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Semantic and Behavioral or Structural Analysis, Which suits best for Social Network Analysis? Hamza Loucif Bordj Bou Arreridj University 34030 El Anasser, Algeria
[email protected]
Samir Akrouf Bordj Bou Arreridj University 34030 El Anasser, Algeria
[email protected]
Boubetra Abdelhak Bordj Bou Arreridj University 34030 El Anasser, Algeria
[email protected]
interaction have spread very quickly, thanks to their easy use. These tools encourage the collaboration and the creation of social networks especially with the rise of internet enabled devices such as smartphones and other more recent hardware innovations such as internet tablet that has made social networking just a finger tap away. There are many forms of social media, but the most common are social networks (Facebook, LinkedIn, Google+), social sharing (YouTube, dailymotion, Flickr), collaborative authoring (wikipedia, Ékopédia), blogs and podcasts (LiveJournal, Twitter), online markets and production (eBay, Amazon, Hi5), mobile based services (location sharing and games) that have become increasingly popular. To illustrate this popularity, it is enough to refer to social networks’ usage statistics. According to [26] during the past year (2012), there was over 465 million Twitter accounts, 75% of companies use Twitter as a marketing channel. On a busy day, twitter sees about 175 million tweets. Facebook has reached 100 billion friendships and 89% of agencies said they would use Facebook to advertise for their clients and there are 2.7 billion likes/comments per day. One hour of video is uploaded to YouTube every second. 69% of online adults use social networking sites. Fully 40% of cell phone owners use a social networking site on their phone, and 28% do so on a typical day [24]. With the powerful emergence of social networks, several actors coming from a wide range of domains have recognized the power of these networks that can be effective in communication, recruitment, politics, marketing, brand awareness, customer relations and entertainment to name just a few. Everyone is trying to use media such as Facebook and Twitter to convey his messages or campaigns, remembering for example the crucial role that social networks played through the Barack Obama's election 2012. Businesses is one of the domains that try to exploit the advantages of social networks and the enormous potential offered by social media in their advertising and marketing campaigns focusing on the use of online social relations among people who share business interests and activities. Social media can be very cost effective as it generally involves minimal or no costs at all and can be a progressive
Abstract With the rapid emergence and the massive proliferation of social applications, the web has become a major communication element of our civilization attracting more and more people as social media have greatly increased the participation, interaction and sharing between people. In recent years, several actors coming from business, politics and many other areas, recognize the power of the social networks due to the fact that they are an information concentrator, and a huge customer base, where everyone is trying to use media such as Facebook and Twitter to convey his messages (products or services) hoping lower cost and large-scale propagation. One of the best ways to do so is to identify opinion leaders who are able to persuade and influence their community more than others to take advantage of their influence and prestige. Several research have proposed algorithms for identifying key nodes which are based on structural characteristics of graphs representing social networks and sometimes on few behavioral aspects that are explicitly insufficient to provide insights about interesting patterns of underlying networks such as the richness of profiles and the quality and the subject of postings. The best way to overcome the limitations of these classical models is to take into account the semantics of measured indicators used by these algorithms for capturing social networks in much richer structures than raw graphs. In this paper we give an experimental justification of the aforementioned limitations by the implementation of two classical models to analyze the diffusion of influence through an egocentric network (namely Shaykh Dr. Aaidh ibn Abdullah al-Qarni network) using NodeXL tool. Experiments results argues the need of a new architecture that we propose to exploit the best features of existing approaches (structural and behavioral) moving to a semantic analysis of social networks.
1. Introduction Today, there is no doubt that everyone knows or has heard of social media, these new means of publication and
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alternative to traditional marketing as it allows having a two way relationship with customers or clients as marketers can provide them with information and potential updates on their latest products or services whilst at the same time, they can provide invaluable feedback, good or bad that will help these marketers to improve their business. The most important benefit of social media in business is the marketing opportunities to increase public awareness of products and services, in particular the viral marketing strategy [4], where customers help actively marketers to promote a product design or customer service. This method is often a more cost effective type of advertising and, in some cases, more effective in absorbing new customers and making people adopt a new product because people are more affected by their friends or the people they trust [5]. According to Iyengar et al. [6], friends have a significant effect on the purchase probability of a customer for a specific product while users tend to buy some of the new products when their friends already bought that product, which means that among all social network users, some can influence their community more than others. Therefore, one of the issues to be resolved in this category of marketing is finding persons with a high network value who are known as opinion leaders in the two-step flow theory [14] or innovators in the diffusion of innovations theory [15], such that when marketed to, influences many others to purchase the product [7] [8] [9]. Thus businesses are interested in finding those influential users and encouraging them to create positive influence. Social influence is the behavioral change of a person because of the perceived relationship with other people. In Merriam-Webster’s dictionary the word influence is defined as “The power or capacity of a person or things in causing an effect in indirect or intangible ways” [10]. According to Wikipedia, social influence takes many forms and can be seen in conformity, socialization, peer pressure, obedience, leadership, persuasion, sales, and marketing. Likewise three broad varieties of social influence have been identified by Herbert Kelman [11]: (i) Compliance, which is defined as agreement among people while keeping their dissenting opinions private. (ii) Identification, in which people are being influenced by someone who is liked and respected, such as a famous celebrity. (iii) Internalization, in which people accept a belief or behaviour and agreement is made publicly and privately [5]. Morton Deutsch and Harold Gerard [12] described two psychological needs that lead humans to conform to the expectations of others; namely, the need to be right (called informational social influence, that is an influence to accept information from another as evidence about reality), and the need to be liked (called normative social influence, which is an influence to conform to the positive expectations of others). With the exponential growth of online social network services such as Twitter and Facebook, social influence can for the first time be measured over a large population. In
the literature we can find two principal categories of influence measurement problem [5]: First, how to rank nodes based on their importance of infecting more nodes if that node individually gets infected (This rank reflects the influence of a user in the network.). Second, how to construct a set of nodes (a subset of initial adopters) with a given size to maximize the number of infected nodes in a network, in other words the influence of an individual is not important by itself, but we focus on the combination of nodes (who are not necessarily the most influential ones in the network) in a set. Each of these two categories is considered from the point of view of several researchers in this domain as a problem of maximization. According to [16][17][18][19][20] The problem of influence maximization can be traced back to the research on “wordof-mouth” and “viral marketing”, motivated by the determination of potential customers for marketing purposes [5]. Word-of-mouth is kwon as an information diffusion technique which is a variant of an area of study in the social sciences called diffusion of innovations. The latter is defined as ″the process in which an innovation is communicated through certain channels over time among the members of a social system ″ [15]. An innovation can be an idea, activity or object that is seen as new. When new ideas are invented, diffused, adopted or rejected, social changes occur. Diffusion of innovations seeks to explain how, why, and at what rate new ideas and technology spread through a social network. In this way, researchers have considered social influence as a diffusion process which can be studied by using information diffusion models. Kempe et al. [8] took the first step to formally define the influence diffusion process in the well known diffusion model Linear Threshold Model (LTM) to achieve the first provable performance guarantees of influence maximization. In this paper we used the two aforementioned models for modeling the dissemination process. Experiments prove the validity of two facts that motivate the research described in this paper, first the non effectiveness of structural factors that can be used to select the elements of a best combination (subset) of initial adopters targeted for initial activation which is considered as the first and the most important step for the two models, second we demonstrate the weakness of the approach which is based on the affectation of randomly chosen weights to edges (representing different kinds of relationships) in order to fill the lack of information about the relations between actors. The rest of this paper is organized as follows: section two provides a brief presentation of standard measures (centralities) of social networks that are related to the concept of social influence in terms of social effects. In the same section we define and explain the implementation of the two models used for measuring the influence propagation in the egocentric network of a well known Muslim scholar Shaykh Dr. Aaidh ibn Abdullah al-Qarni
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which is briefly best known for his books that are aimed at Muslims and even non-Muslims alike [23]. In section three, we describe the different steps involved in the extraction of the egocentric network using the NodeXL tool. The presentation of this tool and the results of the experiments are given in this section as well. Section four provides discussion about the experimental results. Finally, conclusions will be discussed in Section five.
Who are the most influential actors for influence spread? This paper focuses on the resolution of the second question using two well-known models to model the spread of messages (information) within an egocentric network extracted from Twitter that we will describe each of which in this section. Generally, we can distinguish two classes of analysis in the literature. The first one is the structural analysis which concentrates on the structure of the network which is modeled by a graph constructed from individuals represented as nodes and edges between pairs of these nodes denoting ties or relations [5], by measuring the centrality of users defining their roles. The second one is the behavioral analysis which concentrates on the behaviors of users and their interactions. As stated earlier, before introducing the two models, we define briefly three metrics (centralities) for evaluating centrality of a user according to the structure of a network that allow us to select the elements of the initial set of adopters which is considered as a first and key step for both models. Betweens centrality: focuses on the ability of a user to serve as an intermediary in a network. A node located on a geodesic path has a strategic position in the cohesion of a network and the flow of information, especially if this path is unique. For example, a node located on the unique path connecting two sets of connected nodes has a strong control on the communication of these two groups. The More a node is intermediate, the more the network is dependent on him. Closeness Centrality: a user with higher closeness centrality is more visible in the network since this user has quicker accessibility to the entire network. Degree centrality: designate a simple count of the total number of connections linked to a user. Nieminem [22] considers as centrals, the users who have the highest degrees of the network. These users are of a great interest since they are highly visible and have a high potential to disseminate information by their high connectivity to other network elements. Eigenvector centrality: this is a measure of the importance of a node in a network. Unlike degree centrality that gives a simple count of the number of connections a vertex has, eigenvector centrality acknowledges that not all connections are equal so that connecting to some vertices has more benefit than connecting to others [1].
2. Social Network Metrics With the advent of online social networks, which has been one of the most exciting events in this decade, many popular online social networks such as Twitter, LinkedIn, and Facebook have become increasingly popular [3]. Due to this fact, the worldwide web actually is becoming more and more a principal part of modern society as shown by the widespread availability of the internet, the time people spend online, the highly interactive nature of Web 2.0 sites and its effects on our daily actions. While the earlier social networks were based on face-to-face interactions, the online social networks make use of information and communication technologies tools to make interactions less difficult. Moreover, the new suitable hardware and software raise communications and allow people to inexpensively and reliably share information anytime and anywhere. Many such online social networks are extremely rich in content, and they typically contain a tremendous amount of content (text, images and other multimedia data in the network) and linkage (friendship, family members, classmates) which can be leveraged for analysis [3]. Likewise, the interconnectivity of users in online social networks allows user generated content to be easily propagated through the whole social network. The richness of these networks provides unprecedented opportunities for data analytics in the context of social networks [3]. Social network Analysis is the research area which has emerged as a key technique that focuses on measuring and mapping relationships and information flows within the networks that connect people or social units (users, groups, organizations, teams, computers and other entities) to a formal models, besides the study of those units and their attributes [5][1]. Using these models, we can measure different criteria demonstrating different roles and properties of different entities in their society also to facilitates understanding of how behavior of users is affected by the structures of a network as well as which network structure is more likely to emerge [5]. One of the issues to be resolved in this area is the identification of influential users in their networks. This is what we call social influence analysis. It is a sub-area of social network analysis which tries to resolve a number of questions that arise frequently in this context according to [3], namely: How do we model the nature of the influence across actors? How do we model the spread of influence?
3. THE MODELS Given a network G = (V, E) where V is the set of vertices, and E the set of existing edges in the network. A vertex v V is said to be active if the information has reached the vertex and was accepted by it. If the information didn’t reach the vertex or the vertex rejected it, then the vertex is said to be inactive. Each inactive vertex tends to become
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active, and it can switch from inactive to active, but it does not switch in the other direction. There must be an initial set of vertices activated to start the diffusion process targeted for initial activation. They are called ‘initial adopters’ of the information. The influence of this initial set of vertices is the expected number of active vertices in the end of the diffusion process. Consequently, the cascading process will appear as follows: given an initial set of active vertices; while time spreads out, more of an inactive vertex v’s neighbors become active which may cause this vertex to become active at some point. Then v may in turn trigger other vertices to which it is connected to adopt the same decision or action. We have chosen two basic diffusion models: the Linear Threshold Model (LTM) and the Independent Cascade Model (ICM).
treated as weights), and du, dv are the overall degrees of u and v, and then the weights buv and bvu are computed as follows: (1) In other words, it is the ratio of an edge to permit the spread of information from a node to its neighbors. Giving a simple example from real world; a new book comes on the libraries and several friends buy it. As much of your friends read this book, they will eventually convince you to read it as well. This is how LTM works.
3.2. Independent Cascade Model The second model is based on the work in interacting particle systems which describe the behavior of systems by probability theory [2]. ICM starts with an initial set of active vertices A0, and the process unfolds in discrete steps according to the following randomized rule. When vertex v first becomes active in step s, it is given a single chance to activate each of its inactive neighbors w with a probability Pvw to succeed. If v succeeds to activate w in step s+1, then w will try to activate its inactive neighbors too. Otherwise, v cannot make any further attempts to activate w in subsequent rounds. In case that w has many new active neighbors, they attempt to activate w in an arbitrary order. The probability Pvw is an independent parameter of the system [2]. At first, each edge in the network was assigned with a uniform probability. We chose the success probability p to be 50% that gives equal chances to a vertex to be successfully activated or not. The diffusion process ends when there are no more inactive vertices that can be switched to become active at a step s. If we use the books example from the ICM point of view, then we can formulate it as follows: you were just convinced by the new book and you bought it. Then, you will talk about it to your friends and try to convince them to buy it too, but you will have only one chance to convince them and you can never try again.
3.1. Linear Threshold Model One basic approach to model information dissemination in networks is based on the use of node-specific thresholds. Granovetter was among the first to propose threshold models in sociology [21]. We first implement a generalization of the LTM proposed by Kempe et.al [8]. In this model, a vertex v is influenced by each neighbor w according to a weight bvw where bvw ≤ 1. Each vertex v chooses a threshold Tv uniformly and randomly from the interval [0, 1]. This threshold is defined as the weighted function of v’s neighbors that must become active before v becomes active. The random choice of the thresholds T, at each time the process of diffusion runs; it fills the lack of information about the network and the relations between its actors. This is exactly what we want to show in this work, to demonstrate that the random choice of weights assigned to each tie showing the importance and the strength of a link between two users in a network is not a good choice, because the strength of a relation is a function of several parameters like trust which is considered as one of the factors in convincing an individual in a social network to behave in a similar way and the belief in the correctness of an information that an individual gets from its Neighborhoods and similarity which indicate how closely attitudes, values, interests and personality match between people which can lead to interpersonal attraction [5]. As explained before, the diffusion process unfolds in discrete steps: in step s, all vertices that were active in step (s-1) remain active, and new vertex v is activated if the total weight of its active neighbors is at least Tv, in other words:
4. EXPERIMENTS An egocentric (or personal) network is a type of network that concentrates on a specific user as a center of interest rather than a whole network. This user is called “ego” and the rest are called “alters”. The reason why we choose to use this type of network, especially of celebrities like Shaykh Dr. Aaidh al-Qarni, is simply to select the ego and a number of high score of his friends (according to the chosen metric) as initial adopters and most influential ones in order to measure their influence which is the expected number of active nodes by the end of the diffusion process. Finally, we shall say that we used the ego and a number of his friends, who have a celebrity, as the initial set of adopters in order to verify if these celebrities will really
The information stops propagating when there are no more inactive vertices that can become active. Given two vertices u, v; if cuv is the number of parallel edges between them (the multiplicity of the edge euv that is
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INFØDays'2013, 15-16 May 2013, Hassiba Benbouali University , Chlef , Algeria Table 2: Metrics’ Statistics.
influence the propagation of the information and spread it largely. We can extract this kind of network from different social platforms, but we have selected Twitter as one of the most popular, talked-about, and versatile social media platforms. Twitter can be thought of as a microblogging service (i.e. a short messages exchange service). This service allow users to send freely short messages called tweets of 140 characters or less to other people. Twitter calls the people who subscribe to and receive your messages your followers. So once they have added you to their contact list, they can follow your activities through all the messages you post on Twitter. While the people you are following are called your friends [1]. We highlight three “interpersonal” activities from the basic Twitter vocabulary according to [9]. First, users interact by following updates of people who post interesting tweets. Second, users can pass along someone's interesting pieces of information to their followers by Just hit the retweet button. Retweeting, the key mechanism for information diffusion in Twitter causes propagation of a posting that may provide the recipient by new insights of what’s being said about something like new product or event. This mechanism is important as it shows how an advertisement (aiming to promote new products or services directly to a target audience) can propagate across the network using influential users. Finally, the mentioning which means the number of times that the name of a user is mentioned in his followers' postings. We have extracted the egocentric network using the free and open tool NodeXL[1]. NodeXL allows visualizing and analyzing networks graphs, and computing the network metrics as well [13].
Indeed collecting data from Twitter is generally a slow process. This is due to several factors. First, data transfer rates over the Internet can be slow. Second, Twitter has its own bandwidth limitations; after all millions of people are sending data to and from Twitter all the time. However, NodeXl provides several options for speeding up data collections, such as artificially limiting the number of users in the data set. Thus, we have selected only 230 users from the total of 2.000.534 users to avoid exceeding the Twitter rate limits and not to take a very long time that may take hours or even days. It should be mentioned that NodeXl is less optimized for data collection, mainly when we are dealing with egocentric networks which are known as time-consuming. We have chosen options that allow us to collect data about all the ego’s alters whether friends or followers. Once the process is complete, we can display the network that will show the ego in the center, surrounded by a multitude of alters exactly as a star as shown in Fig. 1. We can infer many insights from this network like: as the ego (labeled by @Dr_alqarnee) has many alters who do not follow each other, these are likely to be his weak social ties, and most of them are complete strangers. On the other hand, we can focus on stronger social ties by looking at alters with at least two ties to other people (both ties may be to the same person) in both directions, friend and follower. Everyone we have collected data about will have at least one tie to @Dr_alqarnee. We can infer about alters with at least two ties that they will have either a tie to another alter of @Dr_alqarnee (indicating they are part of a close relationship), or they will have a reciprocal friend/follow tie to @Dr_alqarnee, and both of these factors may indicate the presence of a stronger social relationship between @Dr_alqarnee and the individual in question.
Table 1: Structure characteristics of the used networks .
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Figure 1: The @Dr_alqarnee small-scale egocentric Twitter network.
Figure 2: Calculated Metrics Distributions.
5. RESULTS
In the graph representation of the network, alters with two ties to other people will be represented by vertices with at least two edges (in both directions) between them and at least one other vertex. So In order to focus on more features, we must first calculate some important metrics. Table 1 and Table 2 summarize the structure characteristics and the computed centrality metrics of the studied network. After the calculation of the selected (needed) graph metrics we found that 99,2 % of the edges are in the giant connected component in the egocentric network confirming the high connectedness of such networks. Also the maximum geodesic distance (diameter) is only 2 due to the restriction that we have applied on both the level (a step in the network from one node to another) and the number of users (nodes) that will be imported, in order to extract a data set of manageable size. The In-Degree distribution (see Figure.2.1), shows that most nodes have a null value, unlike the Out-Degree distribution where the majority of nodes have at list an outgoing link to the ego (see Figure.1), that exhibit really one feature of this type of networks.
Before starting the discussion of the results obtained from the implementation of the two models on the dataset that represent the egocentric network, we note that four sizes of the initial set of adopters (targeted for the initial activation) have been chosen, which are respectively 5,10,15 and 20 elements. Also we should mention that for each model, the process was run 50 times for each initial set. Then the average of the size of active nodes after each run was computed. This average is considered to be the influence of the initial set. For more insights and to provide more understandings about the results, we make the size of each vertex as a function of his Eigenvector Centrality as this metric measure the importance of a node according to its connections to high-scoring nodes and the overall degree which represents the vertex opacity (the higher the overall degree is, the darker the vertex is). Notice that the ego is the largest vertex in the center of the network. Likewise, initial active nodes are colored by the red and infectious nodes are green. Figure. 3 illustrates an instance of the diffusion process applied on the Twitter egocentric network using the independent cascade model by choosing 20 initial active nodes (in red) based on the high betweenness centrality. We can see that there are other nodes especially the one located on the right side of the centre which represents Shaykh Dr. Aaidh al-Qarni, with a large size, this node corresponds to another scholar which is a member of the International Union for Muslim Scholars the Shaykh Salman al-Oadah. This validates the fact that a node that is linked to by nodes with high prestige receives a high rank itself.
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Figure 3: An instance after a diffusion run using the ICM on the Twitter egocentric network
We see clearly that ICM outperform LTM when the initial active set is chosen based on overall degree, eigenvector, PageRank or betweenness centrality (except in the case where the initial set contains 20 elements and the two models converge to the same value). This is due to the fact that most nodes have high overall degree with an average of 5, which causes the activation function in LTM to be of low values especially when most neighbors of a node are not active. Whilst, ICM performed better since it is based on the interactions between nodes but not on the overall degree values. Another observation is that the trend of the curves show that the number of activated nodes increases as we add other nodes in the initial set of adopters. There is a natural explanation to this observation. An initial set containing 5 elements influenced 12 nodes (the initial nodes included) and another initial set containing 20 elements influenced 27 nodes (the initial nodes included). Therefore in this case the two initial sets influenced the same number of nodes ((12-5) = (27-20) = 7) without counting the elements of each initial set with the influenced nodes.
Figure 4: Results representing the performance of the algorithms LTM and ICM
6. Conclusion One reason for the surprising low influence rates in the egocentric network which is considered as a scale-free network (because of the low degree of nodes), can be explained by the choice of both the thresholds selected uniformly and randomly from the interval [0, 1] assigned to each node at the beginning in the LTM as a weighted function of the node’s neighbors that must become active, in other words, the number proportion of others who must make one decision before a given actor does so at each time the process of diffusion is run, as well as the activation function assigning a uniform probability to each edge in the network in the ICM aiming to designate the strength of a relationship between two nodes; all of that has been done for filling the lack of information about the network and the relations between its actors. This reinforces what we have stated at the beginning; that the semantic and behavioral factors are the major points in the propagation of the information because the structure factors are not sufficient and do not give the proper results.
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Acknowledgments. We would like to thank the higher ministry of research and education for their financial support for this project. Project number: B*03320120004.
[13] Samir Akrouf, Meriem Laifa, Belayadi Yahia, Mouhoub Nasser Eddine, “Social Network Analysis and Information propagation: A case study using Flickr and YouTube networks”, 2012 International Conference on Information and Multimedia Technology (ICIMT 2012), Kuala Lumpur, Malaysia. December 21-22, 2012. [14] E. Katz, and P. Lazarsfeld, “Personal Influence: The Part Played by People in the Flow of Mass Communications”. New York: The Free Press, 1955. [15] E. M. Rogers, “Diffusion of Innovations”. Free Press, 1962. [16] F. M. Bass, “A new product growth for model consumer durables”. Management Science, 15(5):215–227, 1969. [17] J. J. Brown and P. H. Reingen, “Social ties and wordof-mouth referral behavior”. The Journal of Consumer Research, 14(3):350–362, 1987. [18] G. J., L. B., and M. E. Talk of the network, “A complex systems look at the underlying process of word-of-mouth”. Marketing Letters, 12:211–223(13), 2001. [19] V. Mahajan, E. Muller, and F. M. Bass, “New product diffusion models in marketing”, a review and directions for research. The Journal of Marketing, 54(1):1–26, 1990. [20] M. Richardson and P. Domingos, “Mining knowledge-sharing sites for viral marketing”, in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD’02), pages 61–70, 2002. [21] Mark Granovetter, “Threshold Models of Collective Behavior”. The American Journal of Sociology, Vol. 83, No. 6, pp. 1420-1443, 1978. [22] J. Nieminem, “On Centrality in a graph”. Scandinavian Journal of Psychology 15:322-336, 1974. [23] http://en.wikipedia.org/wiki/Aaidh_ibn_Abdullah_alQarni [24] http://pewinternet.org/Commentary/2012/March/Pew -Internet-Social-Networking-full-detail.aspx [25] http://thesocialskinny.com/100-more-social-mediastatistics-for-2012/
7. References [1] Derek L. Hansen, Ben Shneiderman, Marc A. Smith, “Analyzing social media networks with NODEXL, Insights from a connected world” Elsevier Inc, 2011. [2] Thomas M. Liggett, “Interacting Particle SystemsAn introduction”. School and conference on probability theory, Trieste 13-31 May, 2002. [3] C. C. Aggarwal, “Antroduction to social network data analytics” IBM T. J. Watson Research Center Hawthorne, NY 10532, 2011. [4] F. Walter, S. Battiston, and F. Schweitzer, “A model of a trust-based recommendation system on a social network." Autonomous Agents and Multi-Agent Systems 16(1), 2008. [5] Behnam Hajan and Tony White, “On Measuring Influence and its Properties in Social Networks,” School of Computer Science Carleton University, July, 2011. [6] R. Iyengar, S. Han, and S. Gupta, “Do friends influence purchases in a social network?” Harvard Business School Marketing Unit Working Paper No. 09-123, 2009. [7] W. Chen, Y. Yuan, and L. Zhang, “Scalable influence maximization in social networks under the linear threshold model,” in 2010 IEEE International Conference on Data Mining. IEEE, pp. 88–97, 2010. [8] David Kempe, Jon Kleinberg and Eva Tardos, “Maximizing the spread of influence through a social network,” in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, 2003. [9] M. Cha, H. Haddadi, F. Benevenuto, and K. Gummadi, “Measuring user influence in twitter: The million follower fallacy,” in Proceedings of the 4th International Conference on Weblogs and Social Media, 2010. [10] Behnam Hajian and Tony White, “Modelling Influence in a Social Network: Metrics and Evaluation” in 2011 IEEE International Conference on Privacy Security Risk and Trust and Social Computing. IEEE, pp. 497-500, 2011. [11] H. Kelman, “Compliance, identification, and internalization: Three processes of attitude change,” The Journal of Conflict Resolution, vol. 2, no. 1, pp. 51–60, 1958. [12] M. Deutsch and H. Gerard, “A study of normative and informational social influences upon individual judgment.” The journal of abnormal and social psychology, vol. 51, no. 3, p. 629, 1955.
8. Author name(s) and affiliation(s) Loucif Hamza was born in Bordj Bou Arreridj , Algeria in 1988. He received his bachelor degree from Bordj Bou Arreridj University in 2009. He received in the same university his Master’s degree in July 2011. Now he is a Phd student. Boubetra Abdelhak was born in Bordj Bou Arreridj, Algeria in 1959. He received his Engineer degree from Constantine University, Algeria in 1985. He received his Master’s degree from Great Britain 1990. He received his
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Phd degree from University of Setif Algeria. Currently; he is an associate professor at the Computer department of Bordj Bou Arreridj University, Algeria. His main research interests are focused on Simulation, Computer Business Intelligence, e-learning. … Samir Akrouf was born in Bordj Bou Arreridj, Algeria in 1960. He received his Engineer degree from Constantine University, Algeria in 1984. He received his Master’s degree from University of Minnesota, USA in 1988. He received his Phd degree from University of Setif Algeria. Currently; he is an associate professor at the Computer department of Bordj Bou Arreridj University, Algeria. He is an IACSIT member and is a member of LMSE laboratory (a research laboratory in Bordj Bou Arreridj University). He is also the dean of Mathematics and Computer Science Faculty of Bordj Bou Arreridj University. His main research interests are focused on Biometric Identification, Computer Vision and Computer Networks and social network analysis.
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