emails

RSVP User profile and user activities (information provided by the users and information traced from the user interacting with the website)

Content-based: based on the user’s profile (users’ variables)

eHarmony User profile and user activities (twenty-nine different personality variables and information traced from the user’s interaction with the website)

Content-based: based on the personality variables

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variety of lt that represents the users’ feelings about another user. Then, u2 can reply to u1 by sending one of several pre-defined messages lr which may be either a positive or a negative reply. However, lr can be null, indicating that the target user has not responded to the sender’s request. Furthermore, u1 may also send emails E(u1, u2) to another user u2. In this paper, both the user profiles and the user activities are employed to improve the recommendations in the proposed system. User profiles are used to cluster similar users into communities according to their profiles’ similarities. One major advantage of clustering users into communities is the huge reduction in computational complexity. Clustering also enables the communication between the communities to be identified, which is then used in the matching process. Additionally, once users are clustered into communities; the data obtained from the users’ activities are used to match the users’ communities. These clustering and matching phases are discussed in more detail in the following sections.

B. Forming comminities in social networks As the number of users in dating social networks is growing dramatically (for example, there are over 14 million EHarmony users). Matching the potentially interested users becomes a critical issue for two reasons. The first reason relates to the complexity caused by the large social network member base. The second reason involves the number of users’ attributes that need to be considered in the matching process. However, the data can be reduced by employing pre-processing and filtering techniques, and identifying the relevant attributes that users tend to rely on to make their decisions. The pre-processing techniques used in the proposed system include filtering, integrating and transforming the data to be used in the matching phase. The first step in the pre-processing technique is to reduce the data, by considering the active users only. Many criteria can be used to exclude inactive users, namely, those who have not viewed any profiles; users who have not sent any messages; and users who have not logged on for more than three months. The second step in the pre-processing technique involves the data integration and transformation. The user information comes in four types of data: binary data (e.g. seeking a long relationship, seeking a short relationship), nominal data (e.g. industry, body type), with possible multiple values, ordinal data (e.g. number of children), or continuous data (e.g. age and height). To simplify the clustering process, each attribute is transformed to have a vector space representation, where each attribute-value represents a value in vector. The transformation process was straightforward for the binary and nominal data; however, the numerical data needed to be transformed as nominal data, and then into vector space representation. For example, the age attribute was binned into 15 groups and, then, the user age was assigned with the group within which his/her ages fell. A long-narrow representation is used so that all the values of a variable become a binary variable. Thus, the values that the users have shown in their profile become 1, while the remaining binary

variables yield a value of 0. Using the binary representation overcomes the multi-value problem that some attributes have. Once the data have been integrated and transformed, they are then divided into two groups: male users denoted as Um = {u1, u2,… um}, and female users denoted as Uf = {u1, u2 ,… uf} where U = Um �Uf and Um �Uf = �. This process prepares the data for clustering in the next phase. Firstly, the male and female users are clustered separately, resulting in a set of male communities and a set of female communities. Secondly, the male communities are matched with the female communities using the communities’ centroid. Then, the users are recommended to each other (Figure 1).

Figure 1: The proposed system design

The male users are clustered according to their own attributes {o1, o2 ,… ox}, while the female users are clustered according to their preferred partners’ attributes {p1, p2 ,… pg}. Such clustering ensures high accuracy matching. However, using both the users’ own and preferred attributes in the clustering process creates difficulties in finding a male community that is similar to a female community. For example, while two users may have similar own attributes, they may be very different in what they are looking for in their partners. A decision was made to use the males’ own attributes and the attributes of their preferred partner (female), as Morgan et al (2010) found that females are more mindful, and put greater effort, into completing online dating questionnaires in comparison to male users. This is especially so when completing the requirements about their preference partners. The analysis of the underlying social network’s statistics also confirmed that, even if the number of male users is greater than the number of female users, the female users attempt to provide more information about their preferred partners. Therefore, the male own attributes and preferred attributes for females will be used.

C. Communities to reduce the computational complexity As discussed previously, the matching process in social networks may suffer from a high computational complexity (Comp) due to the presence of a large number of users. The Comp in the traditional social matching systems is associated with the number of users n including m number of male users and f number of female users as a similarity measurement needs to be conducted between every pair of female-male members in the social network. Thus, the Comp can be presented as follows:

Comp = O (f × m)

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However, by forming communities using the clustering technique, the proposed system significantly reduces the Comp. The system can then measure the similarity between a user within one community set, and all users who belong to another (matched) community set. Let the m number of male users are divided into x number of clusters and the f number of female users are grouped into y number of clusters. The reduced Compcan be expressed as:

Comp = O( ���� �

)

As mentioned earlier, the reduction of Comp results from limiting the similarity measurement to the assigned community, instead of all users. The Comp is affected by the number of communities, the average number of users per community and the number of female communities assigned for each male community.

D. Matching communities using users’ communcations The next step in the proposed system uses the communities’ matching to recommend potential partners. Therefore, let Cm be the clustering solution grouping the male users containing ccommunity, Cm = {Cm1, Cm2 ,… Cmc}. Let am1 be the centroid vector of community Cm1 represented as {am11, am12 ,… am1x}. Let Cf be the clustering solution grouping the female users containing d community, Cf = {Cf1, Cf2 ,… Cfd}. Let af1 be the centroid vector of community Cf1 represented as {af11, af12 ,… af1g}.

Figure 2: Communications between communities in social networks

The process of matching all communities is based on the communications between the communities’ members, and utilizes the user activity data. As shown in figure 2, the large circles represent the communities, little circles represent the male users, the squares represent the female users, and the lines represent the communication activities between the users. As mentioned previously, social networks contain user activity data; this data can be used to improve the recommendation. The users interact with each other by sending messages L(u1, u2, lt, lr), emails E(u1, u2), and by viewing each other’s profiles V(u1, u2). These activities can be used to match the users’ communities. In the proposed system, the successful links interaction, L(u1, u2, lt, lr), between a male user’s community and all the female users’ communities are assessed to determine the pair of communities that has the most interactions between them. These communities

are then recommended to each other. A successful link is defined as a link that receives a positive reply lr, thus implying that the opposite party is also interested in this relationship. When the matching process is completed, the recommendation phase takes place. The recommendation phase (Alsaleh et al, 2011) presents the recommendations to the users, in order of the users’ compatibility scores. The compatibility scores are calculated between the members of the pair of matched (or compatible) communities, according to the members’ profiles and preference similarities. The user profile vector of the male user um Cmc is compared with the preference vector of all female users uf Cfd.

4. EXPERIMENTS AND DISCUSSTIONS

The proposed system has been tested on a dataset obtained from a real online dating website containing more than two million users. The data subset shows that the average active users initiates at least one communication channel over a period of three months. For this reason, the proposed system targets only these users.

A. Dataset The user data in this dataset can be categorized into three groups: information about users; information about their preferred partners; and their activities on the website. As mentioned previously, the user profile information was used to cluster the male users, whereas the preferred partners’ information was used to cluster the female users. There were about 42,724 male users and 33,795 female users in this subset data. Since the profile attributes are used for male users, each attributes can have only one answer. Therefore the vector length is 17. Since the preference attributes are used for female users, each attributes can have multiple answers. The vector length is 130. Still the average number of values for the vector per female is less than the average number of values for the vector per male user. This is due to the reason that the male users have to answer all the questions about themselves, while the female users are allowed to answer any or all preferred partner questions. The user activities data are used to match the communities. Successful communication links between the members are then used to match the male and female communities. The analysis shows that there were 61354 links created between users in the network. Out of them, there were 9814 (or 16%) successful links (i.e., those communication links that received a positive reply).This gives us a baseline of 16% success rate in the current network without using a recommender. The success rate is used to evaluate the accuracy of matching. The success rate measures the probability of recommendation being successful as indicated by the (positive) Link returned by the recommended partner.

B. Discussion The clustering process resulted in 100 male communities and 100 female communities. We found from empirical study that 100 communities is the most appropriate value where the majority of the communities are homogeneous and have a similar average membership. Due to its simplicity and speed in running large

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datasets, as found in social networks, a K-means clustering algorithm was used to cluster both male and female datasets using the Euclidean distance function. Next, the communities were matched using the successful communications (Links) between them. Consequently, the matching success rate was based on the successful communications among the communities (viz., 0.50), and on their similarities (viz., 0.31). The success rate measures the probability of recommendation being successful as indicated by the (positive) Link returned by the recommended partner These results clearly demonstrate that matching communities, in terms of their communications, is more successful than is matching communities in terms of their similarities. Indeed, matching communities’ similarities via their centroid vectors is shown to be an inaccurate measuring technique. It appears, however, that this result is influenced by the sparsity of social network data. Hence, the proposed system utilizes the communications activities to compare and match the communities with each other. In addition to the improvement of the success rate, the proposed system also helps to reduce the computational complexity of matching the users of social networks. Thus, rather than comparing every male user with all female users, the proposed system limited the comparisons to the users within an assigned community. As a result, only 92,466,720 comparing processes were undertaken between users, instead of the baseline system, which would have required 1,443,519,630 comparing processes between all members of the network pair-wise. As a consequence, the proposed system computational complexity was reduced by 93.95%.

5. CONCLUSION AND FUTURE WORK

This paper highlights two problems that occur when social networks match users to users, namely, computational complexity and matching accuracy. To resolve these issues, a social matching system based on clustering is proposed. The system uses a clustering technique to create sets of communities based on users’ information, and then similar communities are matched based on users’ activities. The improved matching process can successfully find better matched users as shown by empirical analysis. The user to users recommendations are shown to be of a high quality and are generated with reduced computational complexity. Any future research should explore additional data mining techniques. For example, association rules could be assessed and developed to enhance the matching of users in an online social network.

AcknowledgementThe content presented in this paper is part of an ongoing cooperative study between Queensland University of Technology (QUT) and an Industry Partner, with sponsorship

from the Cooperative Research Centre for Smart Services (CRC-SS). The authors wish to acknowledge CRC-SS for partly funding this work and the Industry Partner for providing data for the analysis. The views presented in this paper are those of the authors and are not necessarily the views of the organizations mentioned

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