Ranking Algorithm by Contacts Priority for Social

Ranking Algorithm by Contacts Priority for Social Communication Systems Natalia Dudarenko, Juwel Rana, and Kåre Synnes Department of Computer Science and Electrical Engineering Luleå University of Technology, SE-971 87 Luleå, Sweden {natalia.dudarenko,juwel.rana,kare.synnes}@ltu.se

Abstract. The paper presents the ranking algorithm by contacts priority with application for social communication services. This algorithm makes it possible to rank contacts in different social communication services by their priority for the user, and find a preferable communication tools for every contact in the social graph. It is proposed to determine priorities of the contacts using communication history. The ranking algorithm is based on the Markov chains theory and social strength calculation approach. The paper exploits the opportunities for measuring social strength for the contacts and also prioritizes communication tools for simplifying communication. The results are supported by an example. Keywords: Ranking algorithm, Markov chains, Social strength, Communication service, Contacts priority.

1 Introduction The social networking and communication services (Facebook, MySpace, Twitter, etc.) have a large impact on our daily life due to enormous applications and large number of users (e.g., Facebook have 400 Milion active users) [1, 6]. In general, the services form social graph among the contacts, provide online interactions among the contact individuals, facilitate content sharing mechanism, and also offer different kind of group communication tools. In a way, this forwards the traditional communication services (e.g., Email, SMS) to new directions (e.g., Google Wave) where Web technologies and mobile devices are playing a very important role [2]. However, that increased numbers of communication services (social networking and communication services) generates huge amount of contents (e.g., picture, video, presence information, etc), which add new challenges for maintaining contacts, and contents, and initiating group communication [4, 10]. Therefore, this paper addresses to the problem of social ranking of the contact individuals by providing an algorithm and discusses possible application scenarios for that. The paper proposes the ranking algorithm by contacts priority (CP-Rank algorithm) that allows classifying the contacts by their priority for the user and finding a preferable communication tools for every contact. The proposed method prioritizes the contacts based on communication history. However, aggregating communication history log is S. Balandin et al. (Eds.): NEW2AN/ruSMART 2010, LNCS 6294, pp. 38–49, 2010. © Springer-Verlag Berlin Heidelberg 2010

Ranking Algorithm by Contacts Priority for Social Communication Systems

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not addressed in the paper. The focus of the paper is to provide sophisticated algorithm for ranking social contacts and communication tools. The CP-Rank algorithm is based on Markov chain theory. The social strength calculation algorithm [20] generates the values for the transition probability matrix with respect to the CP-rank algorithm. The rest of the paper is organized as follow. Section 2 provides related work of the research problem. Section 3 describes social strength as a building block of this research. Section 4 proposes CP-Ranking followed by the algorithm. Section 5 includes an example. Section 6 provides evaluation. Finally, Section 7 contains the discussion and future work. The conclusion is in Section 8.

2 Related Work Reto et al., do a survey for group communication and collaboration in mobile settings where they come to the result that contact ranking is an important problem [16]. They apply the CONGA algorithm for detecting group or community of contacts. In our work, the contacts are ranked in more generalized manner to form groups based on social strength rather detecting communities from the communication log. Ranking algorithms for example Pagerank or hypertext induced topic search [17, 18] are specialized for Web link searching. In the paper, we also develop an algorithm called CP-ranking for ranking the contacts and their communication tools in the online social networks. HITS or Pagerank algorithms consider in-links and out-links to form the utility functions used in the algorithm, while in our proposed algorithm we considered number of interactions using different communication tools in online social networking and communication services to measure social strength. Bao et al., propose two algorithms SocialSimRank and SocialPageRank for capturing the popularity of Web pages [7]. The algorithms take feedback of the user in form of social annotations. In our work, we consider frequency of communication with particular tools to identifying the popularity of particular communication tools. We also consider different threshold values based on popularity of tools to get good result. Different approaches for dealing with social strength are described in [5, 9, 16, 20]. In those papers different kind of quantitative methods are applied to capture social strength. The Markov chain theory is applied for finalizing social strength. As opposed to Zhang X. et al., who take into account the importance of the in-links and out-links and the different link weights, we use the social strength to form transition probability matrix [19].

3 Social Strength Calculation This section discusses how social strength can be calculated from different communication and social networking services. In the paper, social strength defines as numerical values to rank the contacts of a particular user’s social graph. For example, if a user has a social graph of three contacts (A, B and C) then their corresponding social strengths can be represented as 0.30, 0.50 and 0.20, which exposes that the user is

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N. Dudarenko, J. Rana, and K. Synnes

highly connected with the contact B. Since the algorithm was described in an earlier work [20], we limit ourselves to a short description for the self-containment of this paper. Fig. 1 shows a conceptual overview of how social data (e.g., calendar data, sensor data, etc) is collected from different communication and social networking services and processed through aggregated social graph framework. When the user interacts in social media the interactions are captured by the framework [3]. Then the framework applies social strength calculation algorithm to generate rank of contacts in the social graph. For instance, Strength measurement block in the Fig. 1 applies social strength calculation algorithm and provides strongest network of contacts.

Fig. 1. Conceptual Architecture

Communication history log is processed (see Table 1) by following the approach of [20]. According to the approach, each of the frequency parameter includes three dimensions where, implies to communication service, implies to communication tools and implies to contacts (Fig. 2). In the Table 1, the field where =1, =1, =1 means that Johan, a contact of the social graph interacts with the user through the Facebook messaging tool. The overall social strength of the user ( is the main user which forms the aggregated social graph for all of his/her contacts) is

=∑ =1

= 1,

(1)

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Fig. 2. Frequency Data Type

In the proposed CP-Ranking algorithm, the sum of strength of all values in a row of transition matrix is 1.

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N. Dudarenko, J. Rana, and K. Synnes Table 1. Frequency logs

Name

FB/Message (i=1,j=1)

FB/Photo (i=1,j=2)

FB/Comments (i=1,j=3)

Mobile-phone/Call (i=2,j=1)

In the next section the proposed CP-ranking algorithm allow to aggregate the result, rank the contacts and find the preferable service/tools for the user/contacts using social strength.

4 The Proposed CP-Rank Let us consider the communication process in any communication system as a finite Markov chain [13], where={ : =1÷ } is the states set of stochastic process with interactions between the user and his contacts, and between the contacts and communication tools of the different services; ={ : =1÷ , =1÷ ( =1÷ =1÷ ) of moving from the state to the state is a stochastic matrix, the Markov chain is a random walk on the graph defined by the interactions structure of the contacts. The probabilities set satisfies the equation [13,14]

∑

=1,

(4)

=1

that =

1

2

…

…

=

{ : = 1, } ;

(5)

= {| ( ij): =1÷ |, =1÷ } is the transition probability matrix from the state into the state ( , =1÷ ); ={ :0≤